Research On The Technique Of Attitude And Heading System Based On Micro-machined Inertial Component

Recently years, with the development of MEMS (Micro-Electro-Mechanical Systems) and inertial technology, the attitude and heading reference system (AHRS) that is built by MEMS inertial sensors becomes research focus of scholars at home and abroad. It has the features of low price, small size, light weight, high integration and high reliability and has a very broad application prospects in aviation, aerospace, automotive, communications and military fields.The silicon micro-AHRS is composed by MEMS accelerometers, MEMS gyroscopes, magnetometers, the external circuit and DSP processors. Gravity vector and magnetic vector of earth measured by accelerometers and magnetometers are used to compensate for the attitudes which are determined by the gyro. The kalman filter is used for the multi-sensor data fusion, and gives the optimal valuation of heading and attitude ultimately.In the paper, the following works have been done:1. The scheme of MEMS AHRS system built by the MEMS gyroscopes, MEMS accelerometers and magnetometers was determined. The MEMS AHRS error was analyzed and all sensors were calibrated systematically. Then the initial alignment was carried out. The initial attitude angle derived from the initial alignment was used to calculate the optimal heading and attitude estimation by quaternion differential equation and kalman filter. Concerning the characteristics of high dynamic application, an adaptive kalman filter was designed.2. Online test and analysis and data storage software was developed, which could display sensors output data and attitude angle in the form of two-dimensional images in real-time. OpenGL technology is used for the development of three-dimensional visualization of MEMS AHRS's state of motion. The algorithm of software is programmed by the C# programming language which can be provided as a reference for the code transplant to the DSP processor. 3. Xsens's MTi is used to simulate the physical of the MEMS AHRS. The original data of MTi was collected, which was used for attitude estimation with the algorithm of this paper. In addition, experimental adjustments were carried out. The parameters of adaptive Kalman filter are determined by experimental data. The test results of MEMS AHRS and the corresponding analysis are given. After comparing the solver attitude and heading data with the output of MTi, we can draw a conclusion that the algorithm of this paper is practical.The results show that the MEMS AHRS using the algorithm of this paper can achieve the desired precision and the design of MEMS AHRS in this article is feasible.