基于低成本多传感器的自适应组合滤波

Adaptive attitude estimation filtering with low-cost multi-sensors for MAHRS

研究了基于硅微机电系统(MEMS)陀螺、加速度计及磁阻式磁强计组合的微小型飞行器用姿态航向参考系统。针对传统航姿算法无法保证微小型飞行器在长时间、高机动情况下以较高精度保持姿态航向的问题,提出了一种基于低成本多传感器的自适应组合滤波算法。该算法首先通过对运动加速度和磁干扰进行建模并将其引入状态方程来保证载体在长时间高机动情况下依然保持较高的姿态航向精度。其次,采用联邦滤波模式降低运动加速度与磁干扰之间的相互影响,提高算法的精度和可靠性;通过对子滤波器专有状态量的估计方差阵P和量测方差R进行自适应设计,保证不同机动状态切换时算法的稳定性一致。用不同的组合算法进行了半物理仿真试验对比,结果表明,与传统算法相比,该算法在平飞和长时间盘旋飞行条件下均具有较高的精度和鲁棒性。

An integrated Micro Attitude Heading Reference System（MAHRS） based on Micro-electro-mechanical System（MEMS） gyroscopes,accelerometers,and magnetic-sensors was researched for Micro Aerial Vehicles（MAV）.As traditional algorithms could not keep the attitude accuracy of the MAV during a long-time maneuvering,an adaptive attitude estimation filtering algorithm with low-cost multi-sensors was presented for the MAHRS.Firstly,this algorithm was used to establish the models of acceleration and magnetic-disturbance and took them into a state equation.In this way,it could maintain the attitude accuracy of loads during the long-time maneuvering.Then,this algorithm adopted the federated filter mode to reduce the interaction between acceleration and magnetic disturbance to improve its precision and reliability.Moreover,the algorithm made the estimation variance（P） and measurement variance（R） remain steady adaptively during different maneuvering states.By comparing with other different algorithms for the MAHRS,It shows that the presented algorithm is more accurate and reliable than other algorithms during various maneuvering states.