Abstract
We introduce in this paper the fully distributed, random exchange diffusion particle filter (ReDif-PF) to track a moving emitter using multiple received signal strength (RSS) sensors. We consider scenarios with both known and unknown sensor model parameters. In the unknown parameter case, a Rao-Blackwellized (RB) version of the random exchange diffusion particle filter, referred to as the RB ReDif-PF, is introduced. In a simulated scenario with a partially connected network, the proposed ReDif-PF outperformed a PF tracker that assimilates local neighboring measurements only and also outperformed a linearized random exchange distributed extended Kalman filter (ReDif-EKF). Furthermore, the novel ReDif-PF matched the tracking error performance of alternative suboptimal distributed PFs based respectively on iterative Markov chain move steps and selective average gossiping with an inter-node communication cost that is roughly two orders of magnitude lower than the corresponding cost for the Markov chain and selective gossip filters. Compared to a broadcast-based filter which exactly mimics the optimal centralized tracker or its equivalent (exact) consensus-based implementations, ReDif-PF showed a degradation in steady-state error performance. However, compared to the optimal consensus-based trackers, ReDif-PF is better suited for real-time applications since it does not require iterative inter-node communication between measurement arrivals.
Highlights
In several engineering applications, e.g., target tracking or fault detection, multiple agents [1] that are physically dispersed over remote nodes on a network cooperate to execute a global task, e.g., estimating a hidden signal or parameter, without relying on a global data fusion center
In situations where the state dynamic model or the sensor observation models are nonlinear, the posterior distribution of the states conditioned on the network measurements becomes non-Gaussian and, the linear minimum mean square error (LMMSE) estimate of the states provided, e.g., by an extended Kalman filter (EKF) may differ from the true minimum mean square error (MMSE) estimate given by the expected value of the state vector conditioned on the measurements
In a previous conference paper [17], we introduced the random exchange diffusion particle filter (ReDif-PF), which generalizes and extends the methodology in [2] to a PF framework by basically using random information dissemination to build at each network node different Monte Carlo representations of the posterior distribution of the states conditioned on random sets of measurements coming from the entire network
Summary
E.g., target tracking or fault detection, multiple agents [1] that are physically dispersed over remote nodes on a network cooperate to execute a global task, e.g., estimating a hidden signal or parameter, without relying on a global data fusion center. Assuming conditional independence of the different sensor measurements given the state vector, a distributed particle filter (PF) normally requires the computation of a product of likelihood functions that depend on local data only [8]. In a previous conference paper [17], we introduced the random exchange diffusion particle filter (ReDif-PF), which generalizes and extends the methodology in [2] to a PF framework by basically using random information dissemination to build at each network node different Monte Carlo representations of the posterior distribution of the states conditioned on random sets of measurements coming from the entire network.
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