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动态卡尔曼滤波在导航试验状态估计中的应用 被引量:48

Application of dynamic Kalman filtering in state estimation of navigation test
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摘要 阐述了GPS动态试验的新方案,使用两个精度相差一个数量级的GPS接收平台,通过匀速运动车辆的DGPS及GPS的滤波对比试验,验证了卡尔曼滤波器的有效性。并针对传统EKF(extended Kalman filtering)滤波器动态滤波性能较差的缺陷,引入了一种基于非线性思想的动态无导数卡尔曼滤波器,并对其状态方差阵及随机噪声方差阵Cholesky分解更新公式做了改进,避免了导数的运算,加快了滤波速度,有效地确保方差矩阵平方根的正定性从而抑止了发散。将这种新的卡尔曼滤波器应用于实际动态定位状态估计问题上。试验结果表明:比起传统卡尔曼滤波器,新的卡尔曼滤波器有较高的精度,实用性更强。 A new GPS dynamic test schema is discussed. Two GPS receiver platforms whose accuracies are one order apart are used to verify the efficiency of Kalman filter through filtering comparison between DGPS and GPS on uniform velocity vehicle. Furthermore, aiming at the worse dynamic characteristic of traditional EKF (extended Kalman filtering) , a kind of dynamic derivative-free Kalman filter based on nonlinear transformation is proposed. Especially, the update formulas of Cholesky decomposition of state covariance matrix and random noise covarianee matrix are improved. The improvement avoids derivative calculation, enhances the filtering speed and guarantees the positive definiteness of covariance matrix so as to inhibit divergence. The improved Kalman filter was applied to state estimation of an actual dynamic positioning system. Test results show that the new Kalman filter can obtain higher accuracy and better practicability than traditional Kalman filter.
出处 《仪器仪表学报》 EI CAS CSCD 北大核心 2009年第2期396-400,共5页 Chinese Journal of Scientific Instrument
关键词 全球定位系统 动态 DGPS 无导数卡尔曼滤波 Cholesky分解更新 状态估计 global positioning system dynamic DGPS derivative-free Kalman filtering Cholesky decomposition update state estimation
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参考文献8

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