Name
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. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | Distributed signal processing, Kalman filter, quantization, gossip |
Field | DocType | ISSN |
Topology,Weak convergence,Extended Kalman filter,Mathematical optimization,Fast Kalman filter,Control theory,Computer science,Kalman filter,Random dynamical system,Invariant extended Kalman filter,Quantization (signal processing),Invariant measure | Conference | 1520-6149 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Li | 1 | 27 | 3.40 |
| Soummya Kar | 2 | 1874 | 115.60 |
| Shuguang Cui | 3 | 5382 | 368.45 |