Protocol-Based Particle Filtering and Divergence Estimation

Network protocols are applied to reduce the amount of data which are transmitted simultaneously over the network. In this article, round-robin protocol (RRP) and try-once-discard protocol (TODP) are studied for state estimation over network where information of different sensor nodes are fused. Using these protocols, at each time sample, only one sensor node can send its information over the network and consequently the required bandwidth is significantly reduced. Particle filters (PF) are able to estimate states of generally any nonlinear and non-Gaussian systems. Therefore, in this article, the development of particle filtering in networked systems under RRP and TODP protocols is studied. Toward this goal, two sequential importance sampling resampling algorithms under RRP and TODP are proposed where their corresponding marginal posterior pdfs are approximated by sequentially computations of weights. For the case of RRP, likelihoods of previous measurements are included in the weight computations while for TODP case, only the latest likelihood appears. Moreover, the approximated marginal posterior pdfs under RRP and TODP are compared with the normal posterior pdf using Kullback-Leibler divergence measure. This measure computes the difference between two probability distributions. Finally, the efficiency of the proposed method is demonstrated for a networked interconnected four-tank system.