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Ref.: PhD Thesis, University of Algarve, October 2022
Abstract:
Low probability of detection (LPD) underwater acoustic communications are an essential
requirement for command and control of autonomous underwater vehicles (AUV) or
submarines performing covert missions, avoiding their detection while communicating.
Based on low power signals, these covert communications may also extend the autonomy
of battery-operated AUVs, and contribute to reducing the impacts of the environmental
noise level on marine life. The present thesis aims to develop LPD communications
based on modeled and real data from three shallow water experiments. Thus, a superimposed
training method has been proposed. A bitstream is created superimposing a
long probe to the message before transmission. Computationally simple, the algorithm
explores temporal diversity to increase the processing gain and uses a Wiener filter for
equalization. Experimental results presented bit-error rates (BER) < 10-2 for
signal-to-noise ratios (SNR) < -8 dB. To understand the effects of coastal upwelling phenomena
over low SNR communications, a study compares the acoustic propagation for different
sound speed profiles using a propagation model and analyzes data from the BioCom'19
experiment, performed off Cabo Frio Island (Brazil). Temporal and spatial coherence of
low power signals propagating in this harsh environment are estimated, and both a criterion
for multichannel combining and a double Wiener filter to improve equalization are
presented. Passive time-reversal (pTR) techniques have been widely employed for communications.
To address the pTR channel mismatch due to the environmental variability
between the probe and the data transmissions, this work proposes a superimposed training
pTR (STpTR) approach for single and multichannel systems. Despite the high noise
levels, varying from -5 to +6 dB, the STpTR combined with a Wiener filter achieved
BER < 10-2, for bit rates up to 220 bps. To improve covert communications for AUVs,
this work also presents a study about vector sensor multichannel combining. Using the
STpTR approach, results from an experiment on the coast of Algarve/Portugal indicate
that combining the pressure and particle velocity channels of a vector sensor may provide
an average SNR and mean squared-error gain of up to 9.4 and 3.1 dB, respectively,
compared to the pressure channel. Therefore, a better understanding of the environment
combined with the superimposed training pTR using a vector sensor may improve the
LPD communication system's performance and robustness while keeping covertness.
Keywords: Low probability of detection, underwater acoustic communications,
superimposed training, coastal upwelling, passive time-reversal, vector sensor.