Research On Noise Reduction Technology Of MEMS Gyroscope In Guidance Rocket Stability Platform

The stabilized platform is available to isolate disturbance of missile body, improving the stability of CCD Imaging, and then improving image guidance rocket projectile guidance accuracy. MEMS gyroscope is the core inertial measurement unit of stabilized platform system, its precision influence the performance of the stabilized platform directly. However because some reasons, the precision of MEMS gyroscope is low, according to the problem, in the paper the signal denoising technology of MEMS gyroscope was studied.Firstly, the composition, the control algorithm and the working principle of stabilized platform was studied, the influence of MEMS gyroscope on stabilized platform was summarized.Secondly, the paper detailed introduced the working principle and performance index of MEMS gyroscope. Futeher, the random error characteristics was analyzed based on Allan Variance. Thirdly, the MEMS gyroscope signal collecting and processing system was designed, which ADXRS450 selected as the MEMS gyroscope, TMS320F2812 selected as the core chip, AT24C1024 selected as the memory chip. Finally, considering the compensation of MEMS gyroscope random errr, three types of denoising algorithm was studied. In the first case, the random error model was established by ARMA analysis method, and based on the model, using the kalman filtering algorithm for denoising. In the second case, wavelet analysis algorithm was used, and Db-5 was selected as the wavelet basis, the threshold was selected as hard with ’rigrsure’criteria, and the scale of decomposition is 4, signals was denoised by reconstruction. In the last case, a new algorithm named it as Kolmogorov-Zurbenko adaptive dual gain Kalman filter was proposed, the KZA algorithm was adopted to separate the steady section and transition section of MEMS gyroscope signals, the cost function was adopted to obtain proper values of Kalman gain at different section of signals, and then filtering MEMS gyroscope signals using different gain.After debugging, the MEMS gyroscope signal collecting and processing system can work properly, the signal collecting and processing was realized. The simulation results of algorithms indicate that the Kolmogorov-Zurbenko adaptive dual gain Kalman filter providing competitive performance than kalman filtering algorithm based on ARMA and wavelet filtering algorithm in both steady condition and dynamic condition, it has characteristics of low noise level and high response rate. Therefore, it was implemented by C language, and transplanted to the MEMS gyroscope signal collecting and processing system, implement signal processing on-line. The experimental results shows that the algorithm is easy to realized, it can reduce the MEMS gyroscope random error effectively, improve the precison of MEMS gyroscope.