Shallow-water sparsity-cognizant source-location mapping

Abstract

Using passive sonar for underwater acoustic source localization in a shallow-water environment is challenging due to the complexities of underwater acoustic propagation. Matched-field processing (MFP) exploits both measured and model-predicted acoustic pressures to localize acoustic sources. However, the ambiguity surface obtained through MFP contains artifacts that limit its ability to reveal the location of the acoustic sources. This work introduces a robust scheme for shallow-water source localization that exploits the inherent sparse structure of the localization problem and the use of a model characterizing the acoustic propagation environment. To this end, the underwater acoustic source-localization problem is cast as a sparsity-inducing stochastic optimization problem that is robust to model mismatch. The resulting source-location map (SLM) yields reduced ambiguities and improved resolution, even at low signal-to-noise ratios, when compared to those obtained via classical MFP approaches. An iterative solver based on block-coordinate descent is developed whose computational complexity per iteration is linear with respect to the number of locations considered for the SLM. Numerical tests illustrate the performance of the algorithm.

Multiple and single snapshot compressive beamforming

“…In ocean acoustics, CS has found several applications in matched field processing 14,15 and in coherent passive fathometry for inferring sediment interfaces depths and their number 16 . Various wave propagation phenomena from a single source (refraction, diffraction, scattering, ducting, reflection) lead to multiple partially coherent arrivals received by the array.…”

Sparsity-driven passive tracking of underwater acoustic sources

“…. , F , in the regularizer are known to induce resilience to model mismatch into the estimateS(t) [8], [9]. From this vantage point, λ > 0 corresponds to the variance of a random perturbation affecting each replica, which depends on the mismatch between the true propagation environment and the model used to generate the replicas.…”

“…Note that both MFT and the proposed method fail to track the source after t = 65 min. due to the severe mismatch between the environment and the model used to generate the replicas (see [9]). …”

Grid-free compressive beamforming

“…Furthermore, in ocean acoustics, CS is shown to improve the performance of matched field processing, 8,9 which is a generalized beamforming method for localizing sources in complex environments (e.g., shallow water), and of coherent passive fathometry in inferring the number and depth of sediment layer interfaces. 10 One of the limitations of CS in DOA estimation is basis mismatch 11 which occurs when the sources do not coincide with the look directions due to inadequate discretization of the angular spectrum.…”