共查询到17条相似文献，搜索用时 140 毫秒
1.
两种渐消滤波与自适应抗差滤波的综合比较分析 总被引：1，自引：0，他引：1
给出了两种渐消滤波解及其相应的原则，介绍了自适应抗差滤波原理和相应的解，分别从原理和解的表达式上分析了两种渐消滤波和自适应抗差滤波解算的基本性能。利用一个实际算例比较了渐消滤波与自适应抗差滤波解在控制状态异常影响方面的能力。 相似文献
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Kalman滤波时间尺度算法是一种实时的原子钟状态估计方法,在守时实验室具有重要实用价值。由于原子钟状态模型误差估计存在偏差,Kalman滤波时间尺度算法中状态估计可能出现相应异常扰动,应当对状态模型误差进行实时控制。对此,引入基于渐消因子的改进Kalman滤波时间尺度算法。对状态预测协方差矩阵引入渐消因子,利用统计量实时计算渐消因子的量值,控制状态预测协方差阵的增长,降低了原子钟状态估计的扰动。实验结果表明,相比于标准Kalman滤波时间尺度算法和基于预测残差构造自适应因子的Kalman滤波算法,基于渐消因子的改进Kalman滤波时间尺度算法能够提高原子钟状态估计的准确度,改进时间尺度的稳定度。 相似文献
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IMU/GPS组合导航系统自适应Kalman滤波算法 总被引：10，自引：0，他引：10
给出了IMU在地固坐标系中的误差方程，介绍并分析了自适应滤波和渐消Kalman滤波算法原理，然后将渐消因子引入到自适应滤波算法中。并将其应用到IMU／GPS松组合导航系统中，最后利用一个实际算例证明了该组合导航系统的有效性。 相似文献
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卡尔曼滤波常常被用于惯性导航系统初始对准算法,其使用前提是对系统状态进行建模,从而得到比较准确的系统噪声和观测噪声统计特性。在模型失配和观测噪声干扰的情况下,常规卡尔曼滤波会出现精度下降甚至发散,从而影响初始对准精度。针对这一问题,提出了一种新型渐消卡尔曼滤波算法,引入了多重渐消因子对预测误差协方差阵进行调整,设计了基于新息向量统计特性的滤波状态χ2检验条件,使渐消因子的引入时机更加合理,算法的自适应性得到增强。将改进的卡尔曼滤波算法应用到惯性导航系统的初始对准问题中,仿真试验和实测数据试验结果表明,与常规渐消因子滤波算法相比,新算法可以有效提高滤波精度及鲁棒性。 相似文献
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针对建议分布函数的选择问题,系统地分析比较了改进的粒子滤波算法.在此基础上提出了一种新的粒子滤波算法--自适应渐消扩展Kalman粒子滤波方法.该方法用渐消扩展Kalman滤波产生建议分布函数,由于参数的可在线调节性,使得系统具有更好的自适应性和鲁棒性.与用转移先验、扩展Kalman滤波、自适应扩展Kalman滤波、迭代扩展Kalman滤波以及无迹Kalman滤波产生建议分布函数的粒子滤波方法相比,自适应渐消扩展Kalman粒子滤波进一步提高了粒子滤波的精度.通过对GPS与航位推算(DR)组合导航系统GPS/DR的试验,验证了该方法的有效性. 相似文献
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针对建议分布函数的选择问题,系统地分析比较了改进的粒子滤波算法。在此基础上提出了一种新的粒子滤波算法——自适应渐消扩展Kalman粒子滤波方法。该方法用渐消扩展Kalman滤波产生建议分布函数,由于参数的可在线调节性,使得系统具有更好的自适应性和鲁棒性。与用转移先验、扩展Kalman滤波、自适应扩展Kalman滤波、迭代扩展Kalman滤波以及无迹Kalman滤波产生建议分布函数的粒子滤波方法相比,自适应渐消扩展Kalman粒子滤波进一步提高了粒子滤波的精度。通过对GPS与航位推算(DR)组合导航系统GPS/DR的试验,验证了该方法的有效性。 相似文献
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动态导航与定位的质量取决于对动态载体扰动和观测异常扰动的认知和控制质量。在实践中,观测向量及其动态模型信息均可能存在异常,此时若仍利用标准Kalman滤波,则状态滤波解将极不可靠。在标准Kalman滤波原理的基础上,结合模糊控制理论,提出了一种基于模糊理论的抗差Kalman滤波算法。该方法是依据滤波处理后的数据残差,利用模糊理论构造等价权,从而有效控制粗差对导航解的影响,并用算例验证了该方法的可行性和有效性。 相似文献
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论动态自适应滤波 总被引：55，自引：10，他引：55
动态导航与定位的质量取决于对动态载体扰动和观测异常扰动的认知和控制。本文首先介绍了目前广泛使用的Sage自适应滤波，讨论了自适应滤波的残差向量、新息向量及状态参数预报值残差向量的解析关系，以及它们之间的协方差矩阵之间的关系；分析了基于新息向量、残差向量和状态参数预报值残差向量的自适应协方差估计存在的问题。对新近发展起来的抗差滤波、Sage自适应滤波及抗差自适应滤波进行了综合比较与分析，结果表明抗差自适应滤波解算理论与方法除自适应地估计载体状态预报向量的协方差矩阵外，还能自适应地估计任意历元观测量的权。计算结果证实，抗差自适应滤波不仅计算简单，而且能有效地控制观测异常和载体状态扰动异常对动态系统参数估值的影响。 相似文献
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Adaptive estimation of multiple fading factors in Kalman filter for navigation applications 总被引：2，自引：1，他引：1
Kalman filter is the most frequently used algorithm in navigation applications. A conventional Kalman filter (CKF) assumes
that the statistics of the system noise are given. As long as the noise characteristics are correctly known, the filter will
produce optimal estimates for system states. However, the system noise characteristics are not always exactly known, leading
to degradation in filter performance. Under some extreme conditions, incorrectly specified system noise characteristics may
even cause instability and divergence. Many researchers have proposed to introduce a fading factor into the Kalman filtering
to keep the filter stable. Accordingly various adaptive Kalman filters are developed to estimate the fading factor. However,
the estimation of multiple fading factors is a very complicated, and yet still open problem. A new approach to adaptive estimation
of multiple fading factors in the Kalman filter for navigation applications is presented in this paper. The proposed approach
is based on the assumption that, under optimal estimation conditions, the residuals of the Kalman filter are Gaussian white
noises with a zero mean. The fading factors are computed and then applied to the predicted covariance matrix, along with the
statistical evaluation of the filter residuals using a Chi-square test. The approach is tested using both GPS standalone and
integrated GPS/INS navigation systems. The results show that the proposed approach can significantly improve the filter performance
and has the ability to restrain the filtering divergence even when system noise attributes are inaccurate. 相似文献
12.
Comparison of Two Fading Filters anc Adaptively Robust Filter 总被引：1，自引：0，他引：1
YANG Yuanxi GAO Weiguang 《地球空间信息科学学报》2007,10(3):200-203
Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed. 相似文献
13.
Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed. 相似文献
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两种渐消滤波与自适应抗差滤波的综合比较分析(英文) 总被引：1，自引：0，他引：1
Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the in- fluences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed. 相似文献
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抗差卡尔曼滤波在GPS动态定位中的应用 总被引：2，自引：0，他引：2
基于Kalman滤波的GPS动态定位中，动态观测量及其相应的动态模型可能存在异常，若数据处理不考虑对这些异常的特别处理，则模糊度的估值及其所提供的动态信息将极不可靠，按抗差估计原理，文中构造了状态向量和观测值对模糊度的影响函数，并由此建立了动态GPS定位的抗差Kalman滤波解法，实际计算验证了该方法的实用性和可靠性。 相似文献
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An Optimal Adaptive Kalman Filter 总被引：34，自引：0，他引：34
In a robustly adaptive Kalman filter, the key problem is to construct an adaptive factor to balance the contributions of the kinematic model information and the measurements on the state vector estimates, and the corresponding learning statistic for identifying the kinematic model biases. What we pursue in this paper are some optimal adaptive factors under the particular conditions that the state vector can or cannot be estimated by measurements. Two optimal adaptive factors are derived, one of which is deduced by requiring that the estimated covariance matrix of the predicted residual vector equals the corresponding theoretical one. The other is obtained by requiring that the estimated covariance matrix of the predicted state vector equals its theoretical one. The two related optimal adaptive factors are given. These are analyzed and compared in theory and in an actual example. This shows, through the actual computations, that the filtering results obtained by optimal adaptive factors are superior to those obtained by adaptive factors based on experience. 相似文献
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一种基于Bancroft算法的GPS动态抗差自适应滤波 总被引：3，自引：0，他引：3
基于抗差自适应滤波的思想，结合非线性Bancroft算法的特点，提出了一种基于Bancroft算法的GPS动态抗差自适应滤波。计算表明，该算法不仅在一定程度上减弱了由于线性化忽略高次项对导航解的影响，而且再次证实抗差自适应滤波在控制扰动异常的有效性和合理性。 相似文献