Posterior Cramér–Rao lower bounds for extended target tracking with PMBM conjugate recursion

Abstract

This letter considers the posterior Cramér–Rao lower bounds (PCRLB) problem for extended target tracking from a stack of measurement data that are modelled as random variables in the random finite sets framework. The scalars in the traditional PCRLB are converted into vectors based on random finite sets to derive a theoretical lower bound. In this way, the proposed method can be applied to the multi-target tracking problem and accommodates scenarios with targets of varying. Moreover, solving the data association problem from four parts caused by the conjugate update of the Poisson multi-Bernoulli mixture filter is considered. Simulation results are presented to verify the effectiveness of the derived PCRLB.