A comparison of parallel and sequential implementations of a multisensor multitarget tracking algorithm

Abstract

Parallel and sequential implementations of the multisensor joint probabilistic data association (MSJPDA) tracking algorithm are analyzed and compared. The sequential implementation is shown to be exponentially less computationally complex as the number of sensors increases. Simulation results suggest that the sequential method also yields better tracking performance on the average. This is primarily due to the fact that better filtered estimates are available after processing each sensor's data. Thus, while sequential and parallel implementations are equivalent in multisensor filtering when no data association routine is needed, the sequential implementation gives superior tracking performance when data association is required.