Maneuvering Target Tracking Algorithm in Multi-radar Networking Based on Interacting Multiple Model-virtual Observation Kalman Filter

Because a single model cannot describe maneuvering targets accurately and measured values in the polar coordinates of networked radar (NR) have a nonlinear relation with state values in the coordinates system of the fusion center of the radar networking station(FCRNS),we propose a strategy of transforming the tracking coordinates of FCRNS into virtual observation coordinates to satisfy linear constraints in the target tracking of multi-radar networkingusing (Kalman filter) KF. We construct an interacting multiple model that combines the coordinated turn model and the constant velocity model to adaptively model the motion of the airspace maneuvering targets in the multiradar networking. Then we propose an interacting multiple model-virtual observation Kalman filter algorithm (IMM-VOKFA) to track airspace maneuvering targets by modeling the covariance matrix of virtual observation errors and the initial estimation. We use the proposed IMM-VOKFA to track the turning of a maneuvering target in a multi-radar networking system and compare it with interacting multiple model extended Kalman filter algorithm. Simulation results demonstrate that IMM-VOKFA has strong motor adaptability,good calculating stability,and strong engineering effectiveness.