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A High-Resolution Imaging Method for Multiple-Input Multiple-Output Sonar Based on Deterministic Compressed Sensing.

Ning Gao^1,2, Feng Xu¹, Juan Yang¹

¹Ocean Acoustic Technology Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

This summary is machine-generated.

This study introduces a Compressed Sensing (CS) method to improve Multiple-Input Multiple-Output (MIMO) sonar imaging. The technique suppresses noise, enabling clearer underwater target localization with lower sidelobes.

Keywords:

MIMO sonar compressed sensing high resolution matched filtering signal reconstruction

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Area of Science:

Underwater acoustics
Signal processing
Array signal processing

Background:

Conventional sonar and MIMO sonar systems face challenges in achieving high angular and range resolution.
MIMO sonar utilizes matched filtering for enhanced spatial resolution, but imperfect correlations lead to high sidelobes, hindering accurate target localization.
High sidelobe values in underwater imagery impede precise identification of targets.

Purpose of the Study:

To propose a Compressed Sensing (CS) method for reconstructing echo signals in MIMO sonar systems.
To suppress correlation noise arising from imperfect correlation characteristics in MIMO sonar.
To enhance spatial resolution and improve target localization accuracy in underwater imagery.

Main Methods:

A Compressed Sensing (CS) approach is employed, involving echo signal reconstruction to mitigate correlation noise between orthogonal waveforms.
A shifted dictionary matrix and a deterministic Discrete Fourier Transform (DFT) measurement matrix are utilized for signal processing.
Sparse recovery algorithms optimize signal reconstruction, followed by joint transmit-receive beamforming to generate a 2D sonar image.

Main Results:

The proposed CS method effectively suppresses correlation noise, leading to reduced sidelobe values in sonar imagery.
Numerical simulations and lake experiments demonstrate the method's capability to produce lower sidelobe sonar images.
The approach achieves improved performance under sub-Nyquist sampling rates compared to existing methods.

Conclusions:

The developed Compressed Sensing (CS) method offers a viable solution for enhancing spatial resolution and target localization in MIMO sonar systems.
This technique effectively addresses the challenge of high sidelobes caused by imperfect correlation characteristics.
The study validates the method's effectiveness through simulations and experimental data, highlighting its potential for advanced underwater acoustic imaging.