Incorporating Performance Prediction Uncertainty into Detection and Tracking 21

the authors have developed methods to characterize and quantify uncertainty in acoustic environmental predictions. We have developed methods for reflecting this uncertainty in tactical systems such as senor performance prediction, search plan recommendation, and track and detect systems that rely on environmental inputs. Uncertainty in performance prediction can result from model error and uncertainty in environmental information such as the bottom composition, sound speed profile, and background internal waves. In this paper, we characterize and quantify uncertainty in environmental predictions for the components of the sonar equation for multistatic active detection, and incorporate this uncertainty into a Bayesian track-before-detect system called the Likelihood Ratio Tracker (LRT). We present an example that applies LRT to multistatic active detection and tracking. For this example, we show that by incorporating environmental uncertainty into the LRT state space, we can make use of performance prediction while maintaining robustness to prediction errors.