共查询到19条相似文献,搜索用时 125 毫秒
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针对容积卡尔曼滤波在多源融合定位中存在跟踪能力不强和自适应能力差的问题,在传
统容积卡尔曼滤波的基础上,提出了改进自适应抗差容积卡尔曼滤波算法。建立了基于新息的自
适应判决准则与修正方法,使得滤波算法能够及时跟踪目标真实状态;引入抗差因子调节观测协
方差矩阵,以减小观测值异常问题对滤波精度的影响;采用奇异值分解代替容积卡尔曼中的Cholesky
分解,提高数值计算的稳定性。超宽带/惯性导航联合定位实验结果表明,与扩展卡尔曼滤波
和容积卡尔曼滤波相比,改进的自适应抗差容积卡尔曼滤波定位精度更高,数值稳定性更好,增强
了定位系统在粗差干扰下的鲁棒性。 相似文献
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鲁棒EKF在脉冲星导航系统中的应用 总被引:1,自引:1,他引:0
针对脉冲星导航系统的滤波问题,传统的扩展卡尔曼滤波(EKF)算法存在不能克服系统模型存在不确定性参数以及乘性噪声等缺陷,提出一种鲁棒EKF算法。首先,分析了状态预测误差方程和估计误差方程,利用统计学原理,得到了状态预测方差矩阵和状态估计方差矩阵计算等式。由于系统模型存在不确定性参数,状态预测协方差矩阵和状态估计协方差矩阵无法计算;因此,利用4个重要矩阵不等式,分析并找到预测方差矩阵和状态估计方差矩阵的上界。最后,利用状态估计误差协方差矩阵上界设计状态增益矩阵,使得状态估计协方差矩阵的迹最小。将该算法对脉冲星导航系统进行仿真,仿真结果验证了所提算法的有效性。 相似文献
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一种鲁棒GNSS矢量跟踪环 总被引:1,自引:0,他引:1
复杂环境下导航接收机的连续可用性一直是卫星导航领域的研究重点。针对矢量跟踪环动态适应性不够和误差在通道间传播的问题,提出一种鲁棒全球导航卫星系统(GNSS)矢量跟踪环。基于各通道的伪距、伪距率和伪距加速度状态量构建扩展卡尔曼滤波器(EKF),通过灵活设置过程噪声方差阵,实现跟踪通道的耦合与解耦;采用基于极大似然估计器(MLE)的鉴别器生成码延迟和载波频率偏差观测量;利用滤波值修正并预测伪距率来控制本地数控振荡器(NCO),实现环路的闭合。仿真结果表明,本文设计的矢量跟踪环在保证环路相互辅助的基础上,避免了衰减通道误差在通道间的传播,可以对被遮挡信号保持稳定跟踪,鲁棒性优于传统矢量延迟频率锁定环。 相似文献
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基于UKF准开环结构的高动态载波跟踪环路 总被引:2,自引:1,他引:1
对高动态环境下的全球卫星导航系统(GNSS)载波信号跟踪方法进行了研究。在分析高动态载波信号模型的基础上,提出了一种基于无迹卡尔曼滤波(UKF)的准开环载波跟踪方法。此方法能够消除导航数据二进制相移键控(BPSK)调制的影响,并采用四维UKF相位估计模型提高跟踪精度,同时对估计值进行补偿以减少滤波器的滞后性。通过模拟接收机的高动态运动轨迹,从跟踪误差、跟踪结果和补偿效果3个方面,与基于卡尔曼滤波(KF)的锁频环(FLL)辅助锁相环(PLL)结构的传统跟踪方法进行比较,结果表明基于UKF的准开环跟踪方法能够有效地完成高动态环境下的载波跟踪。 相似文献
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《航空学报》2014,(8)
对于具有复杂加性噪声特点的非线性动态系统,往往很难直接运用传统非扩展容积卡尔曼滤波(CKF)方法对其状态进行有效估计。针对这一问题,通过非扩展和扩展Cubature变换精度的对比分析,结合扩展Cubature点集的约简特性,提出了一种约简二次扩展平方根容积卡尔曼滤波(RTA-SRCKF)方法。该方法采用二次扩展策略,在时间更新环节将过程噪声进行扩展,在量测更新环节将量测噪声进行扩展,有效缩减了采样点,降低了算法复杂度,具有很好的实时性,且在未损失滤波精度的前提下算法计算量明显降低,适用于具有复杂加性噪声特点的非线性动态系统状态估计。捷联惯性导航系统(SINS)大失准角初始对准仿真结果验证了理论分析结论,方位对准精度接近理论极限对准精度。 相似文献
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针对目前自适应滤波算法的不足,在测量系统量测噪声方差未知的情况下,设计了一种基于冗余测量的自适应卡尔曼滤波(RMAKF)算法。通过对系统冗余测量值的一阶、二阶差分序列进行有效的统计分析,可以准确估计系统量测噪声统计特性,进而在滤波过程中自适应调节噪声方差阵R,提高滤波精度。以全球定位系统/惯性导航系统(GPS/INS)松组合导航系统为对象进行了仿真实验,结果表明该算法在测量系统噪声特性未知或发生改变时,可对其进行准确估计,在采用低精度惯性器件情况下,滤波结果较其他主要自适应卡尔曼滤波算法有较明显的改进。 相似文献
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为了解决大场景下基于三维到达角的目标跟踪问题,提出了一种具有无偏性的伪线性卡尔曼滤波。首先,基于三维到达角信息对目标运动模型与量测模型进行建模;之后,对量测模型进行了伪线性化处理,得到了线性形式的目标量测模型。为了解决伪线性卡尔曼滤波存在的有偏性问题,提出了一种结合EKF(extend Kalman filter)的三维伪线性无偏卡尔曼滤波。仿真实验表明,该模型能够对非机动目标与机动目标有效跟踪,对于百公里级别的目标,当角测量误差从0.1°变化到0.5°,算法在仿真时间结束时均能将绝对位置误差降低至10 km以内,且算法的运行速度与EKF为同一个量级,同时兼顾了抗干扰能力、定位跟踪精度、运行效率的要求,能够为大场景下的目标跟踪提供有效方法。 相似文献
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Coordinate Conversion and Tracking for Very Long Range Radars 总被引:1,自引:0,他引:1
The problem of tracking with very long range radars is studied in this paper. First, the measurement conversion from a radar's r-u-v coordinate system to the Cartesian coordinate system is discussed. Although the nonlinearity of this coordinate transformation appears insignificant based on the evaluation of the bias of the converted measurements, it is shown that this nonlinearity can cause significant covariance inconsistency in the conventionally converted measurements (CM1). Since data association depends critically on filter consistency, this issue is very important. Following this, it is shown that a suitably corrected conversion (CM2) eliminates the inconsistency. Then, initialized with the converted measurements (using CM2), four Cartesian filters are evaluated. It is shown that, among these filters, the converted measurement Kalman filter with second order Taylor expansion (CM2KF) is the only one that is consistent for very long range tracking scenarios. Another two approaches, the range-direction-cosine extended Kalman filter (ruvEKF) and the unscented Kalman filter (UKF) are also evaluated and shown to suffer from consistency problems. However, the CM2KF has the disadvantage of reduced accuracy in the range direction. To fix this problem, a consistency-based modification for the standard extended Kalman filter (E1KF) is proposed. This leads to a new filtering approach, designated as measurement covariance adaptive extended Kalman filter (MCAEKF). For very long range tracking scenarios, the MCAEKF is shown to produce consistent filtering results and be able to avoid the loss of accuracy in the range direction. It is also shown that the MCAEKF meets the posterior Carmer-Rao lower bound for the scenarios considered. 相似文献
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Seong-Taek Park Jang Gyu Lee 《IEEE transactions on aerospace and electronic systems》1998,34(4):1337-1344
The extended Kalman filter (EKF) has been widely used as a nonlinear filtering method for radar tracking problems. However, it has been found that if cross-range measurement errors of the target position are large, the performance of the conventional EKF degrades considerably due to nonnegligible nonlinear effects. A new filtering algorithm for improving the tracking performance with radar measurements is developed based on the fact that correct evaluation of the measurement error covariance is possible in the Cartesian coordinate system. The proposed algorithm may be viewed as a modification of the EKF in which the variance of the range measurement errors is evaluated in an adaptive manner. The filter structure facilitates the incorporation of the sequential measurement processing scheme, and this makes the resulting algorithm favorable to both estimation accuracy and computational efficiency. Computer simulation results show that the proposed method offers superior performance in comparison to previous methods. Moreover, our developed algorithm provides some useful insight into the radar tracking problem 相似文献
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This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms, one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter. 相似文献
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Siouris G.M. Guanrong Chen Jianrong Wang 《IEEE transactions on aerospace and electronic systems》1997,33(1):232-240
The important tracking problem by radar of an incoming ballistic missile system, which contains uncertainty in modeling and noise in both dynamics and measurements, is studied. The classical extended Kalman filter (EKF) is no longer applicable to such an uncertain system, and so a new extended interval Kalman filter (EIKF) is developed for tracking the missile system. Computer simulation is presented to show the effectiveness of the EIKF algorithm for this uncertain and nonlinear ballistic missile tracking problem. 相似文献
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在对弹道目标跟踪预警的工程实践中,雷达系统对目标运动的信息处理速度尤为重要,因而,文章选取自适应跟踪模型与卡尔曼滤波相结合的方法解决自由段弹道目标的跟踪问题,并与扩展卡尔曼跟踪算法做了对比分析。仿真显示,2种滤波方式分别与自适应跟踪模型相结合后,卡尔曼滤波和扩展卡尔曼滤波跟踪性能相差不大,但其算法简单、运算时间短,可以较好满足自由段弹道目标跟踪的工程需求。 相似文献