A Rao-Blackwellized Random Exchange Diffusion Particle Filter for distributed emitter tracking

We introduce in this paper the fully distributed, Rao-Blackwellized Random Exchange Diffusion Particle Filter (RB ReDif-PF) to track a moving emitter using multiple received-signal-strength (RSS) sensors with unknown noise variances. In a simulated scenario with a partially connected network, the proposed RB ReDif-PF outperformed a suboptimal tracker that assimilates local neighboring measurements only. Compared to a broadcast-based filter which exactly mimics the optimal centralized tracker, ReDif-PF showed a degradation in steady-state error performance. However, compared to alternative fully distributed consensus-based trackers in the literature, ReDif-PF is better suited for real-time applications since it does not require iterative inter-node communication between measurements arrivals.