Compressive sensing method to leverage prior information for submerged target echoes

摘要

Reducing data volume and improving signal-to-noise ratio (SNR) is of great importance for echoes from submerged targets, affected by serious marine environment noise. The echo from a target is made of its response to the incident wave with the superposition of highlights (sub-echoes from main constituents of the target). Each of these highlights can be seen as a block, and the echo therefore has a block-sparse feature. This paper proposes a compressive sensing method to leverage prior information (CSPI), in which knowledge of the incident wave and the block-sparse feature are leveraged into the dictionary structure and signal reconstruction. CSPI is illustrated with simulations and field measurements of backscattering for a 1:20 model of the Benchmark Target Strength Simulation Submarine. For simulated signals with different noise levels, CSPI can reconstruct an almost invisible signal (original SNR?0 dB), and improve SNR by up to 13 dB (for an original SNR of 4 dB) down to a still significant SNR of 7 dB (for an original SNR of 0 dB). For field measurements, CSPI can obtain the same SNR as the original signal using only 13% of the data, increasing the SNR to 15 dB using 30% data, and increasing with the compression ratio.