Bayesian approach to target tracking in the presence of glint
Neil J. Gordon, Angela Whitby · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
When tracking targets with radar, changes in target aspect with respect to the observer can cause the apparent center of radar reflections to wander significantly. The resulting noisy angle errors are called target glint. Glint may severely affect the tracking accuracy, particularly when tracking large targets at short ranges (such as might occur in the final homing phase of a missile engagement). The effect of glint is to produce heavy-tailed, time correlated non-Gaussian disturbances on the observations. It is well known that the performance of the Kalman filter degrades severely in the presence of such disturbances. In this paper we propose a random sample based implementation of a Bayesian recursive filter. This filter is based on the Metropolis-Hastings algorithm and the Gaussian sum approach. The key advantage of the filter is that any nonlinear/non-Gaussian system and/or measurement models can be routinely implemented. Tracking performance of the filter is demonstrated in the presence of glint.