Abstract
Active sonar signal matching is a critical technique for measuring inter-signal similarity and enhancing target detection and classification performance. However, in complex underwater environments, noise, reverberation, and prolonged signal durations often degrade matching accuracy and computational efficiency. To address these challenges, this paper proposes an Adaptive Extremum-aligned Boundary-constrained Dynamic Time Warping (AEB-DTW) algorithm, based on the classical Dynamic Time Warping (DTW) framework. The algorithm extracts significant extrema from signal envelopes to suppress noise and reverberation while capturing salient features. By integrating the position and amplitude of extrema, an adaptive weighted matching strategy is introduced to enhance feature discrimination. In addition, spline fitting is applied to the residuals of the extremum matching path to dynamically generate upper and lower boundary constraints, thus restricting DTW computation to a meaningful region and achieving a balance between accuracy and efficiency. Experiments using lake-trial active sonar data under signal-to-reverberation ratios (SRRs) from 0 dB to 30 dB show that AEB-DTW outperforms ED, DTW, and its variants in matching accuracy, robustness, and angular resolution, while significantly improving computational efficiency, particularly for long-duration signals.
