Multisensor Fusion via Alpha-Beta-Gamma Filtering with Covariance Matching Method

In an air surveillance system the desired improvements of tracking system rely on more accurate state estimates and less computation loads. A state-vector multisensor data fusion approach that consists of local processor and global processor is employed to describe the problem of tracking a maneuvering target in the Inertial Cartesian Coordinate System (ICCS). For local processor, the sensor filtering algorithm utilized in the Reference Cartesian Coordinate System (RCCS) is presented for target tracking when the radar measures range, bearing and elevation angle in the Spherical Coordinate System (SCS). To reduce the computational loads involved in physical implementation, the α-β-γ filtering technique is essentially based on the decoupling technique that filter gain formulations are recursively computed in the Line-of-sight Cartesian Coordinate System (LCCS) and then transformed for use in the RCCS. For global processor, data fusion algorithm called covariance matching method is developed using sensor steady-state covariance matrices to compute each sensor weight for combining the corresponding state estimate in the ICCS. Performance results for the proposed algorithm are compared with those of sensor α-β-γ filtering algorithms, using simulations of typical target maneuvering scenarios.

並列關鍵字

Multisensor fusion ； α-β-γ filtering ； Covariance matching

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