Image tracking using a scale function-based nonlinear estimation algorithm

A refined version of a nonlinear estimation algorithm for tracking extended targets using imaging array data is presented. The algorithm is applied to a situation in which there is no closed-form functional representation for the image of the target. Based on the reduced sufficient statistic method of R Kulhavy, the algorithm recursively propagates, in a Bayes-closed sense, a set of sufficient statistics which approximate the true posterior density of the target parameter vector. The approximation is based on minimizing the Kullback-Leibler distance between the true posterior density and the approximating density. In previous work this density was a Gaussian mixture, while here scale functions are used to approximate the posterior density from which an approximate minimum variance estimate can be calculated. As the tracking progresses the posterior density is estimated on an increasingly finer scale. In order to reduce the number of scale functions, however, a pruning process is necessary. In this way, the number of scale functions approximating the density increases in areas for which the true density is significant while scale functions which approximate the density over regions where it is insignificant are ignored. Results are presented for simulations carried out in which the algorithm is applied to tracking an aircraft based on a sequence of synthetic images.