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APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
1
Doppler Estimation and Correction for Shallow Underwater Acoustic
Communications
Kenneth A. Perrine*, Karl F. Nieman*, Terry Henderson*, Keith Lent*, Terry J. Brudner*, and Brian L. Evans†
*Applied Research Laboratories: The University of Texas at Austin†Dept. of Electrical & Computer Eng., University of Texas at Austin
Asilomar Conference on Signals, Systems, and ComputersNov. 9, 2010
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
2
Users
Access Point
UUVsSeafloor
Datalink
Buoys
Divers
Underwater Acoustic Network
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
3
Underwater Acoustic Channel
• Propagation speed 200,000x slower vs. RF in air• Lowpass (bandwidth decreases with range)• Wideband communication relative to carrier• Shallow water case
Time-varying Doppler
Channel reverberationHigh energy
Long time constant
Measured shallow water channel impulse responsesRange is 30m for position 1 and 1260m for position 3.
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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Underwater Acoustic Channel
• Doppler effects for received QPSK signalResults from linear bulk Doppler correction
Decision regions
APPLIED RESEARCH LABORATORIES
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Proposed Contributions
• Shallow underwater acoustic communicationsOne-element transmitter (stationary and moving cases)
Quadrature phase shift keying (QPSK)
Carrier frequency 62.5 kHz and 31.25 kHz bandwidth
Transmit 31.25 kbps at distances of 30 to 1285 m
One-element receiver (anchored on floating platform)
1. Evaluate SNR performance of three Doppler estimation methods
2. Evaluate static and adaptive equalizers
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
6
Bulk Doppler Estimation
• Approach 1: Self-referenced correlationTransmit two copies of training sequence
Use phase in cross-correlation of received symbols
Rep. 1 Rep. 2
Payload…
Calculate phase offset in decoded symbols
Symbols
P. Moose, “A technique for orthogonal frequency division multiplexing frequency offset correction,”IEEE Transactions on Communications, vol. 42, no. 10, pp. 2908-2914, Oct. 1994
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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Bulk Doppler Estimation
• Approach 2: Carrier recoveryObserve peak FFT
frequency of squaredsamples (in binaryphase shift keying(BPSK) case)
Compare observedfrequency withexpected centerfrequency (withoutDoppler)
t
f(t)
t
g(t) = f(t)2
t
g(t) = f(t)2
t
g(t) = f(t)2
Expected for
Zero-DopplerDoppler-inflicted
Observation
FFT
ω
|G(ω)|
Expected for
Zero-DopplerDoppler-inflicted
ObservationExpected for
Zero-DopplerDoppler-inflicted
Observation
FFT
ω
|G(ω)|
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
8
Bulk Doppler Estimation
• Approach 2: Carrier recoveryVariation: slice packet into “windows”
Rough adaptation to time-varying Doppler effects
t
f(t)
t
g(t) = f(t)2
Expected for
Zero-DopplerDoppler-inflicted
ObservationExpected for
Zero-DopplerDoppler-inflicted
Observation
FFT
ω
|G(ω)|
Expected for
Zero-DopplerDoppler-inflicted
ObservationExpected for
Zero-DopplerDoppler-inflicted
Observation
FFT
ω
|G(ω)|
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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Bulk Doppler Estimation
• Approach 3: Pilot toneEncode pure tone outside of data band
Average over all measured pilot frequencies to estimate deviation from transmitted frequencies
87 kHz tone+/- Doppler
Data:62.5 kHz center;
31.25 kHz BW
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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Bulk Doppler Estimation
• Approach 3: Pilot toneVariation: slice packet into “windows”:
87 kHz tone+/- Doppler
Data:62.5 kHz center;
31.25 kHz BW
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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Windowing Tradeoffs
• QPSK decoding
250 ms 125 ms each 62.5 ms each 31.25 ms each
APPLIED RESEARCH LABORATORIES
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Packet Structure
• Linear frequency modulated (LFM) chirpResistant to Doppler
• Training – 128 symbols4 length-13 Barker sequences
76 symbols for equalizer training
Symbol rate of 15.625 kHz
• Payload – 3968 symbols• Guard interval at end
100 ms for reverberation analysis
• Pilot tones at 45 and 87 kHz
Packet Structure
Packet Spectrum
APPLIED RESEARCH LABORATORIES
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Experimental Setup
• Applied Research Laboratories Lake Travis Test FacilityLake
37 m depth
Former riverbed
Nearby dam
Transmitter on research vessel
Receiver on barge at test station
APPLIED RESEARCH LABORATORIES
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Data Collection Points
1: 15mdocked
2: 325-375mfloating
3: 1235-1285mfloating
4: 185-255mvertical motion
5: 300-80mtowing at ~3 kts
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Static Equalizer
ΣFeedforward taps
Feedback taps
x[m] y[m]
Decision
5 feedforward taps3 feedback taps
APPLIED RESEARCH LABORATORIES
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Fully Adaptive Equalizer
ΣFeedforward taps
Feedback taps
x[m] y[m]
Decision
5 feedforward taps3 feedback taps0.01 learning rate
Update
–
Update: O(N) per symbol(N = total # of taps)
APPLIED RESEARCH LABORATORIES
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Issues with Windowing• Support for Doppler estimation accuracy decreased• Smaller samples are subject to more noise• Discontinuities (even when smoothed) can lead the
adaptive decision feedback equalizer (DFE) astray• Windowing mostly benefits static equalizer
Successful operation
Problematic
APPLIED RESEARCH LABORATORIES
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Experimental Results
• Average estimated SNR for bulk Doppler detection/correction and equalizationCarrier recovery (BCDE) provides highest SNR.
Adaptive equalizer has best increase in SNR overall
A: Self-referenced correlation
B, C, D, E: Carrier recovery(1, 2, 4, 8 windows)
F, G, H, I: Pilot tone(1, 2, 4, 8 windows) Bulk Doppler Detection Method
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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Experimental Results• Self-referenced
correlation (A) performs poorlyRepresents tiny
packet sample• Pilot tone
tracking (FGHI) performs poorly in motion case (Pos. 2)
• Carrier recovery with any number of windows (BCDE) performs best
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
20
Conclusions
• Windowing for Doppler detection benefits static equalization
• Pilot tone method was not reliable
• Best configuration over entire datasetSingle window carrier
recovery method
Adaptive equalization
t
f(t)
t
g(t) = f(t)2
t
g(t) = f(t)2
t
g(t) = f(t)2
Expected for
Zero-DopplerDoppler-inflicted
Observation
FFT
ω
|G(ω)|
Expected for
Zero-DopplerDoppler-inflicted
ObservationExpected for
Zero-DopplerDoppler-inflicted
Observation
FFT
ω
|G(ω)|
Σ
Update
–
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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Underwater Acoustic Comm. Dataset
• Experimental Setup1-element transmitter
BPSK, QPSK, 4-QAM, 16-QAM and 256-QAM
Symbol rates of 3.9 and 15.6 kHz
With and without pilot tones
Ranges 10m to 1285 m
5-element receiver array in L shape
• Raw data in MATLAB formathttp://users.ece.utexas.edu/~bevans/projects/
underwater/datasets/index.html
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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Publications and PresentationsConferece Proceedings• K. F. Nieman, K. A. Perrine, T. L. Henderson, K. H. Lent, T. J. Brudner and
B. L. Evans, “Wideband Monopulse Spatial Filtering for Large Array Receivers for Reverberant Underwater Communication Channels”, Proc. IEEE OCEANS, Sep. 20-23, 2010 Seattle, WA
• K. F. Nieman, K. A. Perrine, K. H. Lent, T. L. Henderson, T. J. Brudner and B. L. Evans, “Multi-stage And Sparse Equalizer Design For Communication Systems In Reverberant Underwater Channels”, Proc. IEEE Int. Workshop on Signal Processing Systems, Oct. 6-8, 2010, Cupertino, CA
• K. A. Perrine, K. F. Nieman, T. L. Henderson, K. H. Lent, T. J. Brudner and B. L. Evans, “Doppler Estimation and Correction for Shallow Underwater Acoustic Communications”, Proc. Asilomar Conf. on Signals, Systems, and Computers, Nov. 7-10, 2010, Pacific Grove, CA
Released Dataset• “The University of Texas at Austin Applied Research Laboratories Nov.
2009 Five-Element Acoustic Underwater Dataset”, Version 1.0, 6-4-20105-element samples of BPSK, QPSK, 16QAM, 64QAM, and 256QAM signalsUp to 1300 yard range, up to 63 kbit/sec data rate
APPLIED RESEARCH LABORATORIES
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Data Collection
• TransmitterOmnidirectional transducer
Submerged between 1-8m
• Receiver4.6m depth
Five directional hydrophones
Half-power beamwidths• Horizontal: ~45°• Vertical: ~10°
• Sampling rate: 500 kHz
Transmitting TransducerSensitivity at 1m
APPLIED RESEARCH LABORATORIES
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Software Receiver
• Frame synchronizerIdentify LFM chirps via cross-correlation
• Bulk Doppler detection• Bulk Doppler correction
Linear interpolation of oversampled basebanded signal
• Decision feedback equalizer (DFE)Static
Decision-directed adaptive w/ learning rate of 0.01
APPLIED RESEARCH LABORATORIES
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July Raytracing
• Severe thermocline:Receiver R can’t directly see
transmitters A or B
Surface
Lakebed
APPLIED RESEARCH LABORATORIES
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Channel Impulse Response
Fig. 4. Channel impulse responses (CIR) for near and far ranges. Position1 range is 30 m and Position 3 range is ~1260 m.
APPLIED RESEARCH LABORATORIES
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Experimental ResultsA: Self-referenced correlation
B, C, D, E: Carrier recovery(1, 2, 4, 8 windows)
F, G, H, I: Pilot tone(1, 2, 4, 8 windows)
Pos. 1: 15m, docked
Pos. 2: 325-375m,free floating
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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Experimental ResultsA: Self-referenced correlation
B, C, D, E: Carrier recovery(1, 2, 4, 8 windows)
F, G, H, I: Pilot tone(1, 2, 4, 8 windows)
Pos. 4: 185-255m,vertical motion
Pos. 5: 300-80m,towing at ~3kts
APPLIED RESEARCH LABORATORIES
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Experimental Results
• A BER of ~0.5 indicates catastrophic failure in decoding.
• 4 or 8 windows significantly helps the static EQ;
• However, adaptive EQ yields better results overall.
APPLIED RESEARCH LABORATORIES
THE UNIVERSITY OF TEXAS AT AUSTIN
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Experimental Results
• Pilot tone approach was not be reliableMultipath interference caused selective fading
Pilot tone was too narrow in bandwidth