Abstract
Target tracking is an essential part in automotive driver assistance systems. Most maneuvering target tracking algorithms are based on model, and an accurate model can enhance the tracking performance. Compared with constant velocity (CV) model, constant acceleration (CA) model and Singer model, the current statistical (CS) model matches well with the actual motion of target vehicle. But when a target maneuvers in the acceleration with a larger changing range or in the form of sudden change of state, the performance of CS model will become worse. To improve the accuracy of CS model responding to strong maneuvering of target and modeling uncertainties, we propose a novel strategy named KF-SVSF with the combination of the Kalman filter (KF) and Smooth Variable Structure filter (SVSF). A simulated result of target vehicle running in variable acceleration movement demonstrates that the KF-SVSF is a good solution to the fault of CS model.
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