1Tracking the Tracker from its Passive Sonar ML-PDA Estimates

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. Index Terms Eavesdropper, Fisher information matrix, ML-PDA, nonlinear identification, platform localization, stealthy platform, wideband passive sonar. P. K. Willett was supported by ONR under contracts N00014-09-10613 and N10014-10-10412. Y. Bar-Shalom was supporte