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建立欠观测条件下的非线性增量量测方程,并给出其线性化方法,在此基础上提出一种欠观测条件下的扩展增量Kalman滤波(EIKF)模型及其递推算法.工程实际中,由于环境因素的影响、测量设备的不稳定性等原因往往带来未知的系统误差,传统的扩展Kalman滤波(EKF)无法对这种未知的系统误差进行补偿和校正,结果产生较大的滤波误差,甚至导致发散.提出的扩展增量Kalman滤波方法能够成功地消除测量的系统误差,从而有效地提高非线性滤波的精度.该方法计算简单,便于工程应用.
Based on this, an extended incremental Kalman filter (EIKF) model and its recursive algorithm are proposed under the condition of under-observation. Engineering In practice, unknown systematic errors often result from the influence of environmental factors and the instability of measuring equipment. The traditional extended Kalman filter (EKF) can not compensate for and compensate for such unknown systematic errors, resulting in a large , And even lead to divergence.The proposed extended incremental Kalman filter method can successfully eliminate the systematic errors of measurement and improve the accuracy of nonlinear filtering effectively.The method is simple and easy to be applied in engineering.