Multi-target data association method via T-S negative hesitation fuzzy model

To address the challenge of data association under uncertainty due to overlapping feature value ranges of multiple targets, we present a novel multi-target data association algorithm based on the Takagi-Sugeno (T-S) negative hesitant fuzzy model. This approach introduces a new multi-target T-S negative hesitant fuzzy model (MTSNH-FM), which is developed by integrating negative hesitant fuzzy sets with negative hesitant fuzzy numbers. For the parameter identification in the MTSNH-FM, we propose a new negative hesitant fuzzy index and apply the least squares method to adjust the model's parameters. Moreover, to tackle the multi-target data association problem within the MTSNH-FM framework, the input variables are determined by maximizing the negative hesitant fuzzy entropy. The results of simulations indicate that the proposed algorithm outperforms existing multi-target tracking techniques, particularly in cluttered environments.