Algorithms for a Class of Distributed Architecture Tracking

Multi-sensor target tracking has traditionally been performed using a single processor to monitor several sensors (centralized fusion), but this method is demanding of both computational power and communication band-width. Distributed sensor fusion is a method of addressing these limitations. However, the distributed sensor fusion problem is more complex due to the correlation of separate track estimates. A method known as measurement reconstruction has recently been shown to address this problem in a speciic architecture. This paper extends the measurement reconstruction approach to a more generalized architecture using two new algorithms. Computational and communication requirements are compared with centralized sensor fusion, and Monte Carlo simulation studies are used to compare the performance of these algorithms.