A Robust Multi-Sensor PHD Filter Based on Multi-Sensor Measurement Clustering

This letter presents a novel multi-sensor probability hypothesis density (PHD) filter for multi-target tracking by means of multiple or even massive sensors that are linked by a fusion center or by a peer-to-peer network. As a challenge, we find there is little known about the statistical properties of the sensors in terms of their measurement noise, clutter, target detection probability, and even potential cross-correlation. Our approach converts the collection of the measurements of different sensors to a set of proxy and homologous measurements. These synthetic measurements overcome the problems of false and missing data and of unknown statistics, and facilitate linear PHD updating that amounts to the standard PHD filtering with no false and missing data. Simulation has demonstrated the advantages and limitations of our approach in comparison with the cutting-edge multi-sensor/distributed PHD filters.