The CRLB for Tracking in Clutter with ML-PDA and ML-PMHT

In a realistic environment, where the target may be missed and false-alarms may be detected, it is prudent to capture such phenomena in the tracking algorithm. Two methods are considered: the maximum likelihood probabilistic data association (ML-PDA) algorithm and the maximum likelihood probabilistic multi-hypothesis tracker (ML-PMHT). In this paper we compare the ML-PDA and ML-PMHT based on their Cramer-Rao lower bounds (CRLBs), the key difference being the form of the scalar information reduction factor (IRF). The IRF is presented for ML-PMHT in a new form that makes it more computationally tractable and also easier to directly compare with that of the ML-PDA.