Agile Target Tracking Based on Greedy Information Gain

Abstract

The information acquisition capability of remote sensing nodes is limited by the scarcity of sensing, processing, communications, and power resources. In this work, a resource allocation method is proposed with an application to target tracking. The method predicts the locations of moving sensing nodes that provide improved information acquisition and tracking performance. Information acquisition is quantified by the expected information gain for each sensing node action. A greedy strategy is applied to select actions that provide the highest information gain while reducing computational complexity. The improvement in tracking performance by the proposed method is demonstrated through a simulation-based experiment. The simulation scenario includes tracking a point target with measurements from a stationary network of sensing nodes and coordinated measurement acquisition from moving nodes using the proposed greedy information gain method.