A. Malarkodi et al.: Acoustic Scattering of Underwater Targets 127

Acoustic Scattering of Underwater Targets

A.Malarkodi 1, D. Manamalli 2, G. Kavitha 2 and G. Latha1 1 National Institute of Ocean Technology, Chennai- 600100, India

2 Madras Institute of Technology, Chennai, India

malar@niot.res.in, manamalli_m@yahoo.com kavithagmit@gmail.com, latha@niot.res.in

Abstract — The objective of this paper is to provide feature extraction algorithm for underwater targets. The targets are homogeneous elastic bodies of finite dimensions. The targets considered are a brass sphere, a PVC sphere, a brass cylinder, a PVC cylinder, concrete block and MS cylinder of different dimensions. The incident acoustic signal used was a linear frequency modulated (LFM) signal of finite duration with the signal bandwidth of 40 kHz to 80 kHz. The scattered acoustic signal from the targets are recorded and processed for feature selection. The scattered signals were analysed using power spectrum analysis, Linear Predictive Coding and Auto Regressive (AR) modelling , and its statistical features are extracted for all the targets. The nature of the backscattered signal for the underwater targets is also explained. The extracted features are passed into the feed forward neural network (FFNN) classifier. FFNN was used to identify the targets of six classes, to check the validity of extracting the feature of the targets. The result of the neural network shows that this feature extraction algorithm could enhance the fractal features of the signals and reduce the number of dimensions of the feature space and prove that it can efficiently classify underwater targets. A comprehensive study was then carried out to compare the classification performance by using these data sets in terms of performance analysis like specificity and sensitivity.

Index Terms— Acoustic back scattering, Linear Predictive Coding, AR modelling, neural network

1. Introduction

The problem of classifying underwater targets from the acoustic backscattered signals involves discrimination between different targets as well as the characterization of background clutter. In the ocean several factors are complicate this process including highly a variable and reverberant environment, ambient noise presents in the ocean, lack of a prior knowledge about the shape of the underwater targets, non repeatability and variation in the its signatures. A number of different classification schemes have been developed in recent years.

Most of the work involves steady state monochromatic plane waves incident upon elastic targets which scatter the incident energy into outgoing spherical waves in the far field. In this paper the authors considered the different geometries of underwater targets of spherical, cylindrical and cuboid shapes. The different materials like PVC, Brass, MS and concrete are used for the experiment. The target strength for different shapes can be defined to be logarithmically related to the sound incident and sound scattered from a target to a range of 1m.

128 PROCEEDINGS OF SYMPOL 2013

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⎛=⎟⎟

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i

r

i

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IITS log*20log*10

Ir, Pr - Scattered signal intensity and pressure Ii, Pi - Incident signal intensity and pressure

For sound waves incident on an object,

whose dimensions are small, compared to the wavelength of the sound, scattering occurs. In perfectly reflecting objects, the most important parameters that affect the target strengths are size, shape, aspect angle and frequency for a given target. If an object is large compared to the wavelength of sound as well as rigid, immovable and non deformable in the sound field and impenetrable under the sound wave's impact, it is possible to derive analytical expressions for the scattered sound [2].

Echoes from randomly rough objects will fluctuate from realization to realization. These

fluctuations are due to the fact that there is interference from the unresolved portions of the scattered and the pattern of interference is different in each realization, hence producing a different echo.

These fluctuations depend upon the size, shape, orientation and material properties of the object as well as the wavelength of the incident sound field [2], [3]. Owing to the variations in the target signature and the environmental conditions, the feature vector will clearly undergo some variations. These variations are studied for different objects of finite geometries with different material properties. The inherent information about the signal was extracted using statistical characteristics of the power spectrum, Linear Predictive Coding (LPC) and Auto Regressive (AR) modelling.

[1][1]

A. Malarkodi et al.: Acoustic Scattering of Underwater Targets 129

Fig. 1 : Measurement Setup

2. Measurement Methodology

The experiment was conducted in the Acoustic Test Facility of the National Institute of Ocean Technology. Six objects of different geometries were used for this experiment. A PVC sphere of 500mm diameter, a Brass sphere of 500mm diameter, a PVC cylinder of 750mm length, a Brass cylinder of 750mm length, a concrete block and a MS cylinder of 500mm length are used for scattering experiment.

These measurements were carried out in a controlled environment, namely an acoustic tank which has the dimension of 16m x 9m x 7m (l x b x d). The experimental setup is as shown in Fig1. The incident signal was a linear frequency modulated (LFM) chirp with 1m sec in duration with the frequency band of 40kHz to 80kHz.

The incident and backscattered signals were generated and recorded respectively using NI data acquisition system model PXI 6124 with the sampling frequency of 400 kHz. The projector and receiver hydrophones used are B&K 8104 with the nominal sensitivity of -206dB re 1V/µPa@1m. Control software for signal generation, data acquisition and storage are written by Labview programming environment. 100 iterations were tried for each object to create a good data base. A good data base was created for comparison and classification of different underwater objects. Appropriate signal scaling and removing artifacts like noise, reverberation effects are carried out before classification.

Since the geometry of the experimental setup is known, the distance between the source and the receiver, the delay in the arrival of the scattered signal through the direct path and the next arrival of the signals coming from the boundaries are known. The small duration of the pulse makes it easier to discriminate

between the scattered and reflected waves. Scattered signal from the receiver output was also checked by cross correlating the receiver output with incident signal and finding out the location of the scattered signal peak with matched filtering. The segmented output of the scattered signal was used for further analysis.

Fig. 2 : (a) Receiver output with boundary reflections, (b) Scattered signal (c) Frequency spectrum

3. Feature Selection and Classification

The feature selection and classification scheme is carried out based on the block diagram shown in Fig 3. Spectrum analysis is a frequency domain tool for signal analysis and characterizes the frequency content of a measured signal. Feature selection involves

Scattered signal

130 PROCEEDINGS OF SYMPOL 2013

power spectrum analysis, Linear Predictive Coding and AR modelling. The power spectral density [3],[5] was calculated for the scattered signal. Statistical features [4] like spectral flux, spectral centroid, spectral roll off, mean, median, standard deviation, spectral flatness, skewness and kurtosis of the curve shape were extracted.

Fig. 3: Feature selection and classification scheme

In the second stage LPC was applied to the scattered signal. The LPC scheme is commonly used for speech coding and recognition applications. LPC [7] was applied to the scattered signal of each object and the coefficients are extracted for each type. LPC is implemented with the linear combination of past samples and can be written as [7]

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neknxanxp

kk +−=∑

=

x[n-k] - Previous samples p - order of the model ak - Prediction coefficient e[n] - prediction error

Autoregressive (AR) parametric spectral estimation of the scatterer signal is applied to the analysis of acoustic scattering by elastic objects. Autoregressive modelling method or all pole model has some advantage over other method is that the estimation of the AR parameters results in linear equations which are

easy to implement and the other advantage of the model is that it is equivalent to a maximum entropy method. A fourth-order linear autoregressive (AR) model is employed for this signal representation and the corresponding coefficients ai’s are used as features for classification.

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A multi layer feed forward neural network [8], [9] has been used for classification. A multilayer feed forward neural network is an interconnection of perceptrons in which data and calculations flow in a single direction, from the input data to the outputs. The number of layers in a neural network is the number of layers of perceptrons. The simplest neural network is one with a single input layer and an output layer of perceptrons.

Fig. 4: Architecture of feed forward neural

network

Mathematically the functionality of a hidden neuron is described by

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where the weights {wj, bj} are symbolized with the arrows feeding into the neuron.

Pre processing

Scattered signal

LPC coefficients

AR coefficients

Power spectral statistics

Neural network

Incident signal

[2]

[3]

[4]

A. Malarkodi et al.: Acoustic Scattering of Underwater Targets 131

The network output is formed by another weighted summation of the outputs of the neurons in the hidden layer. The summation on the output is called the output layer. The output of this network is given by

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where n is the number of inputs and nh is the number of neurons in the hidden layer. The variables {wi

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4. Results and Discussion

In the present study 100 data sets were used for each underwater object. 70 data sets were used for training the network and the remaining data sets were used for testing. The performance of this model was determined by the computation of sensitivity and specificity (Table 1). The sensitivity and specificity are defined as [7]

� Sensitivity =Number of true positive decisions divided by the number of actual positive cases

� Specificity=Number of true negative decisions divided by the number of actual negative cases

Table 1. Performance of the classifier

Targets Sensitivity %

Specificity %

PVC sphere 86 85

Brass Sphere 91 92

PVC cylinder 90 91

Brass cylinder 95 94

MS cylinder 93 91

Concrete block 91 92

5. Conclusion

In this study, the statistical characteristics of power spectrum, LPC and AR coefficients are extracted for underwater acoustic scattered signal of different targets with spherical and cylindrical shapes of brass and PVC materials, concrete block and MS cylinder. Finally, a three layer feed-forward ANN was used for classification. It is observed that the classification results proved that this method is successful for discriminating underwater objects with different material. Therefore, it can be concluded that this technique can be used for analyzing scattered signal for underwater acoustic signal analysis. However the optimum frequency bandwidth can be compared with more number of underwater targets. Though the experiment was conducted in the acoustic tank of closed environment, the classification can be augmented in the presence of ocean noise with improved success rates.

ACKNOWLEDGMENTS The authors gratefully acknowledge the

support given by The Director, National Institute of Ocean Technology. The authors would like to express their sincere thanks to team consisting of Mr.C.Dhanaraj, Mr.K.Nithyanandham, Ms.M.Dhanalakshmi and Mr. K. Rajesh for conducting scattering experiment at Acoustic Test Facility, NIOT.

References

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