Classify Motion Model via SVM to Track Underwater Maneuvering Target

Abstract

Maneuvering target tracking is one of the most important topics in marine research, and it is vital to track a target accurately. Tracking accuracy is closely related to the veracity of estimation of target's motion model. To improve the accuracy of tracking single underwater maneuvering target, this paper brings up a new method to reduce tracking error caused by switching of motion models. A support vector machine (SVM) is used to classify current motion model of the target, then state of the target is estimated by a Kalman filter (KF) with dynamic parameters. Simulation results suggest that compared with the classical interacting multiple model (IMM), algorithm proposed in this paper leads to a more satisfactory tracking root mean square error (RMSE) no matter the target maneuvers or not.