Collaborative emitter tracking using distributed sequential Monte Carlo methods - Stiven Schwanz Dias (2014)
We introduce in this Thesis several particle filter (PF) solutions to the problem of collaborative emitter tracking. In the studied scenario, multiple agents with sensing, processing and communication capabilities passively collect received-signal-strength (RSS) measurements of the same signal originating from a non-cooperative emitter and collaborate to estimate its hidden state. Assuming unknown sensor noise variances, we derive an exact decentralized implementation of the optimal centralized PF solution for this problem in a fully connected network. Next, assuming local internode communication only, we derive two fully distributed consensus-based solutions to the problem using respectively average consensus iterations and a novel ordered minimum consensus approach which allow us to reproduce the exact centralized solution in a finite number of consensus iterations. In the sequel, to reduce the communication cost, we derive a suboptimal tracker which employs suitable parametric approximations to summarize messages that are broadcast over the network. Moreover, to further reduce communication and processing requirements, we introduce a non-iterative tracker based on random information dissemination which is suited for online applications. We derive the proposed random exchange diffusion PF (ReDif-PF) assuming both that observation model parameters are perfectly known and that the emitter is always present. We extend then the ReDif-PF tracker to operate in scenarios with unknown sensor noise variances and propose the Rao-Blackwellized (RB) ReDif-PF. Finally, we introduce the random exchange diffusion Bernoulli filter (RndEx-BF) which enables the network of collaborative RSS sensors to jointly detect and track the emitter within the surveillance space.