1. Applied Research Laboratories: The University of Texas at Austin
Wideband Monopulse Spatial Filtering for Large Array Receivers for Reverberant Underwater Communication Channels
Karl Nieman
Applied Research Laboratories: The University of Texas at Austin
Kenneth Perrine, Terry Henderson, Keith Lent, Terry Brudner, and Brian Evans
IEEE OCEANS
9/22/10

2. Outline Underwater Acoustic Communications (ACOMMS)
Vertical Array ACOMMS Receiver
Wideband Monopulse Beamforming
Multi-Channel Equalization
Multipath Simulation
Experimental Results from Lake Travis
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3. ACOMMS with Large Array Receiver
Buoys
Array
Receiver
Divers
Communicator(s)
Seafloor Instruments
UUVs
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4. ACOMMS vs. RF Wireless Comms
Acoustic propagation speed slower by 200,000X
More severe effects of time-varying boundaries and Doppler
Absorption-limited bandwidth
In 10°C seawater:
1 kHz: ~0.06 dB/km
100 kHz: ~33.4 dB/km
USEABLE BAND (at 1 km)
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5. Shallow Water ACOMMS Channel Impairments
Severe Doppler may be present
Substantial vertical multipath due to boundary reflections
Rapidly-varying channel
Coherence time is only marginally longer than impulse response duration:
absolute amplitude
Shallow water impulse response of LFM sweep over 1 s.
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6. ACOMMS Signal Processing
Compensating for channel impairments:
Detect, synchronize and compensate for Doppler
Adaptively equalize
Adaptive equalizer size dilemma:
Small to maintain stability while tracking quickly-varying paths
Large to fully deconvolve long channel response
Often, all acoustic paths cannot be coherently combined, leaving residual incoherent paths that degrade performance.
Performance is then reverberation limited rather than background-noise limited
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7. Single-Channel ACOMMS Receiver
wet
dry
Frame detection/
synchroni-zation
Bulk Doppler detection/
correction
Single channel adaptive
DFE
omni
receiver
Our configuration:
Adaptive Decision Feedback Equalizer (DFE)
11 half-symbol spaced forward taps
5 decision feedback taps
Hybrid LS-LMS algorithm with adaptation step size 0.01
4096 M-QAM data symbols per packet
Problem: Performance is limited in shallow water
Solution: Substitute a large array of closely-spaced elements for omni receiver
Use vertical multi-channel processing to combat multipath
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8. ACOMMS Receiver Using Array
wet
dry
Horizontal beamform/select
Vertical processing options
Frame detection/
synchroni-zation
Bulk Doppler detection/
correction
K-channel adaptive
DFE
N layers
We compare four vertical processing options:
K=N option: Pass all N outputs to DFE
K=1 option: Form single vertical beam s0(t) for DFE
K=2 option: Form two vertical wideband monopulse beams for DFE
K=3 option: Form three vertical wideband monopulse beams for DFE
K=N has been addressed by many investigators
But often with large vertical element separation
Large N can threaten equalizer success
K=1,2, or 3 can exploit beamformer capabilities already needed for other purposes
9/22/10 K. Nieman, IEEE OCEANS’10 8

9. Explanation of Vertical Processing for K =1,2,3
K=1 option:
Form single vertical beam output s0(t)
Steer to reject bad multipath (e.g. boundary reverberation)
K=2 option:
Use wideband monopulse processing to get 2nd beam output s1(t) such that s1(t) ≈ α s0(t) where
K=3 option:
Include a 3rd beam output s2(t) such that s2(t) ≈ α s1(t)
sum
weight
delay
N elements
Spatial response
beamwidth
(frequency independent)
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10. Wideband Monopulse Wideband Null Steering
Simple linear combination of s0(t) and s1(t) gives single wideband null:
Linear combination of s0(t), s1(t), and s2(t) gives two indepedently-steerable wideband nulls
Equalizer can form these linear combinations (or any others), in the K=2 and K=3 cases, to best minimize MSE
In fact, it does
Monopulse method thus reduces the number of input channels to the equalizer yet allows a spatial null(s) to be steered toward localized interferences
0.17s0(t) + s1(t)
linear
combination
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11. Results of Simulation and In-Water Tests at Lake Travis
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12. Shallow Water Simulation
Ray trace from omni-directional source to 6-element vertical line array for different shallow water channels (ranges of m)
Isotropic sound speed
3 paths (direct, surface bounce, and bottom bounce)
source
array
Performance metric used: output signal-to-noise-ratio (OSNR)
Inverse of mean squared error at DFE output
Ray trace of propagation paths through isotropic medium.
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13. No Doppler Simulation , K=1 option: K=2 option: K=3 option:
K=N option: ,
OSNR at EQ output for stationary 6-element line array processed using four different techniques.
9/22/10 K. Nieman, IEEE OCEANS’10 13

14. 5 m/s Doppler Simulation
K=1 option:
K=2 option:
K=3 option:
K=N option: ,
OSNR at EQ output for moving 6-element line array processed using four different techniques.
9/22/10 K. Nieman, IEEE OCEANS’10 14

15. Evidence of Null-Steering in Equalized Channel
equalizer length
path 1
(direct)
path 2
(surface)
path 3
(bottom)
K=1
LFM matched-filter of EQ-forward filtered output for (a) s0(t), (b) s0(t) + s1(t), and (c) s0(t) + s1(t) + s2(t). Orange and green shaded region shows EQ time span for forward and feedback filter taps, respectively.
K=2
K=3
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16. Experimental Data from Lake Travis
Two sets of data collected at ARL:UT’s Lake Travis Test Station (LTTS) in 2009 using array receiver + mobile, omni source
Overhead view of Lake Travis Test Station with overlaid bathymetric map.
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17. Test A: 6-Element Vertical Line Array
Evaluated gain in OSNR of K=2 re: K=1 vs. range
Just as in simulation, gains are highly dependent on range (multipath structure), and decrease with increasing range
OSNR gain of s1(t) + s0(t) vs. s0(t) for QAM packets. Results sorted by range (red dots, right vertical axis).
9/22/10 K. Nieman, IEEE OCEANS’10 17

18. Test B: 8-Element Vertical Line Array
Gain vs. single-element receiver for each technique
K=2,3 have similar mean to K=N w/ less variance
Also, 1-2 orders of magnitude less complexity in DFE alone
K=N
K=3
K=2
K=1
OSNR gain and ±σ over single element receiver for four processing methods for 96 packets at various rates.
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19. Closing Remarks Conclusions Caveats
K=2,3 can reduce the size/complexity of the equalizer w/ similar performance to K=N in shallow water
Smaller equalizer can be stable w/ increased adaptation rate
Outperforms straight beamforming (K=1) in all data
Caveats
Observed gains dependent on:
multipath environment (e.g. range, depths)
platform motion
May not do as well w/ uniform interference
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20. Questions? References:
[1] – R. E. Francois and G. R. Garrison, “Sound absorption based on ocean measurements – 2. boric acid contribution and equation for total absorption,” J. Acoust. Soc. Am., vol. 72, no. 6, pp , 1982.
[2] – K. A. Perrine, K. F. Nieman, K. H. Lent, T. L. Henderson, T. J. Brudner, B. L. Evans, “Doppler estimation and correction for shallow underwater acoustic communications,” Proc. Asilomar Conf. on Signals, Systems, and Computers, 2010.
[3] – K. F. Nieman, K. A. Perrine, K. H. Lent, T. L. Henderson, T. J. Brudner, B. L. Evans, “Multi-stage and sparse equalizer design for communication systems in reverberant underwater channels,” Proc. IEEE Int. Workshop on Signal Processing Systems, 2010.
[4] – P. J. Beaujean and L. R. LeBlanc, “Adaptive array processing for high speed acoustic communication in shallow water,” IEEE J. of Oceanic Engineering, vol. 29, no. 3, pp , 2004.
[5] – T. L. Henderson, “Matched beam theory for unambiguous broadband direction finding,” J. Acoust. Soc. Am., vol. 78, no. 2, pp , 1985.
Also, our group has released underwater acoustic data recorded at Lake Travis for public use:
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21. Bad Doppler Example
The phase of a QPSK signal after linear Doppler correction.
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22. Data Packet
Data packet with durations in symbol periods

23. Wideband Monopulse ACOMMS Receiver
For this study, multi-channel decision-feedback EQ configured with:
11 half-symbol-spaced forward taps per channel
5 symbol-spaced decision-feedback taps
LMS adaptation rate of 0.01
data packet
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