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Threshold Factor Track Classification Algorithm Based on Target Motion Model

Traditional track classification algorithms such as multi-factor correlation method, fuzzy function method exist few flaws. In order to solve the problem of multi-sensor target group track classification, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model through intrinsic properties of the target itself. It updates iterative center according to moving target real-time motion state and timely abandons weak correlated or uncorrelated the plot. Experimental results show that the mean error and variance in the direction of X/Y/Z have been reduced through the proposed algorithm than traditional algorithm in a certain extent. In addition, the computation time consumed is reduced.