Radar detection improvement by integration of multi-object tracking

This paper presents a new and simple approach to the problem of multiple sensor data fusion. We introduce an efficient algorithm that can fuse multiple sensor measurements to track an arbitrary number of objects in a cluttered environment. The algorithm combines conventional Kalman filtering techniques with probabilistic data association methods. A Gauss Markov process model is assumed to handle sensor outputs at various sampling frequencies and random nonequidistant time intervals. We applied the algorithm to post-process the digital range returns of radar sensors to improve their quality. Since the static noise returns have near-zero velocity, the algorithm associates a certain track with each digital return, and estimates the track velocity, thereby allowing for removal of false returns originating from static pattern noise.