基于多级MEKF的微型无人机状态估计

Multi-level MEKF-based State Estimation of Micro-UAVs

针对微型无人机在GPS拒止环境下,低成本惯性测量单元(inertialmeasurement unit,IMU)精度低稳定性差,传统算法难以保障其状态信息解算实时性和精度的问题,提出一种基于IMU和光流传感器融合的多级乘性扩展卡尔曼滤波(multiplicative extended Kalman filter,MEKF)状态估计算法。将磁力计、陀螺仪和加速度计的数据融合以实现姿态估计;利用姿态估计值、加速度和光流数据以实现速度估计;将速度估计值积分融合高度数据,以实现位置估计。实验结果表明,与传统算法相比,该算法能实现更快速可靠的状态估计。

Aiming at the problem that it is difficult for conventional algorithms to ensure the accuracy and real-time of UAV state information resolution as the low cost inertial measurement units are in poor accuracy and weak in stability when micro-UAVs operate in when GPS-denied environment.A multi-level multiplicative extended Kalman filter(MEKF)state estimation algorithm based on the fusion of IMU and optical flow sensors is proposed.Firstly,the magnetometer,gyroscope and accelerometer data are fused to achieve attitude estimation;secondly,the attitude estimation,acceleration and optical flow data are used to achieve the velocity estimation;finally,the integral of the velocity estimation is fused with the altitude data to achieve position estimation.The experiment results show that the algorithm achieves faster and more reliable state estimation than that of the traditional algorithms.