Distributed Kalman Filtering with quantized sensing state

Di Li; Soummya Kar; Shuguang Cui

ICASSP 2015accepted0 citations

Distributed Kalman Filtering with quantized sensing state

Abstract

This paper studies a Quantized Gossip-based Interactive Kalman Filtering (QGIKF) algorithm implemented in a wireless sensor network, where the sensors exchange their quantized states with neighbors via inter-sensor communications. We show that with the information loss due to quantization, the network can still achieve weak consensus, i.e., the estimation error variance sequence at a randomly selected sensor can converge weakly (in distribution) to a unique invariant measure. To prove the weak convergence, we first interpret the error variance sequence evolution as the interacting particle, then formulate the sequence as a Random Dynamical System (RDS), and finally prove that it is stochastically bounded.

BibTeX

@inproceedings{icassp2015_distributedkalma,
  title = {Distributed Kalman Filtering with quantized sensing state},
  author = {Di Li and Soummya Kar and Shuguang Cui},
  booktitle = {ICASSP 2015},
  year = {2015}
}