Distributed Kalman-Consensus Filtering for Sparse Signal Estimation

Abstract EN

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.