Decentralized structures for parallel Kalman filtering

Abstract

Various multisensor network scenarios with signal processing tasks that are amenable to multiprocessor implementation are described. The natural origins of such multitasking are emphasized, and novel parallel structures for state estimation using the Kalman filter are proposed that extend existing results in several directions. In particular, hierarchical network structures are developed that have the property that the optimal global estimate based on all the available information can be reconstructed from estimates computed by local processor nodes solely on the basis of their own local information and transmitted to a central processor. The algorithms potentially yield an approximately linear speedup rate, are reasonably failure-resistant, and are optimized with respect to communication bandwidth and memory requirements at the various processors.