基于Allan方差解耦自適應(yīng)濾波的旋轉(zhuǎn)SINS精對(duì)準(zhǔn)方法
[Abstract]:The precision alignment method of rotary SINS is studied. The noise variance parameters of rotating SINS state equation and measurement equation are difficult to determine due to the influence of rotating interference of the translocation mechanism and the uncertainty of inertial device error. In order to solve this problem, adaptive Kalman filtering technique is introduced. Sage-Husa is a commonly used adaptive filtering algorithm, but it has strong coupling defects of noise parameters. By studying the relationship between Allan variance and measurement noise variance, using the characteristic of bandpass filter, Allan variance filter can independently calculate the measurement noise covariance matrix R _ s _ k. This method can effectively overcome the coupling problem of Sage-Husa filter. Compared with other improved methods, the method is simple and easy to realize. The simulation experiment and the actual system verification experiment are carried out. The results show that the heading angle alignment accuracy of the SINS, adaptive Kalman filtering algorithm is about 0.6 'higher than that of the standard Kalman filtering algorithm. In the process of error estimation, adaptive Kalman filter can better restrain the influence of external interference error, so it is a better precision alignment method.
【作者單位】: 東南大學(xué)微慣性儀表與先進(jìn)導(dǎo)航技術(shù)教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】:國(guó)家自然科學(xué)基金項(xiàng)目(61374215)
【分類號(hào)】:TN96
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