Forecasting probability of target presence for ping control in multistatic sonar networks using detection and tracking models

This paper describes the forecasting of probability of target presence in a search area (also referred to as the PT map) considering both detection and non-detection conditions. Tracking results are also incorporated to obtain a more accurate PT map under the detection condition. The probability of target presence is a suitable metric for real-time ping control for a submarine search mission, whose objective is to quickly identify and localize as many targets as possible within the search area. Existing formulations of the probability of target presence metric for ping control include an open-loop approach in which measurements are ignored or a semi-adaptive approach in which measurements are considered but without the true/false target investigation. Since false contacts are inevitable in practical applications and the true/false target investigation of the contacts is not immediate, tracking results must be considered in the PT map generation to obtain an accurate assessment of the present and projected operational pictures. We develop an approach to obtain the current and forecasted PT maps by incorporating a measurement model, a sonar performance model, Bayes theorem and a centralized Kalman-Filter based tracker. The PT map is composed of two portions: the portion which contains detected target probability and the portion which contains missed target probability. Each portion of the PT map is updated and propagated separately. The forecasted PT map at the next ping time is obtained by combining the two propagated PT maps. It will be demonstrated by simulations that the combined forecasted PT map represents an accurate multistatic operational picture and can be used with a sonar performance model to obtain a field metric for ping control optimization for the area search mission.