Multiple target data-association algorithm based on Takagi–Sugeno intuitionistic fuzzy model

Abstract

To solve the problem of data association in multiple target tracking in a densely cluttered environment, a multiple target data association algorithm based on Takagi–Sugeno (T-S) intuitionistic fuzzy model is proposed. In the proposed algorithm, the new multiple target T-S intuitionistic fuzzy model (MTTS-IFM) is constructed by incorporating intuitionistic fuzzy sets and intuitionistic fuzzy numbers. To identify the premise parameters for MTTS-IFM, a new intuitionistic index is defined and a modified form of the intuitionistic fuzzy C-means clustering algorithm is proposed. Furthermore, to solve the multiple target data association for the MTTS-IFM approach, the input variables of MTTS-IFM are defined using the maximum intuitionistic fuzzy entropy, and a data association approach is proposed to deal with the uncertainty between targets and measurements. Finally, simulation results show that the performance of the proposed algorithm is better than that of algorithms designed for multiple target tracking in either a clutter-free or cluttered environment. Moreover, the real-time performance was clearly better than that of the joint probabilistic data association filter (JPDAF) and slightly better than that of maximum entropy fuzzy (MEF-JPDAF), Fitzgerald-JPDAF, and a T-S intuitionistic fuzzy model (TS-FM).