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

Jingning Jiang,Xiang Pan,T C Yang

doi:10.1109/access.2020.3025715

Jingning Jiang, Xiang Pan + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3025715

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Abstract

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.

Highlights

Active acoustic imaging is the technique to locate an object’s scattering strength distribution and characterize it
SPARSE RECONSTRUCTION BASED IMAGING METHOD By reviewing Fig.4 and Fig.7(b), it can be found that: (1) The output of matched filter (MF) has a wide main lobe, which may not well separate the reflection and creeping wave; (2) TD-BP using the output of MF produces lots of unwanted ripples and sidelobes
This paper focuses on the inverse scattering imaging problems in a bistatic active sonar imaging

Summary

INTRODUCTION

Active acoustic imaging is the technique to locate an object’s scattering strength distribution and characterize it. J. Jiang et al.: Sparse Reconstruction-Based Inverse Scattering Imaging the linear sampling method (LSM) [18] is applied in the underwater object imaging task with the partial frequency variation approach [19]. This sparse reconstruction-based channel deconvolution imaging is proposed to solve that problem. By reviewing Fig., it can be found that (4) uses the linearized approximation of ||rt − rs|| ≈ Rs + ninc · rt, and ||r − rt|| ≈ Rr − nsct · rt This indicates (4) is originated from the time domain delay-andsum expression:. This paper uses BP’s time-domain equivalence (5) to output the inverse scattering imaging results

SOUND SCATTERING AND INVERSE SCATTERING IMAGING IN A 2D FREE FIELD

SIMULATION IN 2D FREE FIELD SPACE

PHASE CONJUGATION-BASED CHANNEL DECONVOLUTION IMAGING METHOD

CONCLUSION