Multidimensional SME filter for multitarget tracking

Kamen et al. have developed the symmetric measurement equation (SME) filter as an alternative to multi-target trackers based on data association. This paper presents an improved multi-dimensional SME tracking algorithm which agrees with Kamen's for one-dimensional scenarios and avoids the ghost target problem in higher dimensions. In addition, we provide a more efficient method for computing the noise covariance matrix of the SME coefficients. This was the major computational bottleneck of earlier SME implementation, and we have reduced its complexity from at least 2N/2 operations to at most D4N5, where N is the number of targets and D the number of dimensions. Computer simulations illustrate a failure mode that the new algorithm avoids, and gives a sample comparison to a standard data- association algorithm, global nearest neighbor.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.