A Novel Measurement Data Classification Algorithm Based on SVM for Tracking Closely Spaced Targets
Zongmin Zhao, Xiang Wang, Tao Wang · IEEE Transactions on Instrumentation and Measurement · 2018
Target tracking is an important field of investigation in wireless sensor networks. When multiple targets are closely spaced, their measurement points are mixed together due to the insufficient accuracy of the sensor. This may bring some difficulties in determining the positions of observation points required by subsequent algorithms and applications. Most of the known tracking algorithms are derived from the Kalman filter, extended Kalman filter, and particles filter. In this paper, a novel measurement data classification algorithm based on support vector machine (SVM) is provided. SVM and Kalman filter are combined to obtain the updated classification line at each sampling period, and the sampling points would be classified by the updated classification line to calculate the coordinates of the corresponding observation points, which are then used to estimate the precise positions of two targets. A series of simulations and experiments are carried out to validate the presented algorithm on classifying and tracking two targets moving closely. Simulation results for a maneuvering targets classification example illustrate the feasibility of the new algorithm, as well as experimental and quantitative results from the practical data validate the effectiveness and stability of our proposal in contrast with existing methods.