The Heading-angle aided Data Association Algorithm based on Fuzzy Logic

In order to improve the tracking accuracy and the data association accuracy without increasing the computational complexity, a new data association method with the heading angle aided is proposed for the multi-target tracking based on the fuzzy logic inference system in this paper. Firstly, the heading-angle and distance are analyzed to be the effective parameters for separating the different trajectories, the definition of heading-angle and the method of how to calculate the heading-angle have been given, the measurement heading-angle could be calculated using the radar measurement at the current moment and the updated state vector of the target at the previous moment, the state vector of the target including the measurement heading-angle is updated using the cubature Kalman filter (CKF), then the fuzzy logic inference system is used for data association of multi-target tracking. Simulation results show that the proposed method has better association accuracy than the Nearest Neighbour (NN) algorithm, and in guarantee of the association accuracy, the tracking accuracy and the operation efficient are improved than the Joint Probability Data Association (JPDA) algorithm. It is fit for the real application.