Distributed Robust PHD Filter for Multitarget Tracking Under Uncertainties

This paper addresses the issue of multi-target tracking in a distributed sensor network, where sensors have different fields of view (FoVs) and the scenario is characterized by multiple uncertainties on detection probability, clutter rate and noise covariances. First, we design a robust PHD (RPHD) filter, based on a target augmented state and a clutter feature state, to jointly estimate the target cardinality and kinematic states as well as multiple unknown parameters (noise covariances, detection probability and clutter rate). Gaussian Inverse Gamma Inverse Wishart Mixture (GIGIWM) and inverse Gamma mixture representations are used for the intensities of target augmented state and clutter state, respectively, and the estimation of all the unknown variables is achieved by means of variational Bayesian inference incorporating constrained distributions. Secondly, for a distributed sensor network, we propose a distributed fusion algorithm that extends the weighted arithmetic average (WAA) strategy to fuse information within overlapping and non-overlapping regions and to deal with local posteriors containing both global and local variables. Lastly, the local information confidence of each sensor posterior is incorporated into the fusion weight design to enhance the effective utilization of information in fusion. Numerical simulation experiments are performed to demonstrate the superior performance and strong robustness of the presented filter and distributed fusion algorithm.