Track fusion based on threshold factor classification algorithm in wireless sensor networks

Summary Traditional tracking classification algorithm has been widely applied to target tracking in wireless sensor networks. In this paper, focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data. In order to show the improved threshold factor classification algorithm is more effective, we compare the proposed algorithm with the classification algorithm based on the Euclidean distance comprehensive function. Experimental results show that through the proposed algorithm, the mean error and variance in the direction of x/y/z have been reduced to a certain extent, and the computation time consumed is also reduced. Copyright © 2016 John Wiley & Sons, Ltd. Focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data.