Parallel Distributed Architecture of Linear Kalman Filter for Non-stationary MIMO Communication Systems

Abstract

This paper provides the parallel distributed Kalman filter (PDKF) with non-aligned time indexes, which uses four processing nodes to run the linear Kalman filter in parallel. To provide outputs that are in sync with the time indexes that are now in effect, each processing node aggregates prior time indexes utilizing non-aligned time indexes. This method increases system efficiency by enabling simultaneous data sharing among all nodes and parallel processing with earlier time indexes. Moreover, the consistent acknowledgment of each block's processing time by all nodes ensures smooth functioning. With the help of the suggested PDKF, computing-intensive processes can be carried out in a distributed fashion, improving computation efficiency without sacrificing estimation accuracy. Simulation results show that while achieving nearly equivalent convergence performance to the sequentially operated Kalman filter, the proposed PDKF architecture outperforms both the sequential recursive least squares (RLS) and parallel distributed adaptive signal processing RLS algorithms. This study highlights the algorithm's strong overall performance through a thorough discussion, revealing its capacity to lower computing costs without sacrificing estimation accuracy inside the network. The book delves deeper into the algorithm's overall performance, emphasizing its effectiveness beyond simple estimating accuracy.