scholarly journals Distributed Kalman-Consensus Filtering for Sparse Signal Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yisha Liu ◽  
Haiyang Yu ◽  
Jian Wang
Keyword(s):  
Kalman Filtering ◽  
Distributed Algorithm ◽  
Estimation Algorithm ◽  
Distributed Estimation ◽  
Sparse Signal ◽  
Sparse Signals ◽  
Signal Estimation ◽  
Selection Approach ◽  
Consensus Filter ◽  
Sparse Signal Estimation

A Kalman filtering-based distributed algorithm is proposed to deal with the sparse signal estimation problem. The pseudomeasurement-embedded Kalman filter is rebuilt in the information form, and an improved parameter selection approach is discussed. By introducing the pseudomeasurement technology into Kalman-consensus filter, a distributed estimation algorithm is developed to fuse the measurements from different nodes in the network, such that all filters can reach a consensus on the estimate of sparse signals. Some numerical examples are provided to demonstrate the effectiveness of the proposed approach.

scholarly journals A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter

2021 ◽  
Vol 11 (15) ◽  
pp. 7107
Author(s):  
Lulu Lv ◽  
Huifang Chen ◽  
Lei Xie ◽  
Kuang Wang
Keyword(s):  
Topology Optimization ◽  
Network Topology ◽  
Structural Parameters ◽  
Estimation Algorithm ◽  
Optimization Method ◽  
Distributed Estimation ◽  
Communication Overhead ◽  
Consensus Filter ◽  
Network Topology Optimization ◽  
Topology Optimization Method

Distributed estimation and tracking of interested objects over wireless sensor networks (WSNs) is a hot research topic. Since network topology possesses distinctive structural parameters and plays an important role for the performance of distributed estimation, we first formulate the communication overhead reduction problem in distributed estimation algorithms as the network topology optimization in this paper. The effect of structural parameters on the algebraic connectivity of a network is overviewed. Moreover, aiming to reduce the communication overhead in Kalman consensus filter (KCF)-based distributed estimation algorithm, we propose a network topology optimization method by properly deleting and adding communication links according to nodes’ local structural parameters information, in which the constraint on the communication range of two nodes is incorporated. Simulation results show that the proposed network topology optimization method can effectively improve the convergence rate of KCF algorithm and achieve a good trade-off between the estimate error and communication overhead.

scholarly journals c–LASSO and its dual for sparse signal estimation from array data

2017 ◽  
Vol 130 ◽  
pp. 204-216 ◽  
Author(s):  
Christoph F. Mecklenbräuker ◽  
Peter Gerstoft ◽  
Erich Zöchmann
Keyword(s):  
Sparse Signal ◽  
Signal Estimation ◽  
Array Data ◽  
Sparse Signal Estimation
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