自适应滤波器在微型姿态确定系统中的应用

基于MEMS的IMU由于低成本、体积小和低耗能得到了很广泛的应用.但是,惯性MEMS传感器有很大的噪声、偏差以及刻度误差,由于传统低成本的MEMS传感器使用的捷联算法很难取得令人满意符合性能要求的姿态确定值.利用改进的自适应增益卡尔曼滤波器在随机模式下建立一个小型姿态确定系统.这个改进的滤波器在一个时间变量转移矩阵中有六个状态量,它们分别是:三个姿态倾斜角和三个陀螺偏移误差.滤波器用三个加速度计的测量量和磁罗盘来驱动状态的更新.当系统处于非加速度状态下,加速度计对重力加速度的测量以及磁罗盘对航向的测量很显然可以产生很好的状态估计量;当系统处于高速动态状态并且偏移可以收敛到一个精确估计值时,对姿态的估算就需要很长一段时间.自适应滤波器可以在动态状况下用加速度计自动调整增益产生最佳性能.提供了这种技术的算法,并且对此进行分析,之后给出实验结果.

Adaptive filter for miniature MEMS based attitude and heading reference system

The Inertial Measurement Unit(IMU) based on the newly developed MEMS technology has wide applications due to its low-cost,small size,and low power consumption.However,the inertial MEMS sensors have large noise,bias and scale factor errors due to drift.The traditional strapdown algorithm using a low-cost MEMS sensor ONLY was difficulty satisfying the attitude and heading performance requirements.An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on stochastic model.The adaptive filter has six states with a time variable transition matrix.The six states are three tilt angles of attitude and three bias errors for the gyroscopes. The filter uses the measurements of three accelerometers and a magnetic compass to drive the state update.When the system is in the non-acceleration mode,the accelerometer measurements of the gravity and the compass measurements of the heading have observability and yield good estimates of the states.When the system is in the high dynamic mode and the bias has converged to an accurate estimate,the attitude calculation will be maintained for a long interval of time.The adaptive filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance.This paper presents the methodology of the technique,performed the analysis,and gave the testing results of the system based on the adaptive filter.