扩展卡尔曼滤波器 (EKF) 设计用于使用雷达和激光雷达的可靠性函数进行车辆位置跟踪。

Department of Control and Robot Engineering, Chungbuk National University, Cheongju 28644, Korea.

School of Electronics Engineering, Chungbuk National University, Cheongju 28644, Korea.

Sensors (Basel). 2020 Jul 24;20(15):4126. doi: 10.3390/s20154126.

Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study's method produced accurate distance estimations.

使用传感器进行检测和距离测量并不总是准确的。传感器融合通过减少误差来弥补这一缺陷。因此，本研究提出了一种扩展卡尔曼滤波器（EKF），它反映了激光雷达和雷达传感器的距离特性。分析了激光雷达和雷达传感器在距离上的特性，并设计了一个可靠性函数，将卡尔曼滤波器扩展以反映距离特性。根据距离识别传感器误差，提高了位置估计的准确性。使用真实车辆进行了实验，并结合模糊、自适应测量噪声和卡尔曼滤波器的传感器融合进行了对比实验。实验结果表明，该方法能够准确估计距离。