3D Coordinate-coupled Variable Structure Multiple Model Estimator for Maneuvering Target Tracking
In the 3D maneuvering target tracking,unknown prior and coordinate coupling errors can cause model-mode mismatch and state estimation bias.In this paper,the state transition matrices are modified based on the target velocity-orthogonal condition,the spherical feasible domain is approximated by using the primal-dual regularization,and the adaptive turn rate model is combined in the frame of Unscented Kalman Filtering(UKF)to estimate the model-conditioned state,attaining the consistent output processing.3D Variable Structure Multi-Model UKF(VSMMUKF)algorithm is derived.Simulation results show that,compared to the Multimode Importance UKF(MIUKF)algorithm,VSMMUKF can more accurately fit the maneuvering motion of 3D spatial point target with the comparable computational complexity;Compared to the Interactive Multi-model Maximum Minimum Particle Filtering(IMM-MPF)algorithm,the filtering accuracy of VSMMUKF for tracking a fixed-wing Unmanned Aerial Vehicle(UAV)has improved by 2.8%~59.9%,and the overall computation burden has reduced an order of magnitude.
3D Maneuvering target trackingCoordinate-couplingAdaptive turn rateVariable structure multiple modelNonlinear state estimation