基于多级MEKF的微型无人机状态估计
Multi-level MEKF-based State Estimation of Micro-UAVs
刘砚菊 1李景泉 1冯迎宾1
作者信息
- 1. 沈阳理工大学自动化与电气工程学院,沈阳 110159
- 折叠
摘要
针对微型无人机在GPS拒止环境下,低成本惯性测量单元(inertial measurement 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.
Abstract
Aiming at the problem that it is difficult for conventional algorithms to ensure the accu-racy 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 fu-sion of IMU and optical flow sensors is proposed.Firstly,the magnetometer,gyroscope and accelerome-ter data are fused to achieve attitude estimation;secondly,the attitude estimation,acceleration and opti-cal 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 algo-rithm achieves faster and more reliable state estimation than that of the traditional algorithms.
关键词
微型无人机/光流传感器/乘性扩展卡尔曼滤波/状态估计Key words
micro-UAVs/optical flow sensor/multiplicative extended Kalman filter/state estimation引用本文复制引用
基金项目
辽宁省基本科研基金资助项目(LJKMZ20220614)
出版年
2024
国家科技期刊平台
NSTL
万方数据