一种自适应混合滤波铁塔姿态解算方法

An adaptive hybrid filter algorithm for attitude estimation of tower

姿态解算精度是制约铁塔姿态监测准确性的主要因素,针对低成本的惯性传感器存在精度低、抗干扰性较差的问题,提出一种自适应互补滤波与强跟踪卡尔曼混合滤波算法。首先利用互补滤波算法融合加速度计和陀螺仪传感器信息,根据载体非重力加速度调整PI控制器参数,自适应补偿陀螺仪角速度;再将滤波解算的角速度向量作为强跟踪卡尔曼滤波的输入矩阵,并通过比例因子实现对量测噪声方差自适应修正,最后经强跟踪卡尔曼滤波后得到姿态估计。通过转台设计静态和动态实验,实验结果表明:混合滤波在静态环境中能抑制姿态角波动,动态环境中能够较好地跟踪姿态变化。

Attitude estimation accuracy is the main factor resisting the accuracy of tower attitude monitoring.An hybrid filter algorithm combining adaptive complementary and strong tracking Kalman filter was proposed to address the problem such as low precision and poor anti-interference of low-cost inertial sensors.Firstly,the message of sensor was obtained by fusing gyroscope and accelerometer data with complementary filter algorithm.PI controller parameters were adjusted according to the non-gravitational acceleration of the carrier,so that the controller was able to adaptively compensate the gyroscope angular velocity;then the filtered solved angular velocity vector was used as the input matrix of strong tracking Kalman filter for measurement fault detection,and adaptive correction of measurement noise variance was realized by scaling factor.Finally,the pose estimation was obtained after strong tracking filtering.Through the static and dynamic experiments of the turntable design,the experimental results show that the hybrid filtering can suppress the attitude angle fluctuation in the static environment and can track the attitude change better in the dynamic environment.