Consensus-based Distributed Particle Filtering With Distributed Proposal Adaptation

We develop a distributed particle filter for sequential estimation of a global state in a decentralized wireless sensor network. A global state estimate that takes into account the measurements of all sensors is computed in a distributed manner, using only local calculations at the individual sensors and local communication between neighboring sensors. The paper presents two main contributions. First, the likelihood consensus scheme for distributed calculation of the joint likelihood function (used by the local particle filters) is generalized to arbitrary local likelihood functions. This generalization overcomes the restriction to exponential-family likelihood functions that limited the applicability of the original likelihood consensus (Hlinka et al., “Likelihood consensus and its application to distributed particle filtering,” IEEE Trans. Signal Process., vol. 60, pp. 4334-4349, Aug. 2012). The second contribution is a consensus-based distributed method for adapting the proposal densities used by the local particle filters. This adaptation takes into account the measurements of all sensors, and it can yield a significant performance improvement or, alternatively, a significant reduction of the number of particles required for a given level of accuracy. The performance of the proposed distributed particle filter is demonstrated for a target tracking problem.