Active Signal Processing Enhancements for Classification of Low Signal-to-Noise Ratio (SNR) Sonar Signals in Doppler Clutter

The acoustic environment encountered in pulsed active sonar provides some of the most formidable challenges seen in modern signal processing. The confluence of strong bottom clutter, ownship Doppler spreading, and discrete mutual interference results in clutter leakage and sidelobes across beam and Doppler spaces. Platform motion causes clutter returns to be shifted in Doppler, leading to elevated noise shoulder near the zero Doppler ridge, impacting the detection and classification of low Doppler contacts. Current adaptive signal processing algorithms suffer from sample support limitations due to the rapidly changing active environment. In order to prevent signal suppression and maintain robust performance, these algorithms must be run conservatively, limiting their ability to null clutter and interference. In this proposal, Adaptive Methods outlines a new and innovative approach to active sonar signal processing. We build upon our experience in rapid adaptation to provide the next generation of active signal processing. Our approach, termed the Robust Active Signal Processing Adaptive Processor (RASAP) enables aggressive robust adaptation in sample starved environments. This approach mitigates the corruptive effects of interference and leakage while minimizing impact on downstream processing, leading to improved detection, tracking, and classification performance.