Abstract—

This paper presents a Novel Adaptive Fuzzy Extended Kalman Filter namely (NAFEKF) which has been developed and applied for attitude estimation using only the outputs of strap-down IMU (Gyroscopes and Accelerometers) and strap-down magnetometer. The NAFEKF, which is based on EKF (Extended Kalman Filter) aided by FIS (Fuzzy Inference System), is validated in Matlab environment on simulated trip data and real data acquired during an UAV’s trip. Accuracy of estimated attitude is increased using NAFEKF compared to typical EKF and in addition the measurement noise covariance matrix is tuned, the proposed filter uses multiplicative error for quaternion. Simulation results show that estimated measurement noise covariance matrix is closed to its true value in cruise phase of flight (stationary phase), while in nonstationary phase it refers to the validity of accelerometer measurement model in the filter in NAFEKF; it neglects measurements from accelerometers in this case.

中文翻译：

---

Gps拒绝环境中用于姿态估计的新型自适应模糊扩展卡尔曼滤波器

摘要—

本文介绍了一种新颖的自适应模糊扩展卡尔曼滤波器（NAFEKF），该滤波器已开发并仅使用捷联IMU（陀螺仪和加速度计）和捷联磁力计的输出进行姿态估计。NAFEKF基于FIS（模糊推理系统）辅助的EKF（扩展卡尔曼滤波器），已在Matlab环境中对模拟行程数据和无人机飞行过程中获取的真实数据进行了验证。与典型的EKF相比，使用NAFEKF可以提高估计姿态的精度，此外，还可以对测量噪声协方差矩阵进行调整，因此所提出的滤波器对四元数使用了乘法误差。仿真结果表明，估计的测量噪声协方差矩阵在飞行巡航阶段（静止阶段）接近其真实值，在非平稳阶段，它是指NAFEKF滤波器中加速度计测量模型的有效性。在这种情况下，它忽略了来自加速度计的测量。