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MULTI-SENSOR INFORMATION FUSION-BASED MODEL ADAPTIVE LATERAL VELOCITY ESTIMATION METHOD
MULTI-SENSOR INFORMATION FUSION-BASED MODEL ADAPTIVE LATERAL VELOCITY ESTIMATION METHOD
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机译：基于多传感器信息融合的模型自适应横向速度估计方法
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摘要
A multi-sensor information fusion-based model adaptive lateral velocity estimation method. Firstly, designing an adaptive process noise matrix and a measurement noise matrix of an SR-UKF algorithm with reference to information, such as the lateral acceleration, yaw rate, and front wheel steering angle, of a vehicle; then, on the basis of an original estimation method dynamics model, adding an adaptive term so as to fuse a kinematics model, the weight ratio of the two models being adjusted by coefficients of the adaptive term; and finally, the adaptive noise matrix and the adaptive model are substituted into the SR-UKF algorithm for lateral velocity estimation. A basic probability function of the confidence of two sensors is defined according to the deviation between the lateral acceleration and yaw rate sensor values and dynamics model calculation values, and the information of the two sensors is fused according to the Dempster-Shafer evidence theory, so that the accuracy of an evaluation dynamics model and the uncertainty of the sensors are quantitatively calculated according to observation values of the two sensors, so as to obtain a coefficient value of an adaptive term in an evaluation method model, and finally adaptation of the model is achieved.
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