Using Symmetric State Transformations for Multi-Target Tracking

This paper is about the use of symmetric state transformations for multi-target tracking. First, a novel method for obtaining point estimates for multi-target states is proposed. The basic idea is to apply a symmetric state transformation to the original state in order to compute a minimum mean-square-error (MMSE) estimate in a transformed state. By this means, the known shortcomings of MMSE estimates for multi-target states can be avoided. Second, a new multi-target tracking method based on state transformations is suggested, which entirely performs the time and measurement update in a transformed space and thus, avoids the explicit calculation of data association hypotheses and removes the target identity from the estimation problem. The performance of the new approach is evaluated by means of tracking two crossing targets

Simultaneous Tracking and Shape Estimation of Extended Objects

This work is concerned with the simultaneous tracking and shape estimation of a mobile extended object based on noisy sensor measurements. Novel methods are developed for coping with the following two main challenges: i) The computational complexity due to the nonlinearity and high-dimensionality of the problem, and ii) the lack of statistical knowledge about possible measurement sources on the extended object