Mean-field theory and multitarget tracking
Keith D. Kastella · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
This paper presents a novel Kalman filter for track maintenance in multitarget tracking using thresholded sensor data at high target/clutter densities and low detection levels. The filter is robust against tracking errors induced by crossing tracks, clutter and missed detections and the computational complexity of the filter scales well with problem size. There are two key features that differentiate this approach from earlier work. First, in order to enhance tracking of close tracks, the filter explicitly models the error correlations that occur between such target pairs. These error correlations arise due to the measurement to track association ambiguity present when target separations are comparable to the measurement errors in the sensors. Second, in order to reduce the computational load, the filter exploits techniques from statistical field theory to simplify the combinatorial complexity of measurement to track association. This is accomplished by developing a mean-field approximation to the summation over all associations.