Improved JPDA for fast fault detection

Regarding faults as dynamic modes which observe through the multi-sensors, with probabilistic data association based on multi-sensor, we obtain the fast fault detection results according to the association probability and threshold values. Joint Probabilistic Data Association (JPDA) algorithm is one of the effective ways for single-sensor multi-target tracking. Based on the analysis of JPDA algorithm, we improve the JPDA algorithm: first, we propose an approximation method for constructing the confirmation matrix through removing the small probability events using the right threshold values, and then, we present the mathematical division of the confirmation matrix according to the intersection area of the association gate of fault targets to be tracked; lastly, we compute the association probability of fault targets through attenuating the value of the public measurement. The simulation results show preliminarily that our improved JPDA algorithm saves the computing time greatly, and effectively meet the requirements of fast and real-time fault detection.