电光与控制, 2017, 24 (10): 102, 网络出版: 2017-10-30   

改进自适应Kalman滤波的SINS/GPS紧组合导航

SINS/GPS Tightly Integrated Navigation Based on Improved Adaptive Kalman Filter
作者单位
山东理工大学电气与电子工程学院,山东 淄博 255000
摘要
当系统噪声和量测噪声统计特性不明确时,基于新息的自适应滤波对两种噪声进行估计时存在相关性,与实际情况不符而影响滤波精度。针对这种情况,提出改进的自适应滤波算法。首先建立了SINS/GPS紧组合导航系统空间方程;然后介绍了自适应卡尔曼滤波原理,指出了此算法对两种噪声估计出现相关性的原因,在此基础上提出了改进的自适应卡尔曼滤波算法,改进算法对系统噪声和量测噪声同时进行在线估计,解决了原算法存在的不足;通过半实物仿真实验可以看出,在系统噪声和量测噪声不明确时改进算法的估计精度,与原有算法在系统噪声和量测噪声已知时的估计精度相当,充分说明了改进算法的实用性。
Abstract
When the statistical characteristics of the system noise and measurement noise are indefinite,the estimated values of the two kinds of noise based on Adaptive Kalman Filter (AKF) are relevant,which does not conform to the reality,and may lower the precision of the filter.In order to solve the problem,Improved Adaptive Kalman Filter (IAKF) is proposed.The state space equation of a highly-integrated navigation system is built up.Principle of adaptive Kalman filter is introduced,and the reason for the relevance of two kinds of estimated values is given.Based on that,the new algorithm is used to estimate the system noise and measurement noise on-line,and the shortcoming of the original algorithm is overcome.Semi-physical simulation imaging experiment is designed to verify the new algorithm.The result shows that:The estimation accuracy of the improved algorithm with unknown system noise and measurement noise corresponds to that of the original algorithm with known system noise and measurement noise,which verifies the feasibility of the improved algorithm.

房德君. 改进自适应Kalman滤波的SINS/GPS紧组合导航[J]. 电光与控制, 2017, 24(10): 102. FANG De-jun. SINS/GPS Tightly Integrated Navigation Based on Improved Adaptive Kalman Filter[J]. Electronics Optics & Control, 2017, 24(10): 102.

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