Spatial alignment is the prerequisite for the successful data fusion of multiple sensors. A CKF based spatial alignment algorithm for the estimation of bias between radar and infrared sensors on a same platform is presented. The system dynamics of this problem is established in a hybrid coordinate, i.e., the target position in the spherical coordinate while the target speed in the Cartesian one. The system bias is then estimated by the cubature Kalman filter (CKF) in an augmented system state equation. Simulation results show that the proposed algorithm is effective and efficient.
An adaptive alpha-beta filter based on cloud model inference is presented for maneuvering target tracking. The proposed tracker incorporates cloud model in a conventional alpha-beta filter by using the rule bank based on cloud model, which utilizes the residue error and the change of residue error in the last prediction to determine the values of alpha and beta, then track the maneuverable target accurately. The experiment results show that the algorithm is satisfactory and effective.
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