Abstract: There is a common problem existed in the existing attitude algorithm used for inertial-magnetic units, that is, some of those depend too much on the outputs of gyro, and have good dynamic but poor static performance, while the other of those depend too much on the outputs of Accelerometer and Geomagnetic sensor（AG）, and have good static but poor dynamic performance. It is problematic to use the module of the linear acceleration to regulate the dependence of gyro or AG, though it is sometimes effective. A method proposed in this paper is to estimate the module of the linear acceleration vector and the geomagnetic vector, and then takes the estimation error as the observation noise of the Extended Kalman Filter（EKF）, which is the most commonly used in the attitude algorithm, in order to construct an adaptive EKF. The estimation error is zero-mean and stationary, and most importantly, its variance increases significantly when the body is moving, so it can make the adaptive EKF has good static and dynamic performance. The experiments are constructed to compare the Kalman filter of MTi and ADIS16480 with this adaptive EKF, and the results validate the effectiveness of the proposed method.

Key words: inertial-magnetic units, adaptive Kalman filter, human body attitude tracking, estimation error

摘要： 现有的适用于惯性-地磁组合的姿态解算算法存在一个共性问题，即或者过于依赖陀螺仪而使得算法的动态精度较高但静态精度较差，或者过于依赖加速度计和地磁传感器组合而出现相反的结果，利用线加速度矢量的模动态调整对上述两者的依赖程度虽然有效但问题较大。提出实时估计加速度计输出矢量与地磁传感器输出矢量的向量积的模，并将估计残差作为姿态解算算法-扩展卡尔曼算法的观测噪声而构成自适应卡尔曼算法，该估计残差的特点是零均值、平稳且其方差在运动体运动时会明显增大，从而使得所提出的自适应卡尔曼算法兼具良好的静动态性能。实验比较了MTi及ADIS16480内置的卡尔曼算法和该文构造的自适应卡尔曼算法，结果证明了后者的有效性。

关键词: 惯性-地磁组合, 自适应卡尔曼算法, 人体姿态跟踪, 估计残差