A New JPDAF Based on Fuzzy Logic Inference

The problem of data association is very important to multiple target tracking in informantion fusion field. To the data association problem of the multiple target tracking in clutter environment, a new joint probabilistic data association filter(JPDAF) method based on fuzzy logic inference was proposed. Firstly,the characteristics of the measurement of multiple targets were analyzed. Secondly, the rules of the proposed algorithm are expressed in terms of two input variables and one output variables. The input variables are de?ned in terms of the prediction error and change of error. At the same time, in order to adapt computing the asscoaition probabilities of targets and measurements, the fuzzy inference rules were designed according to the advices of experts.Thirdly, the association probabilities of the joint probabilistic data association filter is replaced by the adaptive membership degrees that is computed through fuzzy inference system.Finally, the simulation results show that the performance of target tracking of the proposed algorithm is higher than the JPDAF and Fitzgerald’s method. the runtime statistics of each algorithm is show that the proposed algorithm is faster than the JPDAF ,and inferior to the Fitzgerald’s method