Coherent Multipath Direction-of-Arrival Resolution Using Compressed Sensing

For a sound field observed on a sensor array, performance of conventional high-resolution adaptive beamformers is affected dramatically in the presence of coherent multipath signals, but the directions-of-arrival (DOAs) and power levels of these arrivals can be resolved with compressed sensing (CS). When the number of multipath signals is sufficiently small, a CS approach can be used by formulating the problem as a sparse signal recovery problem. CS overcomes the difficulty of resolving coherent arrivals at an array by directly processing the sensor outputs without first estimating a sensor covariance matrix. CS is compared to the adaptive minimum-variance-distortionless-response (MVDR) spatial processor with spatial smoothing. Though spatial smoothing produces improved results by preprocessing the sensor array covariance matrix to decorrelate the coherent multipath components, it reduces the effective aperture of the array and hence reduces the resolution. An empirical study with a uniform linear array (ULA) demonstrates that CS outperforms MVDR beamformer with spatial smoothing in terms of spatial resolution and bias and variance of DOA and power estimates. Analysis of the shallow-water HF97 ocean acoustic experimental data shows that CS is able to recover the DOAs and power levels of the multipath signals with superior resolution compared to MVDR with spatial smoothing.