共查询到20条相似文献,搜索用时 15 毫秒
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This paper preliminarily investigates the application of unscented Kalman filter (UKF) approach with nonlinear dynamic process
modeling for Global positioning system (GPS) navigation processing. Many estimation problems, including the GPS navigation,
are actually nonlinear. Although it has been common that additional fictitious process noise can be added to the system model,
however, the more suitable cure for non convergence caused by unmodeled states is to correct the model. For the nonlinear
estimation problem, alternatives for the classical model-based extended Kalman filter (EKF) can be employed. The UKF is a
nonlinear distribution approximation method, which uses a finite number of sigma points to propagate the probability of state
distribution through the nonlinear dynamics of system. The UKF exhibits superior performance when compared with EKF since
the series approximations in the EKF algorithm can lead to poor representations of the nonlinear functions and probability
distributions of interest. GPS navigation processing using the proposed approach will be conducted to validate the effectiveness
of the proposed strategy. The performance of the UKF with nonlinear dynamic process model will be assessed and compared to
those of conventional EKF. 相似文献
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Adaptive Kalman Filtering for INS/GPS 总被引:69,自引:0,他引:69
After reviewing the two main approaches of adaptive Kalman filtering, namely, innovation-based adaptive estimation (IAE)
and multiple-model-based adaptive estimation (MMAE), the detailed development of an innovation-based adaptive Kalman filter
for an integrated inertial navigation system/global positioning system (INS/GPS) is given. The developed adaptive Kalman filter
is based on the maximum likelihood criterion for the proper choice of the filter weight and hence the filter gain factors.
Results from two kinematic field tests in which the INS/GPS was compared to highly precise reference data are presented. Results
show that the adaptive Kalman filter outperforms the conventional Kalman filter by tuning either the system noise variance–covariance
(V–C) matrix `Q' or the update measurement noise V–C matrix `R' or both of them.
Received: 14 September 1998 / Accepted: 21 December 1998 相似文献
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GPS/INS组合导航系统抗差滤波器设计 总被引:5,自引:0,他引:5
常规Kalman滤波器已经广泛用于GPS/INS组合导航系统,其中假设系统动态模型和噪声统计特性是精确已知的。事实上,这种假设是不符合实际情况的。在组合导航系统中,惯性测量器件的质量不稳定,GPS测量误差受外界环境的影响,因而对组合导航系统进行抗差设计是十分必要的。本文利用对策论设计了能使不确定噪声下性能最好的极小极大抗差滤波器,并将其应用到GPS/INS组合导航系统中。考虑一个IO状态的GPS/ 相似文献
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附有道路信息约束的自适应卡尔曼滤波在车载导航中的应用 总被引:1,自引:0,他引:1
基于车辆“当前”统计模型,利用自适应卡尔曼滤波对车载GPS动态数据进行了处理。将制约车辆运动的道路信息引入模型中,作为约束条件引入卡尔曼滤波方程。其思路是在原有滤波的基础上,利用道路信息约束条件对滤波方程中的一步预测值进行修正,以提高滤波结果的精度。实验结果表明,该算法具有实用意义。 相似文献
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GPS/INS组合导航非线性系统最优估计算法中,基于统计信息和假设检验理论的多渐消因子自适应滤波算法的应用前提条件是残差向量为高斯白噪声。本文针对观测异常会影响残差向量的数字特性分布,提出了一种神经网络辅助的多重渐消因子自适应SVD-UKF算法。该算法采用神经网络算法削弱观测异常对残差序列高斯白噪声分布特性的影响,利用奇异值分解抑制UKF中先验协方差矩阵负定性变化,同时构造多重渐消因子对预测状态协方差阵进行调整,使得不同的滤波通道具有不同的调节能力,高效地应用于多变量复杂系统。最后利用车载实测数据进行了验证。结果表明,神经网络算法极大削弱了观测粗差对残差序列高斯白噪声分布特性的影响,拓展了多重渐消因子的应用范围,使其能在观测值含有粗差的条件下自适应调节不同滤波通道,消除滤波状态中的异常,提高组合导航解的精度和可靠性。 相似文献
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一种两步自适应抗差Kalman滤波在GPS/INS组合导航中的应用 总被引:3,自引:0,他引:3
当GPS观测可用时,如何提高组合导航的可靠性、连续性以及导航精度是组合导航重要的研究主题。针对伪距、伪距率紧组合导航精度低、姿态角误差修正不精确的缺点,本文从参数可观测性角度提出一种两步自适应Kalman滤波算法。首先简单介绍了紧组合Kalman滤波的过程,然后给出了两步自适应抗差滤波的公式和具体步骤,并且进行了分析和比较。最后用实测算例对提出的算法进行验证。结果表明,相比较于伪距、伪距率紧组合Kalman滤波,两步自适应抗差滤波能够控制动态扰动异常和观测异常的影响;导航精度不会随着组合周期的增长、INS惯性元件误差的增大而降低;在惯性元件误差较大的情形下也能够很好地估计元件误差,提高姿态角精度。 相似文献
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改进的高动态GPS定位自适应卡尔曼滤波方法 总被引:32,自引:5,他引:32
本文针对高动态GPS不易确定动态噪声和观测噪声的特点,提出了一种适用于高动态GPS定位的改进的自适应卡尔曼滤波方法。该方法具有数值稳定性好,存储量小的优点,克服了发散的缺点,具有较强的自适应性。 相似文献
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为了提高GPS数据预处理过程中基线解算的精度,文章研究利用多项式拟合法得到Kalman滤波的系统状态方程和转移矩阵,提出利用Kalman滤波算法对三差观测值进行粗差及周跳的修复。实验结果证明Kalman滤波可以对含噪信号进行有效的降噪,经降噪后的信号具有更好的分布。 相似文献
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Comparing the Kalman filter with a Monte Carlo-based artificial neural network in the INS/GPS vector gravimetric system 总被引:3,自引:1,他引:3
X. Li 《Journal of Geodesy》2009,83(9):797-804
Rigorous physical and mathematical analysis has been intensively developed to obtain the gravity disturbance vector from the
inertial navigation system and the global positioning system. However, the combination of the observation noise and the systematic
INS errors make it very challenging to accurately and efficiently describe the dynamics of the system with rigorous equations.
Thus, the accuracy of the gravity disturbance estimates, especially in the horizontal components, is limited by the insufficient
error models. To overcome the difficulty of directly modeling the systematic errors with exact mathematical equations, a Monte
Carlo based artificial neural network is successfully applied in the moving base gravimetric system. The computation results
show significant improvement in the precision of all components of the gravity disturbance estimates. 相似文献
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我国卫星导航应用市场分析 总被引:2,自引:0,他引:2
分析了中国卫星导航定位产业特征,并用波特五力分析模型对卫星导航定位产业吸引力进行了分析,得出该产业是颇具吸引力的朝阳产业,该产业中的利益各方正处于从无序到有序的发展过程中的结论;指出了制约产业发展的三大问题和两大发展方向. 相似文献
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全球定位系统/航位推算组合导航定位中,由于目标运动的不确定性,GPS接收机与DR器件接收的数据存在噪声,使预置目标运动模型通常很难得到较高跟踪精度,针对应用常规卡尔曼滤波进行组合导航解算由于噪声统计特性未知而引起滤波不稳定的问题,本文提出了一种基于新息序列的量测计算进行自适应估计的卡尔曼滤波算法。该算法通过对新息方差强度进行极大似然估计,将新息计算引入卡尔曼滤波器的增益计算,达到控制发散的目的。最后对改进的算法与一般卡尔曼滤波算法做了对比仿真试验分析,结果表明了改进算法的有效性。 相似文献
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机载激光影像制图系统中的3维定位技术 总被引:4,自引:0,他引:4
介绍了利用863ADHH“机载激光影像制图系统”获得的激光测距数据,飞机GPS位置数据和姿态数据进行3维直接对地定位的一系列技术,及其在计算机视觉方面的应用^「1」。致力于解决如下3个方面的问题:1.如何解算激光测距点末端的3维坐标;2.如何解算已识别目标(高程已知)中每一点的3维坐标;3.影像存在扭曲,如何将现实空间中的直线映射成影像空间中的曲线。本文方法适用于线扫描方式与扫描方式。 相似文献
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A new estimate method is proposed, which takes advantage of the unscented transform method, thus the true mean and covariance are approximated more accurately. The new method can be applied to nonlinear systems without the linearization process necessary for the EKF, and it does not demand a Gaussian distribution of noise and what's more, its ease of implementation and more accurate estimation features enables it to demonstrate its good performance in the experiment of satellite orbit simulation. Numerical experiments show that the application of the unscented Kalman filter is more effective than the EKF. 相似文献
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ZHAO Dongming CAI Zhiwu 《地球空间信息科学学报》2006,9(4):269-272
IntroductionAs is well known,the Kal manfilter(KF) is al-ways usedto deal withthe system whose dynam-ics and observation models are linear , and theextended Kal manfilter(EKF) is the most widelyused esti mator for nonlinear systems . In theEKFthe kal man … 相似文献
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常规GPS/INS紧组合抗差自适应滤波只适用于卫星数≥4的情况,且预测残差构造自适应因子要求观测值可靠。针对该局限性,对常规抗差自适应滤波算法做出两点改进:1)采用两步滤波,用第1步常规EKF滤波残差构造第二步抗差算法的粗差判别量;2)在第2步滤波用预测残差构造自适应因子时,剔除异常观测值对应的预测残差和预测残差协方差,以削弱观测异常对自适应因子的不良影响。实验结果表明,常规抗差算法在卫星数4时不适用。常规自适应滤波算法在观测值存在异常的情况下无法正确修正模型异常。改进后的抗差自适应滤波算法在组合系统观测卫星数4且观测值存在异常的情况下,仍能正确修正观测粗差和动力学模型异常,能够达到良好的导航精度。 相似文献