Multi-target tracking systems typically provide sets of estimated target states as their output. It is challenging to be able to integrate these outputs as inputs to other tracking systems to gain a better picture of the area under surveillance since they do not conform to the standard observation model. Moreover, in cyclic distributed systems, there may be common information between state estimates that would mean that fused estimates may become overconfident and corrupt the system. In this paper we develop a Bayesian multi-target estimator based on the covariance intersection algorithm for multi-target track-to-track data fusion. The approach is integrated into a multitarget tracking algorithm and demonstrated in simulations. The approach is able to account for missed tracks and false tracks produced by another tracking system.

A lower bound on the Kullback-Leibler divergence known as Kullback’s inequality can be determined with the Legendre transform of the cumulant generating function. The Cramer Rao bound can be derived from Kullback’s inequality as the inverse of the second order term in a Taylor expansion. Analogous forms for Kullback’s inequality and the Cram er Rao bound for point processes were recently derived using functional methods from quantum field theory. This article develops Kullback’s inequality and the Cramer Rao bound for point process parametrisations as performance bounds for models used in multi-object filtering.