Low observable target motion analysis using amplitude information

In conventional passive and active sonar systems, target amplitude information (AI) at the output of the signal processor is used only to declare detections and provide measurements. The authors show that the AI can be used in passive sonar systems, with or without frequency measurements, in the estimation process itself to enhance the performance in the presence of clutter, i.e., in a low SNR situation, when the target-originated measurements cannot be identified with certainty. A probabilistic data association based maximum likelihood estimator for target motion analysis that uses amplitude information is derived. A track formation algorithm and the Cramer-Rao lower bound in the presence of false measurements, which is met by the estimator even under low SNR conditions, are also given. Results demonstrate improved accuracy and superior global convergence when compared to the estimator without amplitude information