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提出自适应增量Kalman滤波(AIKF)的概念和定义,建立自适应增量Kalman滤波模型及其分析方法,给出主要的计算步骤.传统自适应Kalman滤波(AKF)方法能够对事先未知的系统噪声和量测噪声的统计量进行有效的估计.但是,传统自适应Kalman滤波方法也无法对由于环境因素(如深空探测)的影响、测量设备的不稳定性等原因产生的未知时变测量系统误差进行补偿和校正,从而产生较大的滤波误差,甚至导致发散.提出的自适应增量Kalman滤波方法不但能够对系统噪声和量测噪声的统计量进行估计,而且还能成功消除这种测量系统误差,有效地提高滤波精度.该方法计算简单,便于工程应用.
The concept and definition of adaptive incremental Kalman filter (AIKF) are proposed, and an adaptive incremental Kalman filter model and its analysis method are established, and the main calculation steps are given.The traditional adaptive Kalman filter (AKF) Noise and measurement noise. However, the traditional adaptive Kalman filter method also can not measure the unknown time-varying measurement due to the influence of environmental factors (such as deep space exploration), the instability of measurement equipment, etc. System error compensation and correction, resulting in a larger filtering error, and even lead to divergence.The proposed adaptive incremental Kalman filter not only can measure the system noise and measurement noise statistics, but also successfully eliminate this The system error is measured to effectively improve the filtering accuracy.The method is simple and easy to be applied in engineering.