一种鲁棒自适应容积卡尔曼滤波方法及其在相对导航中的应用  

Robust Adaptive Cubature Kalman Filter and Its Application in Relative Navigation

作　　者：张旭[1] 崔乃刚[1] 王小刚[1] 崔祜涛[1] 秦武韬 

机构地区：[1]哈尔滨工业大学航天学院,黑龙江哈尔滨150001

出　　处：《兵工学报》2018年第1期94-100,共7页Acta Armamentarii

基　　金：国家自然科学基金项目(61304236)

摘　　要：针对无人机编队相对导航系统中视觉导航传感器量测噪声服从非高斯分布的问题,提出一种带噪声估计器的鲁棒自适应容积卡尔曼滤波(CKF)算法。该算法将Huber求解线性回归问题与协方差匹配方法相结合,利用残差序列实时估计,调整系统过程噪声和量测噪声的统计特性,并采用遗忘加权参数对接收到的测量数据进行加权,从而准确地估计出无人机之间的相对位置、速度和姿态信息,提高了鲁棒CKF算法的自适应能力。仿真结果表明,与标准CKF算法和鲁棒CKF算法相比,该算法对受污染的噪声统计特性有较强的自适应性,估计精度高,鲁棒性更强。An adaptive Huber-based cubature Kalman filter（CKF） algorithm with noise estimator is proposed to solve the problem that the measurement noise of vision-based relative navigation sensor for unmanned aerial vehicles（UVAs） formation follows non-Gaussian distribution.The Huber technique based on solving the linear regression problem and the covariance matching method are combined in the proposed algorithm.The residual sequences are used to estimate and tune the statistical characteristics of process noise and measurement noise on line,and then the received measured data are weighted by using the forgetting weighted parameters,thus estimating the relative position,relative velocity and relative attitude information among unmanned aerial vehicles accurately,and improving the adaptive capability of Huber-based CKF algorithm.The simulated results show that the proposed algorithm has strong adaptability to the statistical properties of the contaminated noises,higher estimation accuracy and stronger robustness compared with the standard CKF algorithm.

关 键 词：无人机 相对导航 非高斯噪声 鲁棒自适应滤波 鲁棒性 

分 类 号：V249.321[航空宇航科学与技术—飞行器设计]