No abstract

This study presents a novel adaptive grid interacting multiple model based on modified iterated extended Kalman filter (AGIMM-MIEKF) for tracking a manoeuvreing target using radar/infrared (IR) heterogeneous sensors. This tracking algorithm is developed by aligning observation data of radar/IR sensors in time, and fusing the synthesised data before applying to AGIMM-MIEKF algorithm. Under the architecture of the proposed algorithm, the AGIMM deals with the model switching, whereas the MIEKF accounts for non-linearity in the dynamic system models. A new measurement update equation and an iterated termination criterion are derived and applied to radar/IR tracking system. The simulation results show that the presented AGIMM-MIEKF has higher tracking precision than the traditional algorithms.

No abstract

No abstract

No abstract

No abstract

No abstract

To solve the problem of tracking maneuvering airborne targets in the presence of clutter, an improved interacting multiple model probability data association algorithm (IMMPDA-MDCM) using radar/IR sensors fusion is proposed. Under the architecture of the proposed algorithm, the radar/IR centralized fusion tracking scheme of IMMPDA-MDCM is designed to guarantee the observability of the target state. The interacting multiple model (IMM) deals with the model switching. The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively. The probability data association (PDA) handles data association and measurement uncertainties in clutter. The simulation results show that the proposed algorithm can improve the tracking precision for maneuvering target in clutters, and has higher tracking precision than the traditional IMMPDA based on EKF and IMMPDA based on DCM algorithm.