Distributed Bernoulli Filters for Joint Detection and Tracking in Sensor Networks
Stiven Schwanz Dias, Marcelo G. S. Bruno · IEEE Transactions on Signal and Information Processing over Networks · 2016
We introduce in this paper a novel, fully distributed diffusion Bernoulli filter based on random information dissemination over a partially connected sensor network. The proposed algorithm allows the network nodes to cooperatively perform joint multiframe detection and tracking of an emitter that randomly appears in and disappears from a surveillance region. In addition, we also introduce an alternative flooding-based algorithm that reproduces exactly, in a fully distributed fashion, the optimal centralized Bernoulli filter based on all network node measurements. We derive two alternative low bandwidth implementations of the proposed filters, using respectively a marginalized sequential Monte Carlo (SMC) method and a hybrid Gaussian mixture model (GMM)/SMM scheme. The algorithms were evaluated in a simulated scenario where network nodes directly assimilate raw received signal strength measurements, subject only to possible measurement censoring.