Abstract
We consider the problem of robust direction-of-arrival (DoA) estimation in dynamic ocean environments. Robust direction finding of underwater acoustic signals transmitted from known locations may enable accurate underwater localization and enhance link communication rate by instructing a receiver to listen for transmissions from a specific direction. We propose to estimate the DoA of underwater acoustic signals via subspace methods, executed at a very large receiver array, that involve performing what is known as principal-component analysis (PCA) for finding the L2-norm principal vector subspace of the recorded signal snapshots. However, in practice coherence loss, which typically arises from dynamic wavefront fluctuations due to internal waves, scattering from the sea surface and/or bottom and other unknown environmental parameters, may result in recorded signal snapshots that are corrupted by faulty measurements, leading to an inaccurate estimation of the DoA and source position. We propose to model the loss of coherence as multiplicative random noise applied to the measured acoustic signal. In such cases, L2-norm PCA methods suffer from significant performance degradation. Motivated by the resistance of novel L1-nrom-derived subspaces against the impact of irregular, highly deviating points in reduced-dimensionality data approximations, we propose to employ L1-norm (absolute error) maximum projection PCA of the antenna array measurements and evaluate the performance of a novel, outlier-resistant DoA estimation algorithm.
Published Version
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