Abstract
This paper presents an underwater passive source localization method by forming an underdetermined linear inversion problem. The signal strength on a specified grid is evaluated using sparse reconstruction algorithms by exploiting the spatial sparsity of the source signals. Our strategy leads to a high ratio of measurements to sparsity (RMS), an increase in the peak sharpness with a low side lobe level, and minimization of the dimensionality of the problem due to the formulation of the system equation of the multiple snapshots based on the data correlation matrix. Furthermore, to reduce the computational burden, pre-locating with Bartlett is presented. Our proposed technique can perform close to Bartlet and white noise gain constraint processes in the single-source scenario, but it can give slightly better results while localizing multiple sources. It exhibits the respective characteristics of traditionally used Bartlett and white noise gain constraint methods, such as robustness to environmental/system mismatch and high resolution. Both the simulated and experimental data are processed to demonstrate the effectiveness of the method for underwater source localization.
Highlights
Matched-field processing (MFP) has been extensively studied for passive source localization [1].It works by scanning through spatial grids of potential targets while looking for the best matching of the observed data and the sound propagation physical model in an ocean waveguide associated with the scanning target position
We present a sparse-reconstruction-based MFP method for underwater passive source localization, which builds the measurement equation according to the data correlation matrix; in the body of the paper, we refer to the method as Compressive Sensing-Revised (CS-R)
The Restricted Isometry Property (RIP) for sparse recovery is related to the orthonormality of the columns of the A matrix
Summary
Matched-field processing (MFP) has been extensively studied for passive source localization [1]. We can exploit the spatial sparsity feature of the underwater source signals and develop a high-resolution MFP method based on sparse reconstruction algorithms, which have been attracting great interest due to their relationship with Compressive Sensing (CS) [9]. Because of utilizing the prior knowledge of sparsity, the advantages of the sparse reconstruction-based MFP method can include high resolution, low sidelobe level, robustness, and no need for a large number of snapshots. The Bartlett MFP [39] is the most robust method and is extensively used in papers on MFP, and it is an appropriate candidate for a baseline comparison We consider this process using single and multiple incoherent sources.
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