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摘要:
光学陀螺随机误差在线补偿方法通常先离线建立随机误差模型，再利用Kalman滤波在线补偿随机误差。受陀螺仪自身性能稳定性以及外界环境的影响，离线建立的随机误差模型应用于在线补偿时会出现偏差；外界环境变化导致量测噪声统计特性具有时变性，不满足Kalman滤波必须已知噪声先验统计的要求，这2个因素均会降低随机误差的在线估计精度。提出一种基于自适应滤波的随机误差在线补偿方法。通过引入虚拟噪声，将随机误差模型偏差的影响归结为时变虚拟系统噪声；进而利用渐消记忆时变噪声估值器，对虚拟系统噪声和量测噪声的统计特性进行估计与修正，消除随机误差模型偏差及量测噪声时变的影响。试验结果表明，所提方法可以实现对随机误差的高精度在线补偿，具有一定工程应用价值。
Abstract:The random error online compensation method of optical gyro establishes a random error model offline and compensates random error online by Kalman filter. However, when used for online compensation, the offline random error model has deviation due to the influence of the external environment and the stability of the gyro’s performance. In addition, changes in the external environment cause measurement noise with time-varying statistical characteristics, which does not meet the requirement that the Kalman filter must have known the noise’s prior statistics. The above factors reduce the online estimation accuracy of random error. Therefore, an online random error compensation method based on adaptive filtering is proposed. The influence of random error model deviation is reduced to time-varying virtual system noise by introducing virtual noise. In order to remove the effects of random error model deviation and time-varying measurement noise, the fading memory time-varying noise estimator is used to estimate and correct the statistical properties of the virtual system and measurement noise. The experimental results show that the proposed method can realize high-precision online compensation for random error, and has certain engineering application value.
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Key words:
- random error /
- online compensation /
- model deviation /
- time-varying noise /
- adaptive filtering
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图 1 基于自适应滤波的随机误差在线补偿方法的流程图
Figure 1. Flow chart of the random error online compensation method based on adaptive filtering
表 1 补偿前后的输出结果对比
Table 1. Comparison of output result before and after compensation
(°)/s 方法 均值 均方差 补偿前 −1.33×10−6 3.06×10−3 传统方法 −3.65×10−7 8.21×10−4 本文方法 4.33×10−8 1.79×10−5 
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