Distributed Consensus $+$ Innovation Particle Filtering for Bearing/Range Tracking With Communication Constraints

A constrained sufficient statistic (CSS)-based distributed particle filter (CSS/DPF) implementation is proposed for nonlinear bearing-only and joint bearing/range tracking applications in sensor networks. The CSS/DPF runs localized particle filters at nodes constituting the sensor network and uses the resulting local sufficient statistics (LSS) to compute the global sufficient statistics (GSS) for the overall system. The CSS/DPF is, therefore, a two-step procedure: i) the means of the LSSs for the local filters are computed by running average consensus algorithms, which are then used to derive the corresponding GSSs, and ii) each node renews the local weights of the localized particle filters using the updated GSSs. The attractive feature of the CSS/DPF is the reduced number of consensus runs as compared with the state-of-art consensus-based DPF implementations. To further reduce the consensus overhead, we couple the CSS/DPF with the distributed unscented particle filter (DUPF), collectively referred to as the CSS/DUPF, which extends the linear consensus and innovation framework to nonlinear distributed estimation. Our Monte Carlo simulations show that the performance of the CSS/DUPF follows that of the centralized particle filter, even with a limited number of iterations per consensus run.