Applied Mathematics & Information Sciences

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This paper proposes an improved automotive target tracking scheme using FMCWradar which is necessary for the advanced collision warning systems. Since there exist strong nonlinear relationships between the FMCWradar measurements and the target state, the target tracking and data association in dense road clutters have been recognized as a quite challenging problem. It is obvious that the use of accurate range rate measurement might be an excellent choice to improve both target tracking and clutter suppression performances. This motivates us to develop a novel linear recursive automotive target tracking filter based on the measurement conversion in the predicted line-of-sight (LOS) Cartesian coordinate system (PLCCS). Since the x axis of the PLCCS is set by the predicted LOS vector from the host to the target, if the LOS prediction error is imperceptible, the range rate can be approximated to the x component of the relative target velocity in PLCCS. Employing the PLCCS drastically reduces the complexity of the problem and allows us to solve it within the framework of linear recursive Kalman filtering. Through the simulations, the superiority of the proposed method is compared to the existing nonlinear automotive target tracking filters.

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