Particle filter for multipath time delay tracking from correlation functions in deep water.

Abstract

This paper presents a particle filtering-based approach for tracking multipath time delays from correlation function, such as autocorrelation, cross-correlation, and matched-filter output. The proposed approach exploits the continuous evolution with time of the correlations between multipath arrivals masked by the background noise to track time delays. The prominent feature of this approach is tracking the signal-related peaks (single points) instead of correlation pulses adopted in conventional approaches. To do so, the correlation function with only local peaks is introduced in the model of the measurement equation. This allows no assumption on the reference signal used to match the correlation pulse and no a priori knowledge of the covariance of the background noise. The time-evolving marginal posterior probability densities are also extracted by filtering to reveal the uncertainty of the time delays in every step of tracking. The approach is performed on both simulated data in reliable acoustic path propagation and experimental data collected during two deep water experiments; the results demonstrate significant advantages of the proposed method over a conventional state-space approach, the multiple hypothesis tracking, and a modified peak amplitude detection method.