Doppler estimation and correction for shallow underwater acoustic communications
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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

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Doppler estimation and correction for shallow underwater acoustic communications l.jpg
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 Computers

Nov. 9, 2010


Underwater acoustic network l.jpg
Underwater Acoustic Network Acoustic Communications

Buoys

Access Point

Divers

Users

Seafloor

Datalink

UUVs


Underwater acoustic channel l.jpg
Underwater Acoustic Channel Acoustic Communications

  • 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 reverberation

      High energy

      Long time constant

Measured shallow water channel impulse responsesRange is 30m for position 1 and 1260m for position 3.


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Underwater Acoustic Channel Acoustic Communications

  • Doppler effects for received QPSK signal

    • Results from linear bulk Doppler correction

Decision regions


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Proposed Contributions Acoustic Communications

  • Shallow underwater acoustic communications

    • One-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)

  • Evaluate SNR performance of three Doppler estimation methods

  • Evaluate static and adaptive equalizers


Bulk doppler estimation l.jpg
Bulk Doppler Estimation Acoustic Communications

  • Approach 1: Self-referenced correlation

    • Transmit two copies of training sequence

    • Use phase in cross-correlation of received symbols

Symbols

Rep. 1

Rep. 2

Payload…

Calculate phase offset in decoded 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


Bulk doppler estimation7 l.jpg
Bulk Doppler Estimation Acoustic Communications

  • Approach 2: Carrier recovery

    • Observe peak FFTfrequency of squaredsamples (in binaryphase shift keying(BPSK) case)

    • Compare observedfrequency withexpected centerfrequency (withoutDoppler)


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Bulk Doppler Estimation Acoustic Communications

  • Approach 2: Carrier recovery

    • Variation: slice packet into “windows”

    • Rough adaptation to time-varying Doppler effects


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Bulk Doppler Estimation Acoustic Communications

  • Approach 3: Pilot tone

    • Encode 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


Bulk doppler estimation10 l.jpg
Bulk Doppler Estimation Acoustic Communications

  • Approach 3: Pilot tone

    • Variation: slice packet into “windows”:

87 kHz tone

+/- Doppler

Data:

62.5 kHz center;

31.25 kHz BW


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Windowing Tradeoffs Acoustic Communications

  • QPSK decoding

250 ms

125 ms each

62.5 ms each

31.25 ms each


Packet structure l.jpg
Packet Structure Acoustic Communications

  • Linear frequency modulated (LFM) chirp

    • Resistant to Doppler

  • Training – 128 symbols

    • 4 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


Experimental setup l.jpg
Experimental Setup Acoustic Communications

  • Applied Research Laboratories Lake Travis Test Facility

    • Lake

      37 m depth

      Former riverbed

      Nearby dam

    • Transmitter on research vessel

    • Receiver on barge at test station


Data collection points l.jpg
Data Collection Points Acoustic Communications

1: 15mdocked

2: 325-375mfloating

3: 1235-1285mfloating

4: 185-255mvertical motion

5: 300-80mtowing at ~3 kts


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Static Equalizer Acoustic Communications

Decision

Feedforward taps

Σ

x[m]

y[m]

5 feedforward taps3 feedback taps

Feedback taps


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Fully Adaptive Equalizer Acoustic Communications

Decision

Feedforward taps

Σ

x[m]

y[m]

Update

5 feedforward taps3 feedback taps0.01 learning rate

Feedback taps

Update: O(N) per symbol

(N = total # of taps)


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Issues with Windowing Acoustic Communications

  • 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


Experimental results l.jpg
Experimental Results Acoustic Communications

  • Average estimated SNR for bulk Doppler detection/correction and equalization

    • Carrier 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


Experimental results19 l.jpg
Experimental Results Acoustic Communications

  • Self-referenced correlation (A) performs poorly

    • Represents tiny packet sample

  • Pilot tone tracking (FGHI) performs poorly in motion case (Pos. 2)

  • Carrier recovery with any number of windows (BCDE) performs best


Conclusions l.jpg

Σ Acoustic Communications

Update

Conclusions

  • Windowing for Doppler detection benefits static equalization

  • Pilot tone method was not reliable

  • Best configuration over entire dataset

    • Single window carrier recovery method

    • Adaptive equalization


Underwater acoustic comm dataset l.jpg
Underwater Acoustic Comm. Dataset Acoustic Communications

  • Experimental Setup

    • 1-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 format

http://users.ece.utexas.edu/~bevans/projects/underwater/datasets/index.html


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Publications and Presentations Acoustic Communications

Conferece 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, 6-4-2010

5-element samples of BPSK, QPSK, 16QAM, 64QAM, and 256QAM signals

Up to 1300 yard range, up to 63 kbit/sec data rate


Data collection l.jpg
Data Collection Acoustic Communications

  • Transmitter

    • Omnidirectional transducer

    • Submerged between 1-8m

  • Receiver

    • 4.6m depth

    • Five directional hydrophones

    • Half-power beamwidths

      • Horizontal: ~45°

      • Vertical: ~10°

  • Sampling rate: 500 kHz

Transmitting Transducer

Sensitivity at 1m


Software receiver l.jpg
Software Receiver Acoustic Communications

  • Frame synchronizer

    • Identify 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


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July Raytracing Acoustic Communications

Surface

  • Severe thermocline:

    • Receiver R can’t directly see transmitters A or B

Lakebed


Channel impulse response l.jpg
Channel Impulse Response Acoustic Communications

Fig. 4. Channel impulse responses (CIR) for near and far ranges. Position

1 range is 30 m and Position 3 range is ~1260 m.


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Experimental Results Acoustic Communications

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)

Pos. 1: 15m, docked

Pos. 2: 325-375m,free floating


Experimental results29 l.jpg
Experimental Results Acoustic Communications

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)

Pos. 4: 185-255m,vertical motion

Pos. 5: 300-80m,towing at ~3kts


Experimental results30 l.jpg
Experimental Results Acoustic Communications

  • 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.


Experimental results31 l.jpg
Experimental Results Acoustic Communications

  • Pilot tone approach was not be reliable

    • Multipath interference caused selective fading

    • Pilot tone was too narrow in bandwidth


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