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Bistatic sonar or multistatic sonar system can collect more scattering information of targets than a monostatic sonar system. In this paper, sparse learning via iterative minimization method (SLIM) is introduced to distinguish wave components for time-domain (TD) back-propagation (BP) inverse scattering imaging improvement. Unlike the prevailing high central frequency (>100 kHz) and wideband imaging sonar systems, a relatively low-frequency band (1-10 kHz) is considered here. Due to the low sidelobe output of SLIM, the investigated object's surface in TD-BP image is much clearer in an ideal two-dimensional free field case. Furthermore, when the environmental information is known, this sparse reconstruction-based channel deconvolution method can be implemented to recognize, categorize the main propagating paths and then rectify their time of arrivals. Compared with the phase conjugation-based channel deconvolution method, the proposed approach's results have fewer sidelobes and higher signal-to-background ratio in the simulation.

Sparse Reconstruction-Based Inverse Scattering Imaging in a Shallow Water Environment