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We present a novel Rao-Blackwellized multiple particle filtering method for inference of correlated latent states observed via nonlinear functions. We adopt a state-space framework and model the dynamic correlated states using a mixing matrix, embedded in white Gaussian noise. The critical challenges in practice are the lack of knowledge about the mixing parameters and the possibly large dimensionality...
Particle filtering has been widely accepted as an important methodology for processing data represented by state-space models characterized by nonlinearities and/or non-Gaussianities. It is also well documented that particle filtering deteriorates quickly in performance when the dimension of the tracked state becomes large. This limits its application in many science/engineering problems. Previously...
In this paper an analysis is conducted regarding the likelihood function of an RSSI-based sensor measurement that is affected by a target of interest (TOI) and an interfering target source. The interferer's true location is unknown but is assumed to be Gaussian distributed with known parameters. This analysis is motivated by its potential application within a multi-agent distributed tracking system,...
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