Real-time adaptation of decision thresholds in sensor networks for detection of moving targets

This paper addresses real-time decision-making associated with acoustic measurements for online surveillance of undersea targets moving over a deployed sensor network. The underlying algorithm is built upon the principles of symbolic dynamic filtering for feature extraction and formal language theory for decision-making, where the decision threshold for target detection is estimated based on time series data collected from an ensemble of passive sonar sensors that cover the anticipated tracks of moving targets. Adaptation of the decision thresholds to the real-time sensor data is optimal in the sense of weighted linear least squares. The algorithm has been validated on a simulated sensor-network test-bed with time series data from an ensemble of target tracks.