Title :
Distributed Kalman Filtering with quantized sensing state
Author :
Di Li ; Kar, Soummya ; Shuguang Cui
Author_Institution :
Dept. of ECE, Texas A&M Univ., College Station, TX, USA
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.
Keywords :
Kalman filters; error statistics; quantisation (signal); wireless sensor networks; QGIKF algorithm; RDS; error variance sequence evolution; estimation error variance sequence; information loss; inter-sensor communications; interacting particle; quantization; quantized gossip-based interactive Kalman filtering algorithm; quantized states; random dynamical system; unique invariant measure; wireless sensor network; Artificial neural networks; Kalman filters; Noise; Distributed signal processing; Kalman filter; gossip; quantization;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
DOI :
10.1109/ICASSP.2015.7178730