Particle Filtering-Based Target Tracking in Binary Sensor Networks Using Adaptive Thresholds

Target tracking in wireless sensor networks with constrained resources is a challenging problem. In this paper we consider scenarios where sensors sense an object of interest and process the received measurements using adaptive thresholds to obtain quantized data in the form of two levels. The data are quantized to address resource constraints in sensor networks. The processed data are then sent to a fusion center which resolves the tracking problem by means of a particle filter which can handle non-linearities in the state model. The performance of various strategies for threshold adaptation is studied by computer simulations and the results reveal that improvement is obtained over scenarios with fixed thresholds.