Tracking the Tracker from its Passive Sonar ML-PDA Estimates

TL;DR

This paper investigates how to identify a passive sonar platform's motion from target estimation data, providing observability analysis and simulations for a scenario relevant to security and surveillance.

Contribution

It introduces a method to infer the observer's motion from ML-PDA target estimates and analyzes the observability of this inverse problem.

Findings

Identifiability of platform motion from target estimates established.

Simulation results demonstrate the feasibility of the inverse estimation.

Observability conditions are characterized for the two-leg motion model.

Abstract

Target motion analysis with wideband passive sonar has received much attention. Maximum likelihood probabilistic data-association (ML-PDA) represents an asymptotically efficient estimator for deterministic target motion, and is especially well-suited for low-observable targets; the results presented here apply to situations with higher signal to noise ratio as well, including of course the situation of a deterministic target observed via clean measurements without false alarms or missed detections. Here we study the inverse problem, namely, how to identify the observing platform (following a two-leg motion model) from the results of the target estimation process, i.e. the estimated target state and the Fisher information matrix, quantities we assume an eavesdropper might intercept. We tackle the problem and we present observability properties, with supporting simulation results.