The Extended Kalman Filter (EKF) is currently a dominant sensor fusion method for mobile devices, robotics, and autonomous vehicles. Its performance heavily depends on the selection of EKF parameters. Therefore, the optimal selection of parameters is a critical factor in EKF design and use. In this paper, a methodical and efficient method of EKF parameter tuning is presented. The tuning framework uses nominal parameters generated by Gauss Markov (GM) and Allan Variance (AV) methods that are tuned by Genetic Algorithms (GA) accelerated by Design of Experiments (DoE). This framework has been implemented in MATLAB and tested using simulations and real data under a tightly coupled EKF that fuses IMU and GNSS measurements of a self‐driving car provided by the Blackberry QNX company. The results demonstrate that GA‐tuned parameters increase accuracy substantially over nominally tuned parameters, and that the DoE technique consistently improves the convergence behavior of the GA.

中文翻译：

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使用实验和遗传算法进行卡尔曼滤波器参数优化的有效调整框架

扩展卡尔曼滤波器（EKF）当前是用于移动设备，机器人技术和自动驾驶车辆的主要传感器融合方法。其性能在很大程度上取决于EKF参数的选择。因此，参数的最佳选择是EKF设计和使用的关键因素。本文提出了一种有效而有效的EKF参数整定方法。调整框架使用由高斯马尔可夫（GM）和艾伦方差（AV）方法生成的标称参数，这些参数由通过实验设计（DoE）加速的遗传算法（GA）进行了调整。该框架已在MATLAB中实现，并在紧密耦合的EKF下使用仿真和真实数据进行了测试，该融合了Blackberry QNX公司提供的自动驾驶汽车的IMU和GNSS测量结果。