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. Two non-simulation techniques for comparison of multisensor probabilistic data association filters are developed and are used to compare tracking performance of the sequential and the parallel implementation of the algorithm. The non-simulation techniques are shown to accurately predict performance trends observed in simulations, that is the sequential implementation gives better tracking performance in terms of RMS position error and track lifetime. For the sequential implementation the question of processing order for different sensors is also briefly addressed. We also show that the sequential implementation is exponentially less computationally complex as the number of sensors increases. 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.