Past attempts to use acoustic sensor performance predictions, typically probability of detection as a function of range, in naval undersea warfare tactical decision aids such as trackers and mission planning tools have met with great difficulty. These efforts have been hampered by the uncertainty often inherent in these predictions. In some cases, the use of incorrect predictions produced results or recommendations that were worse than not using the predictions at all. The goal of the work reported in this paper is to develop a Track-Before-Detect (TBD) system that accounts for this uncertainty and has the following features: (1) It produces results are at least as good as those obtained with no performance prediction information. (2) It produces a significant improvement in performance in some situations. In this paper we describe an extension of a TBD system called the Likelihood Ratio Tracker (LRT) that incorporates uncertainty in performance prediction. We have run LRT on data that simulate a multistatic active sonar detection system similar to one in use by the Navy. In these simulated cases, we have shown that using performance prediction improves LRT tracking and detection performance even in the presence of large prediction errors.
This paper provides a brief history of some operational particle filters that were used by the U. S. Coast Guard and U. S. Navy. Starting in 1974 the Coast Guard system provided Search and Rescue Planning advice for objects lost at sea. The Navy systems were used to plan searches for Soviet submarines in the Atlantic, Pacific, and Mediterranean starting in 1972.
The systems operated in a sequential, Bayesian manner. A prior distribution for the target’s location and movement was produced using both objective and subjective information. Based on this distribution, the search assets available, and their detection characteristics, a near-optimal search was planned. Typically, this involved visual searches by Coast Guard aircraft and sonobuoy searches by Navy antisubmarine warfare patrol aircraft. The searches were executed, and the feedback, both detections and lack of detections, was fed into a particle filter to produce the posterior distribution of the target’s location. This distribution was used as the prior for the next iteration of planning and search.
This paper develops methods for associating two sets of sensor tracks in the presence of missing tracks and translation bias. Key results include 1) extension of the maximum A Posteriori probability method of matching tracks to use feature information as well as kinematic information; 2) translation bias removal techniques that are computationally tractable for large numbers of tracks, and effective in the presence of missing tracks. These methods were evaluated by Monte Carlo simulation. The experimental results indicate that the maximum A Posteriori probability method with its adaptive threshold achieves close to its best performance for matching tracks without an additional threshold adjustment.