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Fuzzy logic fusion capabilities for efficient implementation of data association

In this paper, we propose a data association algorithm employing fuzzy logic based on multisensor and multi-feature target information to solve the uncertainty of the data received from sensor measurements in a high noise environment. The learning method to train the fuzzy data association system with full-fuzzy model based on a steepest descent gradient is analyzed. The improvement in primary sensor data fusion on data association is analyzed. The innovation tries to gain improvement in data association performance by fusing many features of targets while at the same time not increasing the computational structure of the tracking filter. The theoretical analysis and example demonstrate the feasibility of efficient data fusion of different forms using the fuzzy logic system for data association in multisensor multitarget tracking.