Comparison of probabilistic least squares and probabilistic multi-hypothesis tracking algorithms for multi-sensor tracking

A key element for successful tracking is knowing from which target each measurement originates. These measurement-to-target associations are generally unavailable, and the tracking problem becomes one of estimating both the assignments and the target states. We present the probabilistic least squares tracking (msPLST) algorithm for estimating the measurement-to-target assignments and the track trajectories of multiple targets, using measurements from multiple sensors. This is a different approach to that used in probabilistic multi-hypothesis tracking (PMHT), although both algorithms employ the concept of an extended observer containing both the target states and the measurement-to-target assignments. A comparison of both algorithms is made, and their performance is evaluated using simulated data.