Tracking Time-Varying Multipath Channels forActive Sonar Applications

TL;DR

This paper introduces a novel framework for directly learning and tracking time-varying multipath channels in raw sonar measurement data, improving detection reliability in dynamic shallow-water environments.

Contribution

It develops a state-space model with an EKF for channel tracking directly in raw data, bypassing traditional computationally intensive transformations, and integrates this into target detection.

Findings

Model better captures sea-surface fluctuation effects

Enhanced detection reliability in dynamic environments

Outperforms traditional background learning methods

Abstract

Reliable detection and tracking in active sonar require accurate and efficient learning of the acoustic multipath background environment. Conventionally, background learning is performed after transforming measurements into the range-Doppler domain, a step that is computationally expensive and can obscure phase-coherent structure useful for monitoring and tracking. This paper proposes a framework for learning and tracking the multipath background directly in the raw measurement domain. Starting from a wideband Doppler linearization of the impulse response of a time-varying multipath channel, a state-space model with a heteroscedastic measurement equation is derived. This model enables channel tracking using an extended Kalman filter (EKF), and unknown model parameters are learned from the marginalized likelihood. The statistical adequacy of the proposed models is assessed via a p-value significance test. Finally, this paper integrates the learned channel model into a sequential likelihood-ratio test for target detection. BELLHOP-based simulations show that the proposed model better captures channel dynamics induced by sea-surface fluctuations and transmitter and receiver drift, yielding more reliable detection in time-varying shallow-water environments