Adaptive filter for a miniature MEMS based attitude and heading reference system

A strapdown Inertial Navigation System (INS) can provide attitude and heading estimates after initialization and alignment. Many factors affect the accuracy and the performance of the system. They mainly are: sensor noise, bias, scale factor error, and alignment error. 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 is difficultly 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 a 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. The paper presents the methodology of the technique, performs the analysis, and gives the testing results of the system based on the adaptive filter. The whole system can be fitted within the size of 5cmx5cmx5cm with analog to digital conversion and digital signal processing boards.