Fuzzy logic association: performance, implementation issues, and automated resource allocation
James F. Smith · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
A recursive multisensor association algorithm has been developed based on fuzzy logic. It associates data from the same target for multiple sensor types. The algorithm provides an estimate of the number of targets present and reduced noise estimates of the quantities being measured. Uncertain information from many sources including other algorithms can be easily incorporated. A comparison of the algorithm to a more conventional Bayesian association algorithm is provided. The algorithm is applied to a multitarget environment for simulated data. The data from both the ESM and radar systems is noisy and the ESM data is intermittent. The radar data has probability of detection less than unity. The effects on parameter estimation, determination of the number of targets, and multisensor data association is examined for the case of a large number of targets closely spaced in the RF-PRI plane. When a sliding window is introduced to minimize memory and CPU requirements the algorithm is shown to lose little in performance, while gaining significantly in speed. The algorithm's CPU usage, computational complexity, and real-time implementation requirements are examined. Finally, the algorithm will be considered as an association algorithm for a multifunction antenna that makes use of fuzzy logic for resource allocation.