Enhancing Stochastic Matched Filter Performance Through Efficient Noise Estimation

This paper introduces a framework to enhance the performance of the Stochastic Matched Filter (SMF) through an efficient noise estimation process. Unlike traditional approaches that rely on fixed or heuristic noise assumptions, our method models the noise characteristics directly from the data, utilizing the inherent properties of the environment for more accurate filtering. By integrating data-driven noise estimation, the proposed approach optimizes the SMF for improved robustness in low signal-to-noise ratio (SNR) conditions. Furthermore, the method achieves a good pulse compression, enabling precise signal localization and enhanced resolution. To demonstrate its practical relevance, we apply the proposed technique to the challenging task of Antarctic Blue Whale (ABW) call detection, showing its effectiveness in minimizing false detections while maintaining high accuracy. This highlights its utility in bioacoustic monitoring and other applications in dynamic acoustic environments.