Fast Data Fusion algorithm for tracking maneuvering target by vehicle formation

When multiple flying vehicles with bearing-only tracking collaborate in locating a target, observation data fusion is required to compensate the drift of navigation system, the noise from observation system, and the unknown maneuvering of target object. Kalman Filtering of nonlinear observation data needs a huge amount of computation, and will cause trouble to onboard embedded computing systems. Least Squared Error (LSE) can process nonlinear measurements at high speed, but the observation noise may be amplified due to the intrinsic deficiency. In this paper, a Fast Data Fusion algorithm is proposed. It combined these two methodologies. LSE is used as the preprocessing stage to build a coarse estimation and Kalman Filtering is used to further eliminate noises from preprocessed estimations. Simulation results show that, proposed method uses no more than 3% of computation time of Kalman Filtering and achieved similar or even better target locating precision.