JOE Manuscript v07

7/29/2019 JOE Manuscript v07

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A Combined Broadband Single-Beam and

Narrowband Split-Beam SONAR System

Jae-Byung Jung, Member, IEEE, Alexander B. Kulinchenko, Member, IEEE,

Patrick K. Simpson, Senior Member, IEEE and James W. Tilley, Member, IEEE

Abstract

In this paper, we present a recent development in which the capabilities of a broadband single-beam and

narrowband split-beam sonar system are integrated into a single system. This development not only provides an

efficient tool to accommodate the benefits of both split-beam processing for target tracking (e.g. fish counting)

and broadband processing for target identification (e.g. species identification), but also takes advantages of the

synergism of both. Specifically, split-beam processing provides accurate angular information for targets within

the ensonified coverage. The targets angular information is used in broadband processing to compensate for the

irregularity of broadband spectrum caused by inherent uneven sensitivity of transducer across the effective beam

angle. Simultaneously, exceptional range resolution derived by broadband pulse compression provides improvement

over narrowband target resolution. Signal processing also allows a broadband sonar to emulate multiple narrowbandsonar systems by applying an instantaneous time-frequency representation. A series of tests under both controlled

conditions at a lakeside acoustic test facility and in operational environments on rivers have been conducted.

Index Terms

Broadband, narrowband, single-beam, split-beam, fisheries, sonar, neural network, classification, time-frequency

representation.

I. INTRODUCTION

A

S the number of fish in rivers and streams diminishes and become threatened, endangered, or extinct,

we see a growing concern from the local communities that is being met with increased funding and

political attention. There is a need for better fish monitoring tools for the riverine environment.

The primary tools for monitoring fish passage in an area where there are man-made impediments to

travel are tags and sonars. There are many tagging solutions [48], ranging from sonar tags for actively

following a fish [29], to clipping of a fin for later identification [2]. Sonar systems are used predominantly

for assessing the quantity and behavior at key points along a fishs path [25][26].

Split-beam sonar has a transducer that is typically divided into four quadrants. The target detection is

determined by comparing the echoes received from all quadrants. Using the phase difference between the

signals received by appropriate sections allows the target to be located within the beam [21].

In the shallow water riverine environments, the sonars ping rate is set very high to provide multiple

reflections from a single target and facilitate tracking. Each target detection is passed to a fish tracking

routine that aggregates successive pings in the same or adjoining range cells into a track of the fishs path

through the sonar beam [3][4][5]. Tracking fish is also the primary method of fish counting in riverine

environments such as those found in Alaska and Canada. This information is the primary tool used for

determining the amount of the fish runs [8][46][70][71].

Although the narrowband split-beam systems can monitor the movement of fish tracks and estimate the

size of targets by calculating target strength, target identification and spatial range resolution can be very

limited with these systems.

Jae-Byung Jung, Alexander B. Kulinchenko, and Patrick K. Simpson are with Scientific Fishery Systems, Inc., Anchorage AK 99516

(email: [email protected]; [email protected]; [email protected]).

James W. Tilley is with Alaska Native Technologies, LLC., Anchorage AK 99516 (email: [email protected]


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The primary advantages of broadband sonar over the existing narrowband systems includes: (1) the

broad spectral information for species identification, (2) the improved target detection, (3) more stable

estimate of signal, and (4) improved spatial resolution.

As a well-established theorem in information theory that holds that the information-carrying capacity of

a communications channel depends on its bandwidth, the broadband acoustic data processing is expected

to be a productive approach for species identification with the additional assistance of the appropriate

analytical tools such as neural networks, expert systems, and new methods of discriminant analysis

[42][43].

Broadband systems also exploit time-bandwidth product of pulse compression to produce excellent

spatial resolution as well as superior signal to noise ratio for improved target detection and stable signal

estimate over conventional narrowband systems.

Through the application of instantaneous time-frequency representation (ITFR) algorithms, it is possible

to emuluate multi-channel narrowband spectral responses at every discrete sample from a broadband

signal. Various kernels have been investigated to acquire better temporal and spectral resolution with less

interference [9][10][28][44][72].

Neural networks have been used to achieve over 80 % correct discrimination among frequency spectra

for echoes from different species of fish [52] and zooplankton [68]. The results of several independent

field tests have shown that broadband sonar provides a promising approach for discriminating various fish

species using either (or both) spatial and spectral features [22] [31][32][33][34][35][51][56][60].

In addition to the individual benefits of narrowband split-beam and broadband single-beam systems,

the combination of these two capabilities in a single system supplies mutually advantageous cooperative

functions. The accurate target location realtive to the Maximum Response Axis (MRA) provided by the

split-beam portion of the system provides the information needed to compensate for the uneven broadband

beam patterns. Conversely, the improved range resolution from broadband pulse compression helps split-

beam tracks be distinguished clearly. Technical details of thsee synergistic combinations are discussed in

the sections V and VI.

From a practical point of view, especially for riverine fish migration monitoring, sonar systems are

often deployed in very remote and secluded locations, often with great distances between the wet-end of

the sonar where the transducer is housed, and the dry-end of the system where data is stored, power is

provided, and communications with the system are established. Under such conditions, the wet-end (sonar

head) and the dry-end (monitoring station) can be isolated by natural barriers and be required to cover

a long distance. Therefore, analog sensor signal conditioning, such as band-pass filter and amplification,

and analog-to-digital conversion as an initial step of data acquisition should be processed as early as

possible in the wet-end sensor unit to prevent signal contamination from various noise sources during

the transmission. Furthermore, the up front digitization and full digital signal processing in the wet-end

unit allows digital data transfer in wired or wireless TCP/IP, providing additional flexibility in remote

environments.

The following sections discuss the overall architectural design of the system, details of system devel-

opment, newly proposed data processing methods utilizing both narrowband and broadband processings,

and various field tests for system evaluation.

I I . FUNCTIONAL REQUIREMENTS

A novel sonar system combining broadband single-beam and narrowband split-beam is designed to

identify, count, and track migratory fish stocks for use in shallow water environments.

The key requirements that were incorporated when combining the capabilities of each sonar system

include the following:

Switch between broadband and narrowband,

Switch between single-beam and split-beam,

Interleaving broadband single-beam and narrowband split-beam,


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Programmable transmit power,

Programmable receive gain,

Programmable waveform,

Wet-end signal conditioning and data acquisition,

Network connectivity through TCP/IP, and

Compatibility with existing softwares.

When the sonar is operated as a single beam system, it can transmit broadband pulses or multi-frequencynarrowband pulses. This mode of operation is primarily used for determining species or size of the targets,

such as fish, bubbles, and debris. When the sonar is operated as a split-beam system, the transmit frequency

is fixed at the optimal frequency for transducer performance. This mode of operation provides split-beam

processing for tracking targets as they move through the beam.

Fig. 1. Interleaving ping patterns: 6 examples of flexible ping scheduling schemes are shown; narrowband pings only, 3 narrowband pings

and 1 broadband ping, 2 narrowband pings and 2 broadband pings, 1 narrowband ping and 3 broadband pings, 1 narrowband ping and 1

broadband ping, and broadband pings only, respectively.

Furthermore, the system can change sonar parameters on a ping-by-ping basis allowing several shorter

narrowband pulses in the split-beam mode to be interleaved with a smaller number of the longer broadband

(or multi-frequency) pulses. Each sequence of interleaving pulses can be defined so that it provides

adequate narrowband coverage for target tracking and provides additional broadband target identification.Fig. 1 includes six examples of ping scheduling schemes, including (1) narrowband pings only, (2) three

narrowband pings and one broadband ping, (3) two narrowband pings followed by two broadband pings,

(4) one narrowband ping followed by three broadband pings, (5) one narrowband ping followed by one

broadband ping, and (6) broadband pings only, respectively.

III. HARDWARE ARCHITECTURE

The combined split-beam/broadband sonar system consists of three primary units: wet-end system; dry-

end system; and underwater cable. The wet-end system is deployed in the water and is the main sonar

unit with the computational resources and sensor module enclosed in an underwater housing. The dry-end

system remains shore side. Its primary functions include power supply to wet-end system, communication

with wet-end system, and wired or wireless TCP/IP with external computers or data storage. An underwatercable connects both wet-end and dry-end units together over a distance of as much as 100 m. Although

all three units are needed and must function seamlessly together, we would like to focus our discussion

on wet-end system in this section.

The wet-end consists of three primary system segments: client PC, controller, and transducer as is

illustrated in Fig. 2.

A. Client PC Segment

This is the main processing unit performing data acquisition, signal processing, and communication

with the dry-end.


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A low power single board computer (SBC) with capability of floating point computation executes

signal processing tasks in coordination with an optional digital signal processing (DSP) board. One of the

available serial ports (RS-232) in SBC transmits control commands to the Controller Segmentto all changes

to the transmit power and receive gain. An integrated ethernet (TCP/IP) also allows communication with

dry-end system.

Data acquisition board passes on the waveform to Controller Segment so that it can be amplified

according to the transmit power setup by SBC. At the same time, analog-digital conversions are executed

in 4 separate channels simultaneously according to the receive gain setup by SBC.

Once all the data processing tasks were completed, the data sets are either saved in the internal data

storage or transferred to the dry-end system through TCP/IP in real-time.

Fig. 2. The wet-end hardware architecture: component modules consist of client PC segment, control segment, and transducer segment.

DC power lines and category-5 lines are fed from dry-end via underwater cable.

B. Controller Segment

This segment consists of custom electronics boards that have been developed exclusively for digital

control, power distribution, transmit waveform amplification, and receive signal amplification.

A field programmable gate array (FPGA) communicates with the SBC to control 16 programmable

transmit power levels and to adjust 16 programmable receive gain levels to remain flexible for eachdeployment environment. Based upon the software configuration, the embedded analog power management

module distributes the appropriate DC power levels to each of the functional modules across the entire

wet-end system.

Analog transmit (TX) module sends an amplified analog waveform to the transducer for signal projection

in the water column. The analog receive (RX) module receives the raw signals in four separate hardware

channels from each of the transducers quadrants and then executes the initial signal conditioning in

hardware including preamplification, band pass filtering, and the application of signal gain.


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C. Transducer Segment

The transducer has four equally split quadrants that provide the ability to perform split-beam processing.

Although each of the channels are recorded independently at reception, all quadrants simultaneously

transmit the same waveform at the same power level for a composite conical beam projection.

IV. SIGNAL PROCESSING

The data processing paths vary depending on the mode of operation for that ping: broadband single-beam or narrowband split-beam. Each of these data processing paths has its own processing flow as well

as assistance from its counter part. Fig. 3 shows the block diagram of the overall data processing chain

for the received signal.

Fig. 3. Overall data processing chain: First 4 blocks in each quadrant represent hardware implemented functions for initial common

processing and the remainder of the blocks are software processed functions.

The processing can be partitioned into five functional divisions: (1) the common hardware processing

upon the signal reception, (2) channel mixer, (3) narrowband split-beam processing, (4) broadband single-

beam processing, and (5) classifiers.

A. Common Hardware Processing

Each of four transducer quadrants are independently processed for signal conditioning, signal gain, and

analog-digital conversion. Each of the four hardware channels have identical configurations for the initial

common signal processing.

1) Signal Conditioning: A fixed level of preamplification is applied to the incoming raw analog signal

from each quadrant. A band pass filter passes only frequencies within the broadband range andattenuates frequencies outside that range.

2) Signal Gain: A programmable gain amplifier further amplifies the analog signal with 16 discrete

levels is controlled by an FPGA that receives commands from the SBC through RS-232.

3) Analog-Digital Conversion: 4-channel simultaneous data acquisition is attained at high speed (500 kHz)

and high sampling resolution (16 bits/sample).

B. Channel Mixer

Four individual discrete time series are fed into the channel mixer to produce one composite signal and

four split-beam signals.


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For the broadband processing, the composite signal is formed by the sum of the four discrete time series

from each of the four quadrants. This composite signal is used mostly for broadband signal processing

chain, although it can be also used for target strength calculations to help more accurate split-beam

detection module.

For narrowband processing, the sum of quadrants 1 and 2 are combined to represent upper half of the

transducer, the sum of quadrants 2 and 3 are combined to represent left half of the transducer, the sum

of quadrants 3 and 4 are combined to represent lower half of the transducer, and the sum of quadrants 4

and 1 are combined to represent right half of the transducer, respectively.

C. Narrowband Split-Beam Processing

Narrowband split-beam data processing begins with phase comparison between upper and lower halves

for vertical offset and between left and right halves for horizontal offset. Once the phase differentials

are attained, a decimated version of the split-beam data and broadband target strength data are used for

split-beam detection to provide target tracks for tracking and track classification.

1) Phase Computation: Four discrete time series representing each half of the transducer configuration

go through carrier removal, and then relative phase differences of the two opposite halves are

calculated to provide horizontal phase offset and the vertical phase offset. Also, this data will be

decimated from the original sample rate to a lower, more manageable, data rate before it is exported.2) Phase-Angle Conversion: Two phase differentials are converted into Cartesian angles using the

physical configuration of the transducer.

3) Split-Beam Detection: Broadband target strength calculations and narrowband phase/angle calcula-

tions are used to build a robust target detector. A threshold value of the target strength and variance

of the phase/angle differentials are the primary parameters used target detection. Also, a list of

locations of individual targets are provided for tracking.

4) Tracking: An - tracking system is used to track detected targets [3]. Track tuning to determine theappropriate number of missed detections, association windows, and track initiation and termination

criteria will be conducted during data analysis. Also, more complicated tracking algorithms such as

joint probability data association (JPDA) and multi-hypothesis tracker (MHT) can be applied when

the complexity of the tracking increases [4].

D. Broadband Single-Beam Processing

Broadband single-beam processing starts with the composite signal from all four quadrants. Transmis-

sion loss over the data collection range is compensated by time-varying gain, pulse compression provides

excellent range resolution, and the spectral estimation offers broad spectral features for classification.

1) Target Strength: The composite broadband signal is used to calculate target strength. Unlike the

narrowband target strength with frequency dependency, broadband target strength can provide more

reliable estimation by aggregating broadband response. Accurate target strength helps split-beam

detection module in split-beam processing chain.

2) Time-Varying Gain: The transmission loss is computed from a combination of spherical spreadingloss (20log R) and absorption (R), where R is the range [m] and is the attenuation coefficient[dB/m]. The application of the transmission loss results in a range-compensated discrete time seriesthat is propagated through the remainder of the system.

3) Pulse Compression: A matched filter is used to produce the compressed pulse, which provides im-

proved spatial resolution for signals when a sufficiently large bandwidth is available. Mathematically,

the matched filter is computed by taking the inverse Fourier transform of the product of the Fourier

transform of the transmit signal with the complex conjugate of Fourier transform of the received

signal as described below.

xMF = F1{F(xTX) F

(xRX)} (1)


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where xMF is a compressed pulse time series, xTX is a transmit time series, xRX is a received timeseries, F() is Fourier transform.The effective pulse width of the compressed pulse, TMF, becomes reduced by a factor of the time(T)-bandwidth(B) product. For example, a pulse duration of 667 s is compressed to 16.7 s for the asonar with 60 kHz of bandwidth.

TMF = T 1

T B=

1

B(2)

A few detection criteria including peak threshold, peak window size, and peak separation are applied

to the pulse-compressed data series, producing a list of locations (in range) of the start of each

detected target. A peak detector is used on the detection signal to locate echoes in the return

time series. The peak detector is the technique which first make an estimate about the background

signal level in that neighborhood (e.g., by taking an average around the point) and then comparing

the sample in the center of the neighborhood with the background signal estimate. When a peak

is located, it signifies that a portion of the return has matched up with the filter and therefore

represents a likely reflector in the water. If there is significant separation between last detected echo

and current detection, then a detection is declared and echo extraction occurs. These three sonar

configuration echo detection settings, as well as target strength, are used as coefficients for matched

filter detection process.

4) Spectral Estimation: Spectral estimation is performed using the Fast Fourier Transform (FFT). For

an FFT of size N, the number of frequency bins is N/2. Frequency bin width is the bandwidthof each of the contiguous bins across the usable bandwidth. Frequency bin width is determined by

the size of the FFT and the sample rate. For the classification purposes, the derived echo spectrum

must undergo normalization to accommodate differences in frequency response between different

transducers. This normalization process allows the classifier to be transducer independent

5) Beam Compensation: Uneven broadband beam patterns and sensitivity can be adjusted by compen-

sating for differences in the beam at various frequencies depending on the targets location relative

to the maximum response axis. Details of this beam compensation process are discussed in section

V.

E. Classifiers

Three individual classifiers (track, size, spectral) make independent decisions based on features derived

from track, size, and spectrum. Neural networks are trained using known signals to build each classifier,

and then tested with unseen patterns to evaluate the performance of each classifier. Afterward, the final

decision is made from the fusion classifier for target identification. Alternatively, in place of the fusion

classifier, decision level inference using fuzzy logic or other artificial/computational intelligence algorithms

can be implemented.

1) Track Classifier: The kinematic features such as location, velocity, and movement are extracted

from the - tracker and used to identify the class of underwater target. Also, statistical acousticfeatures such as target strength and spectral/temporal information from individual detections along

each track are added in the pool of features for target identification.2) Size Classifier: The samples associated with a detected target will be used to estimate the size of a

target. Targets that have elongated detection envelopes represent larger targets. As an example, large

aggregations of closely spaced detections are indicative of a marine mammal and sparse detections

of only a few detections within a range window are indicative of fish or other small targets.

3) Spectral Classifier: The spectra produced from the spectral estimation process are used as features for

classification. The classifier is iteratively improved using data from a variety of target assemblages.

The classifier that is utilized is similar to the Parzen estimator that produces probability densityfunctions for each class. Classes will depend on the quality of the ground-truth data. The more

diverse and varied the data set, the better the performance.


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4) Fusion Classifier/Identification: The classification decisions produced by the track classifier, size

classifier, and spectral classifier are merged to form the fusion classifier for the final target identi-

fication.

V. BEA M COMPENSATION

One of the synergistic effects of interleaving narrowband and broadband pings is that is allows for

the compensation of the broadband spectrum using the information from the split-beam processing ofprevious ping. In this section, we describe this compensation process and explain how the narrowband

split-beam can help broadband spectrum adjustment to fully utilize the dynamic range of the directivity

and sensitivity of the transducer.

Unlike a narrowband-only system with fixed directivity pattern and sensitivity in a fixed frequency, a

broadband transducers directivity pattern and sensitivity are a function of frequency. Fig. 4 shows the

directivity patterns of the broadband transducer that was installed in this system. These directivity patterns

were calibrated for eight separate narrowband waveforms in a controlled environment. Note that the higher

the frequency, the narrower the effective beam angle. Also, when a Liner Frequency Modulated (LFM)

waveform is transmitted, the target response with higher frequency component tends to return lower energy

if the target is slightly off MRA.

Fig. 4. Transducer directivity patterns for 8 different narrowband frequencies from 150 kHz to 200 kHz at 10 kHz intervals.

Split-beam processing provides an accurate bearing angle of a target off MRA. Lets assume the z-axis

is the MRA, the x-axis is the horizontal reference, and the y-axis is the vertical reference. Note that theaxes x and y are not in polar domain but in Cartesian coordinate. When a target is detected along thetarget vector, T, and the split-beam processing provides the horizontal and vertical angular offsets, and, relative to the transducer as illustrated in Fig. 5, we can calculate the bearing angle of T relative toMRA as described below in (3).

= arctan1

tan2 + tan2 (3)

Although the measured directivity patterns are not perfectly symmetrical, especially after first side lobes

outside 8 13 depending on frequencies, we can focus on the main lobes of the directivity patterns


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Fig. 5. Split beam target detection and relationship with transducer geometry. See text for a description of the variables.

on each frequency to construct the directivity pattern compensation factor as a function of frequency and

bearing angle off MRA as illustrated in Fig. 6.

As split-beam processing provides accurate bearing angle of the target off MRA, and the broadband

pulse compression and instantaneous time-frequency representation (ITFR), which is discussed in the

following section, provides precise frequency component of target return, we can estimate how much

deviation of sensitivity we lose by interpolation from calibration points.

Fig. 6. Transducer directivity patterns (3D regression). Using this data and knowing the position of the target relative to MRA, it is then

possible to provide a uniform target response across the entire bandwidth.


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Another nonlinear impact on the broadband spectrum is the transducer sensitivities from transmit voltage

response (TVR) and open circuit voltage response (OCVR). The Figure of Merit (FOM) is the sum of TVR

and OCVR, and is useful in providing a relative gauge of the performance of piezoceramic transducers.

Therefore, the normalized total sensitivity can be calculated by combining TVR and OCVR, and then

take out its maximum value as described in (4), which represents the relative deviation of sensitivity

when the frequency component deviates from the resonance frequency of the transducer. Fig.7 shows the

normalized total transducer sensitivity that was also measured in a controlled environment as a function

of frequency.

SN = F OM F OMmax. (4)

Fig. 7. Normalized transducer sensitivity of high frequency version of SciFish2100-D system. Overall sensitivity (FOM) is normalized at

the center (main resonance) frequency at 165 kHz.

Therefore, we can combine both directivity-related adjustment factor and the sensitivity-related factor

together and build the total beam compensation as a function of frequency and target angle off MRA as

shown in Fig.8. In other words, if we can compensate for the transducer-inherent sensitivities using beam

compensation, we will have more target-subjective frequency distribution instead of sensor-subjectiveversion.

V I. EXTENSION TO MULTI-F REQUENCY SONAR

As a broadband signal provides various perspectives within the specific spectral range, it is sometimes

very useful to convert it into a finite number of narrrowband time series in various frequencies. The

biggest advantage in this processing is to emulate multiple narrowband sonars using a single broadband

sonar, although the increased processing burden for real-time implementation must be considered. This

section explains the process of extending a broadband sonar to its equivalent multiple narrowband sonars.


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Fig. 8. Total beam compensation (3D regression) considering target bearing angle and frequency response. Transducer-inherent non-linearity

is cancelled out by taking out the total beam compensation and the generalized target response can be achieved.

A. Time-Frequency Representation

In general, Fourier Transform converts a temporal signal, x(t), to spectral information as described in(5). This is a very useful tool when analyzing a time series from the spectral perspective, but the temporal

information is lost after the conversion.

Fx(f) =

x(t)ej2ftdt (5)

One of the possible methods to retain the temporal information after the transformation is a Short Time

Fourier Transformation (STFT). As described in (6), the STFS uses a sliding time window, w(t), to retaintemporal information while applying the Fourier Transformation. One of the big drawbacks of the STFT

is that it has a fixed overall resolution, because the window function determines whether there is good

frequency resolution or good time resolution. A wider time window provides better frequency resolution

but poorer time resolution whereas a narrower window gives better time resolution but poorer frequency

resolution.

Sx(t, f) =

x()w(t )ej2fd (6)

When a precise frequency response needs to be achieved instantaneously or at very localized instant

in time, a Time-Frequency Representation (TFR) can be applied. The analytically appealing Cohens

Generalized Time Frequency Representation (GTFR) of a temporal signal, x(t), is shown in (7) whenseeking a performance improvement of the spectrogram and making instantaneous representation of both

temporal and spectral information available.

Cx(t, f) =

(, )x

t

2

x

t +

2

ej2dd (7)


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It is now straightforward to state some of the commonly used constraints imposed on the GTFR and

their corresponding interpretation as kernel constraints using the kernels listed in (8).

(, ) =

1, W igner V illee(2)

2/, Choi Williamsej, Kirkwood Rihaczekej2||, P agecos(), M argenau Hillsinc(), Born Jordong()||sinc(2a), Z hao Atlas Marks

(8)

where , a, and g() are kernel parameters selected to best suit the application.

B. Real-Time Implementation of Instantaneous Time Frequency Representation (ITFR) for Multi-Frequency

Sonar

Performance of the GTFR depends on the choice of kernel in different applications. Those kernels

listed above are well known to reduce interference (cross-term) during the conversion. However, complex

kernels reduce also the feasibility of implementing the algorithm performing in real-time, especially when

a high ping rate is required to monitor migratory fish schools in riverine environment. After reviewing each

of those algorithms, the Wigner-Ville Distribution (WVD) was best determined to be the most practical

choice for instantaneous time-frequency representation.

The WVD, shown in (9) can be seen as a special case of GTFR, Cx(t, f), when the kernel, (, ), is 1.Although there is the potential not to be able to remove the interference without a more effective kernel,

computational complexity of Wigner-Ville distribution is much lower than those of the listed kernels

with temporal or/and spectral constraints. When considering these trade-offs, the benefit for real-time

implementation with reasonable computational burden overshadows the aforementioned drawbacks.

W Vx(t, f) =

x

t

2

x

t +

2

ej2fd (9)

An example of field data collected in Ship Creek, Anchorage, AK, on August 12, 2004 illustrates the

ITFR process with WVD. Fig. 9 shows a snapshot of a ping recorded in a single composite broadband

channel at sampling frequency of 500 kHz. Because of the protection circuit in hardware electronics,

all signals were clipped at 1 V. Thus, the actual LFM transmit waveform from 135 kHz to 195 kHzwith amplitude of 100 Vpp appears at 0667 sec, followed by a relatively low transducer ringing until840 sec, but they appear clipped in the collected time series. Meanwhile, a clear target return of aChinook Salmon (Oncorhynchus tshawytscha) shows up between 3.72 msec and 4.40 msec.

When we focus on the target return of the time series in Fig. 9, a power spectrum of the target can

be calculated using the discrete version of (5) with FFT size of 512. In this calculation, all discrete

samples between 3.72 msec and 4.40 msec were included to produce the target power spectrum shown in

Fig. 10, which coincides with the frequency range of the transmit waveform. The shape of the distribution

within the broadband frequency range between 135 kHz and 195 kHz can be used as features for target

identification in spectral classifier.

One of the properties of a spectrogram produced from a STFT in (6) is the ability to retain the temporal

information while providing spectral features. Fig. 11 shows the spectrogram of a whole time series using

an FFT size of 128 and time window size of 125 samples (=250 msec). The spectral features show up

along the time axis except both edges of the time series caused by time window, but smearing features

dilute the resolution in both time and frequency axes.

To retain finer frequency resolution at every time interval with realistic computational expense, the

WVD ITFR is used as shown in Fig. 12. The spectral features show up along the time axis at every


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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

x 103

1.5

1

0.5

0

0.5

1

1.5Received Time Series (Composite Broadband Signal)

Time [second]

SignalLevel[V]

Fig. 9. An example of field data collected in Ship Creek, Anchorage, AK, on August 12, 2004. LFM from 135 kHz to 195 kHz for 667sec

pulse length was transmitted and is seen at the beginning of the time series. A clipped version of transmit waveform that appears between 0

and 667sec was recorded, although the actual transmit signal level was approximately 100 Vpp. Transducer ringing followed from 667 sec

and faded away at about 840sec. The target return from a King Salmon shows up clearly between 3.72 msec and 4.40 msec.

instance without black-out sections on both edges, and much sharper resolution was achieved in both time

and frequency axes.

A spectrum is available at every time interval and extracting an horizontal strip at a certain frequency

from Fig. 12 represents a narrowband-equivalent time series.

Besides, to attain spectral energy at a fixed frequency, an envelope of each time series needs to be

calculated by shifting the spectrum by the constant frequency offset, fo. The relationship between timedomain and frequency domain in frequency shift is denoted in the equation below.

e2f0tx(t) Fx(ej2(ff0)) (10)

Thus, multi-channel time series from a single broadband signal can be derived to produce multi-

frequency narrowband signals. Fig. 13 focuses on the time segment between 3.72 msec and 4.40 msec

to show 16 narrowband time series extracted from a single broadband signal. In this example, narrowband-

equivalent target returns are in frequencies 135.0 kHz, 138.2 kHz, 144.7 kHz, 147.9 kHz, 151.1 kHz, 154.4 kHz

160.8 kHz, 164.0 kHz, 167.3 kHz, 173.7 kHz, 176.9 kHz, 180.2 kHz, 183.4 kHz, 189.8 kHz, 193.1 kHz, and

196.3 kHz.

Although some artifacts with negative energy are present after computation (a well-known side-effect

of the WVD), which is not physically possible, some simple methods such as taking the absolute values

or discarding negative values can easily cure these processing anomalies.

VII. EXPERIMENTAL RESULTS

There have been several field tests of the combined broadband/split-beam sonar system, including

controlled experiments in pools and a lake and deployments along a river for the duration of a large


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0 50 100 150 200 25040

30

20

10

0

10

20

30Target Power Spectrum

Frequency [kHz]

Power[dB]

Fig. 10. Power spectrum from target detection. All samples in the time window from 3.72 msec to 4.40 msec were extracted and the power

spectrum was calculated to provide the distribution of energy in frequency domain. Most of the energy is concentrate within 135 kHz195kHz

according to the transmit waveform of LFM.

Fig. 11. Spectrogram with FFT size of 128 and time window size of 125 samples (=250 msec). The spectral features along the time axis

are available, but the resolutions in time and frequency depend on the FFT size and time window size. Also, spectral features on both ends

of time series are not available due to the use of time window.


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Fig. 12. Wigner-Ville distribution for instantaneous time frequency representation. Improvement of resolution is noticeable in both time

and frequency domains compared with STFT.

Fig. 13. Sixteen individual time series are extracted from a broadband signal using ITFR with Wigner-Ville distribution. Each of the signals

is equivalent to its narrowband time series at each of those 16 constant frequencies. Because the frequency of transmit time series (LFM) is

linearly increasing in time, the lower frequency components arrive earlier than those of higher frequency components in the returned target

echo.


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salmon run. All of the experiments were conducted in Alaska, with pool tests being performed during

the winter and lake and river testing during the summer. These experiments were conducted to verify that

the combined sonar operates correctly in their individual split-beam and broadband modalities. In a later

paper, we will present results of the beam compensated broadband performance in a series of controlled

classification studies [36].

Three tests are described in the sections that follow. The first of these tests focused on the ability of

the split-beam sonar system to track and enumerate fish during high passage rates. The remaining two

tests focused on the ability of the broadband sonar system to perform target identification. The first of

these broadband tests was conducted in a lake focused on the ability to discriminate a swimmer from

similar targets [35]. The second broadband test was conducted with Pacific Northwest National Lab for

discriminating smolt-size Chinook salmon from aquatic macrophytes [34].

A. Splitbeam Experiment

The primary purpose of the field test in Wood River, Alaska, was to evaluate narrowband split-beam

sonar processing. Wood River is known to have a large Sockeye Salmon (Oncorhynchus Nerka) return

between late June and early July. The Alaska Department of Fish and Games (ADF&G) monitors this

migration from several counting towers along the rivers shore. The tower crews visually count individual

fish passing underneath the tower for 10 minutes every hour during the period of fish run when the majorityof the fish are migrating upriver and then estimates the hourly fish passage rate from the subsample.A combined broadband/split-beam sonar system was deployed in the Wood River near Aleknagik,

Alaska, fully operating with minimum interruption for power and data backup from 6/26/06 to 7/4/06.

Fig. 14 shows the combined sonar system deployed next to an ADF&G counting tower.

Fig. 14. ADF&G counting tower and combined sonar system deployed to monitor Sockeye salmon migration in Wood river, AK.

Throughout the test period, the sonar system was continuously transmitting 200 sec narrowband pulsesat 15 pings per second. Periodically, a tower count was made when the visibility in the river was adequate.

The tower counts and the sonar counts were performed independently and the results were provided to a

third party (Dr. Tim Mulligan). The comparative results were not reported until after the summer season

was completed and Dr. Mulligan had sufficient time to perform his comparative evaluation.


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The comparative evaluation was conducted using all visual tower counts. Each of the corresponding

sections of recorded narrowband signals were extracted. Utilizing the EchoView application from Sonar-

Data (now Myriax, www.echoview.com), the split-beam data was processed offline to count fish. The

number of fish tracks in each ten-minute tower count session were logged and compared with that of

visual tower count. Table I enlists date, start time, end time, visual fish count from counting tower, and

fish track count from off-line sonar data analysis of each of the 20 sessions where tower counts were

taken.

TABLE I

SPLIT-BEAM SONAR FIS H COUNT COMPARISON BETWEEN VISUAL TOWER COUNT AND SONAR SYSTEM TRACK COUNT

Date Start Time End Time Tower Count Sonar Count

6/29/06 09:21 09:31 441 430

6/29/06 10:00 10:10 165 156

6/29/06 11:11 11:21 868 654

6/29/06 11:55 12:05 467 426

6/29/06 14:03 14:13 373 348

6/29/06 14:57 15:07 770 584

6/30/06 00:02 00:12 0 6

6/30/06 06:30 06:40 918 647

6/30/06 15:24 15:34 505 491

6/30/06 15:41 15:51 217 215

6/30/06 18:40 18:50 430 397

6/30/06 18:50 19:00 590 499

6/30/06 19:00 19:10 651 478

7/1/06 06:23 06:33 1284 902

7/1/06 23:57 00:07 1074 839

7/2/06 23:11 23:21 1023 744

7/2/06 06:56 07:06 1210 923

7/3/06 06:51 07:01 953 771

7/3/06 11:23 11:33 330 304

7/3/06 14:59 15:09 1710 939

For the analytical comparison of each fish counting technique, each fish count was converted into an

hourly passage rate and directly compared as shown in Fig. 15. The horizontal axis represent the visual

fish count from counting tower, the vertical axis represents the number of fish tracks extracted from

sonar data recorded at the same time, and the dotted diagonal line represents a virtual 1:1 mapping as a

reference, respectively.

Also, the 2nd order polynomial curve fitting, p(x), that is described in (11) can be derived from the 20data points and depicted in a solid curve in Fig. 15.

p(x) = 0 + 1x + 2x2 + 3x

3 (11)

where 0=-2.5267x109, 1=-4.0365x10

6, 2=0.8510, 3=160.8368, and x is a vector including 20 sonarcounts.

Of the nine sessions where the hourly fish passage rate was under 3000 fish per hour, the sonar counts

and tower counts closely agreed with a very small margin. Deviations are seen in the remaining sessions

as the fish passage rate increased. There are two possible reasons for the sonar under-counting when the

population increased drastically. One possible cause is the fish swimming out of sonar beam angle so that

the system could not sufficiently ensonify those fish. The other cause could be that many fish would have

traveled too close to each other to make multiple tracks appear to be a single track in the recorded data

although they swam inside the beam angle.

Considering the majority of the Salmon migrations were reported with less than 3000 hourly fish passage

rate, the split-beam performance evaluation in Wood Rover was considered successful.


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Fig. 15. Fish count comparison for hourly passage rate. The square data points represent the relationship between the visual fish count

from counting tower (horizontal axis) and the number of fish tracks extracted from sonar data (vertical axis) along the dotted diagonal line

for 1:1 mapping as a reference. The 3rd order polynomial fitting curve shows the trend of the performance (solid line).

B. Broadband Experiment - Swimmer Identification

A field test has been conducted to demonstrate the capability of the broadband sonar system to identify

swimmers from other underwater objects. Classifiers were built and tested from data collected from an

swimmer donned with mask, fins, and a scuba tank in Marion Lake, Alaska in 2005. The test results using

low frequency version of the combined sonar system (60 kHz120 kHz) are described in the followingsubsections.

Two known targets (air filled 2-liter plastic bottles) were placed in the middle of water column as

reference targets emulating the approximate size and shape of human lungs. One bottle was placed at

30 m and the other was placed at 71 m range. A swimmer swam in approximately a straight line along the

MRA from a location 22 m from the transducer toward the first bottle at 30 m. After passing the first bottle,the swimmer came to the surface to adjust his swimming direction, and then swam in approximately a

straight line toward the second bottle. The swimmer again surfaced to re-adjust the swimming direction

at 65 m, and swam past the 2nd bottle up to 95 m range where he turned around and attempted to retrace

the same route back. The swimmer surfaced several times to re-adjust his direction as he was coming

back.

Throughout the swimming sequence there is a significant number of air bubbles produced from the scuba

gear and the swimming motion, especially when the swimmer was at close range and at the surface. The

presence of these bubble clouds had the effect of diminishing the separation between the swimmer and

the air-filled bottles for the classifier, as the air bubbles were necessarily included with the swimmer and


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represented a significant portion of the backscatter from the swimmer.

Only a small portion of the collected data were extracted for spectral comparison using principal

component analysis (PCA). Approximately 200 echoes from the second bottle at 71 m were compared with

those of the swimmer at 6370 m range outward bound (flippers toward the sonar, generating bubbles).The extracted spectra within the frequency of interest (60 kHz120 kHz) were converted into principalcomponents.

Fig. 16 shows the two largest principal components that provide the greatest separation between two

target types (swimmer and bottle). The use of PCA is very useful as an intial analysis tool as it provides

a preliminary indication of separability among different targets in high dimensional feature space. The

PCA test was very promising, with only two principal components out of 63 distinct spectral energy bins

indicating the possibility of separation among different target classes.

Fig. 16. Principal component analysis: 2-dimensional presentation of 2 different targets. Two largest principal components that provide

the greatest separation between two target types (swimmer and bottle) were plotted as a preliminary observation of separability. There are

several points that appear to be overlapped, although two noticeable clusters can be distinguished.

Since some degree of separation was observed between the swimmer and the bottle data, we anticipate

that the artificial neural network would improve the discrimination between the two types, because (1)

the artificial neural network utilizes the full degree of freedom of feature sets without data loss, and (2)

the artificial neural network provides flexible non-linear separation.

Having used PCA to demonstrate that separability can be achieved, an artificial neural network was

trained to build a classifier. In this example, a multi-layered perceptrons was trained using 10% of

the randomly extracted spectra from two different data sets: swimmer target and non-swimmer target,


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respectively. The swimmer data set was collected when the swimmer was on the surface of the water

for adjusting the swimming direction and the bottle data set was collected when the swimmer was not

present.

Fig. 17. Classification result for swimmer vs. bottles at Marion lake, AK. The swimmer track stands out with air bubbles that were caused

by the swimming (kicking) motion. Two tracks of bottles at about 30 m and 70 m were classified differently from that of swimmer.

The trained neural network was used to colorize target detections by class. Fig. 17 shows the final

classification result. The black detections represent the swimmer and the gray detections represent thebottle. Although there are several intermittent misclassifications, the overall track of the swimmer appears

obvious as well as that of the bottle. In fact, this plot does not simply show the testing of the classification,

but includes both training and testing. However, using only a small fraction of the entire data set provided

very good generalization capability.

C. Broadband Experiment - Smolt Fish Identification

The Pacific Northwest National Laboratory conducted a study of the broadband fisheries sonar systems

ability to discriminate between Chinook Salmon smolt and aquatic macrophytes [34]. Like the counting

study that was performed with the split-beam evaluation, a blind study was conducted in which a trusted

third party (R. Johnson) held back the identity of the test subjects until after the system reported its results.Data collection was conducted at one of the rectangular ponds in 100K-basin area at the Hanford

Site near Richland, WA, from August 31 through September 2, 2004. All data collection work was

conducted with a horizontal acoustic projection across the narrow axis of the pond with the anticipation

of discriminating Chinook salmon smolt, in dorsal or ventral aspect from various assemblages of aquatic

macrophytes.

The targets were located at 16.7 m from the transducer, and the back-wall was 1.7m beyond the target

(18.4 m from the transducer). The water depth of the pond was approximately 5.1m, and the transducer

and the acoustic targets were deployed in the middle of the water column approximately at 2.55 m from

the bottom of the pond as illustrated in Fig 18.


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Fig. 18. Geometry of the pond that field test was conducted in the Hanford Site near Richland, WA. Dimensions of the pond and the

relative distance between target and transducer are illustrated.

Juvenile fall Chinook salmon 130-150 mm in length were transported to the site live in a large aerated

cooler. Just prior to being tested fish were anesthetized using MS 222. All fish were tested in good to

excellent condition (i.e. minimal scale and fin loss). Macrophytes were collected at a marina in North

Richland and consisted of water milfoil and elodea.

A special tool was devised to control the aspect of targets. A long bar was tied across the top of the

hand railings on either side of a walkway and another bar attached at one end of the long bar was rotated

by a rope to change the aspect angle of the target. The fish and macrophytes were positioned in the centerportion of the sonar beam along the MRA. To suspend the fish, clear monofilament line was fastened to

the bar and the fish was fastened to the line by threading line down one side of the mid portion of the

fish. A lead weight was added approximately 4 ft below the fish to keep the line tight. To achieve the

proper fish orientation a separate section of monofilament was attached the pivoting arm 4 ft from the fish

line. The line was attached to mouth region and weighted at the lower end. By moving the pivoting arm

the fish aspect could be changed. For all tests on either a dorsal or ventral aspect was used. Macrophytes

were deployed by using a separate weighted line and attached using a small section of monofiliament to

a loop in the line.

The transducer was mounted at the end of a metal tube, which was attached to a wood block tied to


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the bottom rails of the path to keep the transducer in the middle of the water column firmly pointing

straight to the target under the changing weather condition.

The dry end side of the combined sonar system, incuding a PC, monitor, and terminal box, were set

up in a rented moving truck, which was about 50 m away from the wet-end deployment. 100 m-long

SciFish2100 transducer cable connected between the transducer and the terminal box.

The first 1.5 days were used for the data collection and the creation of the neural network classifier.

The remainder of the time was used to collect blind test sets. In total, 8 different fish and 7 different

macrophytes were used to collect training data. Also, 86 unknown targets were used to collect blind test

datasets, whose identification would be released after the test results were provided.

All 4,257 pings of the collected data were fed to train a neural network. Of the classifier samples,

2419 samples are actual fish and the remaining 1838 samples are actual macrophytes. The classifier made

probabilistic decision in terms of the degree of similarity between 0 and 1.0. If the value is close to 0,

the sample is fish, and if it is close to 1.0, it is macrophyte. However a hard (deterministic) decision can

also be made by applying a threshold value.

A total of 86 blind test sets were collected during the 2nd and 3rd day, but the identifications of 5

blind test sets (ID 20911, 20919, 21110, 21125, 21134) were released later and used for training the

neural network classifier. Thus, those five data sets were not considered as true blind test data sets and

they were not counted for testing. Since all blind test data sets were collected under in a more controlled

environment and under slightly different weather conditions than the training data sets, adding those sets

would make training data sets more representative of a varying realistic environment.

A total of 81 unknown and 5 known data sets were tested to evaluate the performance of the classifier

built by the known data sets. Table II shows the summary of the blind test results. The columns under

Classified as were outputs from the neural network classifier. Each decision with higher probability

above 50% was declared as corresponding class. The columns under Reality (Ground Truth) were

actual truth table released after we had provided the outputs of classifier to Pacific Northwest National

Lab. The column Result tells if the classifier made correct decision or not.

TABLE II. CLASSIFICATION RESULT FROM BLIND TESTS BASED ON THE CLASSIFIER BUILT USING THE KNOWN SAMPLES.

Order Target ID PingsClassified as Reality (Ground Truth)

ResultFish Macrophyte

Count % Count % Decision Actual Aspect

1 11451 251 239 95.21 12 4.78 Fish Fish dorsal TRUE


3 11513 260 184 70.76 76 29.23 Fish Fish ventral TRUE

4 11522 260 16 5.38 281 94.61 Macrophyte Macrophyte small/short TRUE

5 11531 211 14 6.63 197 93.36 Macrophyte Macrophyte large/short TRUE


7 11548 248 19 7.66 229 92.33 Macrophyte Macrophyte long/skinny TRUE


9 11604 238 90 37.81 148 62.19 Macrophyte Macrophyte no entry TRUE

10 11609 257 113 43.96 144 56.03 Macrophyte Fish dorsal FALSE


12 11630 277 78 28.15 199 71.84 Macrophyte Macrophyte very small TRUE








20 11746 219 2 0.91 217 99.08 Macrophyte Macrophyte small millfoil TRUE


continued on next page


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continued from previous page

Order Target ID Pings

Classified as Reality (Ground Truth)



22 20835 268 1 0.37 267 99.62 Macrophyte Macrophyte small poto TRUE


24 20857 276 111 40.21 165 59.78 Macrophyte Macrophyte long strand TRUE

25 20906 292 0 0 292 100 Macrophyte Macrophyte short millfoil TRUE

26 20911 136 136 100 0 0 Fish Fish dorsal TRUE

27 20919 121 121 100 0 0 Fish Fish ventral TRUE



30 20945 268 158 58.95 110 41.04 Fish Macrophyte no entry FALSE






36 21014 198 57 28.78 141 71.21 Macrophyte Macrophyte small TRUE


38 21026 187 41 21.92 146 78.07 Macrophyte Macrophyte small/longer TRUE


40 21039 154 140 90.90 14 9.09 Fish Macrophyte very small poto FALSE





45 21103 210 184 87.61 26 12.38 Fish Macrophyte small poto FALSE

46 21110 123 0 0 123 100 Macrophyte Macrophyte med. Millfoil TRUE


48 21120 242 98 40.49 144 59.50 Macrophyte Fish ventral FALSE

49 21125 239 0 0 239 100 Macrophyte Macrophyte large poto TRUE


51 21134 206 0 0 206 100 Macrophyte Macrophyte small poto TRUE

52 21141 223 26 11.65 197 88.34 Macrophyte Macrophyte small poto TRUE


54 2115 200 169 84.50 31 15.50 Fish Macrophyte small millfoil FALSE

55 21158 186 17 9.13 169 90.86 Macrophyte Macrophyte small millfoil & poto TRUE

56 21202 220 8 3.63 212 96.36 Macrophyte Macrophyte bigger bundle poto TRUE

57 21209 205 69 33.65 136 66.34 Macrophyte Macrophyte poto TRUE




61 21309 207 43 20.77 164 79.22 Macrophyte Macrophyte med poto TRUE

62 21314 157 41 26.11 116 73.88 Macrophyte Macrophyte med poto TRUE

63 21319 196 126 64.28 70 35.71 Fish Macrophyte smaller millfoil FALSE

64 21324 233 30 12.87 203 87.12 Macrophyte Macrophyte long poto TRUE


66 21333 228 15 6.57 213 93.42 Macrophyte Macrophyte long poto TRUE

67 21338 209 94 44.97 115 55.02 Macrophyte Macrophyte small milllfoil TRUE


69 21351 254 208 81.88 46 18.11 Fish Fish ventral TRUE70 21355 215 206 95.8 9 4.18 Fish Fish dorsal TRUE










continued on next page


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continued from previous page

Order Target ID Pings

Classified as Reality (Ground Truth)



80 21443 230 59 25.65 171 74.34 Macrophyte Macrophyte long strand poto TRUE


82 21501 210 103 49.04 107 50.95 Macrophyte Macrophyte med. poto TRUE


84 21522 170 54 31.76 116 68.23 Macrophyte Macrophyte med. poto TRUE

85 21528 253 102 40.31 151 59.68 Macrophyte Macrophyte long strand poto TRUE


Excluding the five known blind test sets, the results showed that 70 true decisions and 11 false

decisions, resulting in (86.42% correct decision) The system had the most difficulty distinguishing fish

from macrophytes when smaller portions of macrophytes were used. The confidence levels were near 50%

in the majority of the cases.During 3-day data collection period, we had rough weather condition, especially, on 2nd and 3rd day

due to strong wind. One of the possible reasons of the incorrect classification could be that the relative

target-transducer geometry change often pushed the target out of the beam angle causing weak signal

return and intermittent detections. In such a case the returned signals usually dont represent the whole

target.Overall performance of broadband fish identification was satisfactory in discriminating smolt-size

Columbia river Chinook salmon against aquatic macrophytes.

VIII. CONCLUSION

This paper has presented a recently developed sonar system that combines a broadband single-beam and

narrowband split-beam into a single sonar system. By combining these two sonar modalities, it provides

a research tool that can provide target tracking and target identification a capability that is not available

in either system independently.Furthermore, synergistic impacts from both modalities provides exceptional performance. We discussed

details of broadband spectrum adjustment using beam compensation from split beam bearing angle and

sensitivity of transducer. Also we used instantaneous time frequency representation to create variouspseudo-narrowband signals for multi-frequency data streams.

Field evaluations have been conducted in Alaskan lake and rivers to verify that each portion of the

system narrowband and broadband provided the ability to track and identify targets.Efforts are currently underway to validate and automate the beam-compensation capability provided by

the split-beam processing and improve the real-time performance of the multi-frequency portion of the

sonar.

APPENDIX A

TECHNICAL SPECIFICATION OF COMBINED SPLIT-B EA M / BROADBAND SONAR SYSTEM

Technical specification of high frequency version (135 kHz to 195 kHz) of the combined split-beam

narrowband / single-beam broadband sonar system is described below. the low-frequency version sharesmost of the technical specification except the frequency range (60 kHz to 120 kHz). Fig. 19 provides a

picture of the sonar system.

ACKNOWLEDGMENT

The authors would like to thank Alaska Department of Fish and Games (ADF&G) and Alaska Sus-

tainable Salmon Fund (AKSSF) for their supports and thoughtful reviews during several rounds of

development. They would also like to thank Donald Degan and Anna-Maria Mueller from Aquacoustics,

Inc. and Timothy Mulligan for their valuable comments and reviews. Further, they thank the reviewers

of this paper for very insightful comments and suggestions, which they believe have led to a remarkable

improvement in quality and presentation of this paper.


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TABLE III

TECHNICAL SPECIFICATION OF HIG H FREQUENCY VERSION OF THE COMBINED SPLIT-B EA M/B ROADBAND SYSTEM

Transmit Specification

Beam Shape/Width 6 degrees conical at 165kHz

Frequency, Broadband 135kHz to 195 kHz

Frequency, Split-Beam 16510 kHz

Frequency, Multi-Freq. Any non-overlapping 5 kHz bands between 135 kHz and 195 kHz

Waveform, Split-Beam & Multi-Freq. Continuous Wave (CW)Source Level 214 dB at 165 kHz

Detection Range (min/max) 12 to 200 m at 165 kHz

Range Resolution, Broadband 1.2 Cm

Power Settings 16, from 0 (listen only) to 15 (max)

Pulse Length, Broadband & Multi-Freq. 15.40 sec (compressed)

Pulse Length, Split-Beam 200 sec (adjustable)

Ping Rate (min/max) 1 ping / 2 secs to 30 pings / sec

Receive Specification

Bandwidth, Broadband 60 k Hz

Bandwidth, Split-Beam 10 k Hz

Gain 16 settings (programmable between 20 d B and 60 d B

A/D Conversion 500 Ksamples/sec per channel simultaneously

Split-Beam Angular Resolution 0.1 degrees

Processor SpecificationSingle Board Computer 1.1G Hz Pentium M

A/D & D/A Converter 4 channels, 500 Ksamples/sec

Network TCP/IP Ethernet (10/100/1000 M bps)

Software Specification

Data Storage 4 channels, raw data

Export Formats Echoview (multi-freq, split-beam)

Displays Sonar Grams, Split-Beam

Dimensions Specification

Sonar 22.8 Cm Diam, 53.8 Cm Length

Cable 30 m Length, OD 1.45 Cm

Fig. 19. Combined split-beam narrowband / single-beam broadband sonar system with terminal box and underwater cable.


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Jae-Byung Jung (S01-M02) received the B.S. and M.S. degrees in electronics engineering from Hanyang University,

Korea, in 1993 and 1995 respectively. He was an Assistant Research Engineer at LG Industrial Systems, Co., Ltd.,

Korea, from 1995 to 1996. He earned the Ph.D. in electrical engineering from the University of Washington, Seattle,WA, in 2001. From 2001 to 2002, he was a Post-Doctoral Research Associate at the Department of Electrical

Engineering, the University of Washington. In 2002, he joined Scientific Fishery Systems, Inc., where he is currently

a Senior Engineer responsible for research and development of underwater acoustic instruments.

His areas of interest include neural networks, fuzzy systems, evolutionary algorithms, signal processing, computer

vision, robotics, and software engineering.

Alexander B. Kulinchenko was born in Lviv, Ukraine in 1971. In 1993, Mr. Kulinchenko received Electro-Mechanical

Engineering Degree in State University Lvivska Polytechnika, Lviv, Ukraine. Continuing education Mr. Kulinchenko

received Master of Science Degree in Electro-Mechanical Engineering in State University Lvivska Polytechnika,

Lviv, Ukraine, in 1994. In 1995 Mr. Kulinchenko moved to Detroit, MI where he worked as draft assistant at Lear

Seating Corporation, Plymouth, MI. In 1997, Mr. Kulinchenko moved to Anchorage, AK. In December 2001, Mr.Kulinchenko graduated from University of Alaska in Anchorage with Bachelor of Science Degree in Computer Science.

Since August 2001, Mr. Kulinchenko has been working as software engineer at Scientific Fishery Systems, Inc. Mr.

Kulinchenko has experience in software applications development, modifications and testing. Good knowledge of Java,

Enterprise Java Beans (EJB), Extensible Markup Language (XML), C++, Intel Image Processing Library (IPL), Open

Source Computer Vision Library (Open CV), Visual Basic (VB), MatLab and Neural Networks. Knowledge of Microsoft Visual Developer

Studio (C++, VisualBasic, SourceSafe), Borland JBuilder (Java).

Mr. Kulinchenko has also experience in electro-mechanical engineering including knowledge of AutoCAD.

Patrick K. Simpson received his Bachelor of Arts degree in Computer Science in 1986 from the University of

California at San Diego. Soon after graduating, Mr. Simpson has distinguished himself in the application of neural

networks, fuzzy systems, and artificial intelligence to difficult defense related problems in areas such as electronic

intelligence, radar surveillance, sonar signal identification, and various aspects of automated diagnostics.

Early in his career, Mr. Simpson wrote several archival papers, taught several short courses, wrote a text book

that has been used in college courses around the United States, and lectured on the theory and application of neural

networks and fuzzy systems world wide. Mr. Simpson served as President of the IEEE Neural Networks Council in

1994 and was the General Chairman of the 1998 IEEE World Congress on Computational Intelligence.

In 1992, Mr. Simpson founded Scientific Fishery Systems, Inc. (SciFish) to develop technologies for more efficient

fisheries. Through initial funding from the Small Business Innovative Research (SBIR) program, and subsequent funding from commercial

sales, government sales, and continued research and development, SciFish has developed several technologies that have been applied to

various aspects of the fishing industry.

James W. Tilley received the B.S. degree in electrical engineering from the University of Nevada, Las Vegas in 1998.

Upon graduation, Mr. Tilley moved to the Seattle area and began work in the field of electromagnetic compatibilitytesting. In January of 2005, Mr. Tilley began working for Scientific Fishery Systems on their split-beam sonar system.

Later that year, he transferred to sister company Alaska Native Technologies, LLC. where he is currently working on

unmanned underwater systems.

Documents

JOE Manuscript v07