An Under-Sampled Line Array Element Signal Reconstruction Method Based on Compressed Sensing Theory

Abstract

The half-wavelength spacing arrangement of underwater uniform linear arrays has been widely used for better anti-interference performance and higher signal gain. However, practical challenges of small element spacing, numerous elements, high hardware costs, large data storage requirements, high processing complexity, and mutual coupling effects between elements, have hindered its widespread use. This paper proposes an under-sampled array signal reconstruction method based on the compressed sensing (CS) theory in the element domain. This method is not limited by the array configuration and constructs a deterministic measurement matrix that satisfies the restricted isometry property (RIP). based on the array configuration, to ensure reconstruction performance. The method uses a two-dimensional orthogonal matching pursuit (OMP) method for time-space joint reconstruction of under-sampled spatial signals. Our simulation and practical test data processing results demonstrate that this method can achieve high-precision reconstruction of under-sampled array element domain signals at low under-sampling rates and can reconstruct full array signals with minimal error. Even under low signal-to-noise ratio (SNR) conditions, offering a practical and efficient solution to the challenges of underwater acoustic array signal processing.