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

An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastie model. The adaptive filter has six states with a time variable transition matrix. 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 eslimates of the states. When the system is in the high dynamic mode and the bias has converged to an aceurate estimate, the attitude caleulation 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,

Initial alignment for low-cost attitude and heading reference systems

MEMS （micro electro mechanical systems） inertial navigation system ~, Mll＇~3） nas Been WllUly used in robots for its low-cost. The MINS and magnetometers are commonly the component parts of the attitude and heading reference systems （AHRS）, which provide pitch and roll angles relative to the earth gravity vector, and heading angle relative to the north. However, the performance of sen- sors with low cost AHRS is not so good. The gyros are not sensitive enough to observe the earth an- gular velocity, so the traditional technique like alignment algorithm is invalid. The measurements of gyros become useless to determine the initial attitude matrix from navigation frame to body frame. The alignment algorithm is computed by the accelerometers and magnetometers. The process is es- tablished as an optimization problem of finding the maximum eigenvector. Meanwhile the sensitive analysis with respect to the biases of accelerometers is proposed. Then the recursive least squares al- gorithm （RLSA） is introduced. The comparison between the proposed method and RLSA is provid- ed. The results demonstrate its accuracy favorably and verify the feasibility of the proposed algo- rithm.

Inner Attitude Integration Algorithm Based on Fault Detection for Strapdown Inertial Attitude and Heading Reference System

This article proposes a new inner attitude integration algorithm to improve attitude accuracy of the strapdown inertial attitude and heading reference system （SIAHRS） , which, by means of a Kalman filter, integrates the calculated attitude from the accelerometers in inertial measuring unit （IMU） , called damping attitudes, with those from the conventional IMU. As vehicle＇ s acceleration could produce damping attitude errors, the horizontal outputs from accelerometers are firstly used to judge the vehicle＇ s motion so as to determine whether the damping attitudes could be reasonably applied. This article also analyzes the limitation of this approach. Furthermore, it suggests a residual chi-square test to judge the validity of damping attitude measurement in real time, and accordingly puts forward proper information fusion strategy. Finally,the effectiveness of the proposed algorithm is proved through the experiments on a real system in dynamic and static states.

An adaptive attitude algorithm based on a current statistical model for maneuvering acceleration

A current statistical model for maneuvering acceleration using an adaptive extended Kalman filter（CS-MAEKF） algorithm is proposed to solve problems existing in conventional extended Kalman filters such as large estimation error and divergent tendencies in the presence of continuous maneuvering acceleration. A membership function is introduced in this algorithm to adaptively modify the upper and lower limits of loitering vehicles＇ maneuvering acceleration and for realtime adjustment of maneuvering acceleration variance. This allows the algorithm to have superior static and dynamic performance for loitering vehicles undergoing different maneuvers. Digital simulations and dynamic flight testing show that the yaw angle accuracy of the algorithm is 30% better than conventional algorithms, and pitch and roll angle calculation precision is improved by 60%.The mean square deviation of heading and attitude angle error during dynamic flight is less than3.05°. Experimental results show that CS-MAEKF meets the application requirements of miniature loitering vehicles.