自适应增量 Kalman 滤波方法

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.

Adaptive incremental Kalman filter method

An adaptive incremental Kalman filter (AIKF) method was proposed, of which the concept, model, basic equations and key calculative steps were given. Classical adaptive Kalman filter(AKF)method can effectively estimate the prior knowledge on the statistical characteristics of state noise and measurement noise. Classical AKF method cannot compensate and correct the unknown time-varying system errors that due to environmental factors and the instability of measurement equipments in actual engineering (such as deep space exploration), which produced considerable filter errors and even led to diverge. The presented adaptive incremental Kalman filter method can estimate statistical characteristics of state noise and measurement noise, and also can successfully eliminate these measurement equation's system errors. The method can greatly improve the accuracy of incremental Kalman filter. The method is simple to calculate and easy to apply in engineering.