本文選題：隨機誤差　切入點：自回歸滑動平均(ARMA)模型　出處：《壓電與聲光》2017年01期 　論文類型：期刊論文

【摘要】：針對半球諧振陀螺(HRG)隨機誤差影響慣性測量單元測量精度的問題,提出了一種改進的基于自回歸滑動平均(ARMA)模型和自適應濾波(AKF)的隨機誤差處理方法。該文對預處理的數(shù)據(jù)進行了自相關和偏相關特性分析,判斷隨機誤差的適用模型,以及利用貝葉斯信息準則(BIC)準則估計ARMA模型的階數(shù),通過長自回歸模型計算殘差法獲取模型參數(shù),引入加權自適應因子在線調整一步預測誤差陣和量測噪聲矩陣用于改進濾波方程,并比較了5項主要誤差系數(shù)值。結果表明,改進的算法能夠有效抑制隨機誤差,為HRG的隨機誤差建模補償提供了新方法。
[Abstract]:Aiming at the problem that random error of hemispherical resonance gyroscope (HRG) affects the measurement accuracy of inertial measurement unit, An improved ARMA model based on autoregressive moving average (ARMA) model and adaptive filter (AKF) is proposed to deal with random errors. In this paper, the autocorrelation and partial correlation characteristics of preprocessed data are analyzed to judge the applicable model of random errors. The order of ARMA model is estimated by Bayesian Information Criterion (ARMA), and the model parameters are obtained by long autoregressive method. The weighted adaptive factor is introduced to adjust the one-step prediction error matrix and the measurement noise matrix to improve the filtering equation, and five main error coefficients are compared. The results show that the improved algorithm can suppress the random error effectively. It provides a new method for HRG stochastic error modeling compensation.
【作者單位】： 火箭軍工程大學控制工程系;
【基金】：國家自然科學基金資助項目(61174030)
【分類號】：TN96

【參考文獻】

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1 林青;戴慧s，

本文編號：1582260