共查询到20条相似文献，搜索用时 375 毫秒
1.
GPS／IPS组合定位系统中的周跳检测 总被引：2，自引：0，他引：2
全球定位系统（ＧＰＳ）和惯性导航系统（ＩＮＳ）是目前世界上最先进的导航系统。将先进的导航技术应用于精密定位，是近２０年来大地测量发展的一个重要特点。本文概述了ＧＰＳ和ＩＰＳ（惯性定位系统）各自的优缺点；指出了研究ＧＰＳ／ＩＰＳ组合定位系统的必要性。提出了利用短时期ＩＰＳ的结果来发现和修复ＧＰＳ周跳的“比对法”。 相似文献
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航空矢量重力测量误差模型的建立及初步分析 总被引：1，自引：0，他引：1
航空矢量重力测量的目的是确定沿飞行轨迹的整个重力扰动矢量。基于ＩＮＳ／ＧＰＳ的矢量重力测量,重力扰动矢量确定精度直接受ＩＮＳ测量误差和ＧＰＳ观测误差的影响。ＩＮＳ测量误差主要包括姿态测量误差和加速度计误差。影响ＧＰＳ导出加速度精度的主要误差是多路径效应和测量噪声。本文建立了航空矢量重力测量的基本误差模型,据此,对上述各项误差的成因,性质及消除措施作了初步探讨,其中重点分析了ＧＰＳ确定加速度的误差模 相似文献
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在高精级道路定位工作中，由于采用不同的全球性大地定位系统（ＧＰＳ），已发现具有一些局限性，其表现为卫星遮蔽导致的粗劣几何图形引起的运行中断事故及载波相位锁定的失败。通过把一个ＧＰＳ接受器同惯性导航系统（ＩＮＳ）相结合，也就是说，ＧＰＳ／ＩＮＳ一体化在很大程度上称补了上述不足，运用ＧＰＳ／ＩＮＳ定位的概念，其精度可以精确到厘米，同时应用ＩＮＳ可以提供有效循环位移的检验与校正方法，利用Ｇａｌｇａｒｙ附 相似文献
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GNSS/INS组合导航中,姿态解算和比力转换精度是影响精度的关键因素,且GNSS观测数据存在粗差,易对组合导航系统产生影响,针对以上问题,本文设计了一种顾及姿态解算精度的组合导航抗差算法,利用罗德里格斯公式进行姿态更新和比力转换,通过引入抗差估计理论,利用观测值和预测值的差值构造抗差因子,重新设计观测量噪声矩阵.一组跑车实验验证,抗差估计可以减弱粗差对组合导航系统的影响,提高在干扰环境下的导航性能. 相似文献
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GPS在摄影测量与遥感中应用的现状与趋势 总被引：2，自引：0，他引：2
在过去几年里，动态ＧＰＳ定位，技术及ＧＰＳ／ＩＮＳ组合技术取得了重大进展，从而极大地推动了ＧＰＳ在摄影测量与遥感中的应用。目前，ＧＰＳ在摄影测量与遥感中应用已从研究与试验阶段走向作业生产，其应用前景十分广阔。随着ＧＰＳ应用的日益广泛，它将根本改变摄影测量与遥感领域的面貌 相似文献
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针对SINS／GPS组合导航系统中卡尔曼滤波发散的情况,引入了自适应滤波和H∞滤波,分析了它们各自的特性,最后进行仿真计算,验证了这两种滤波用于SINS／GPS组合导航系统的可行性和有效性,对实际应用中组合导航系统滤波器的设计具有一定的指导意义。 相似文献
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针对级联式SINS／GPS导航系统中组合滤波器的测量噪声与系统噪声的相关问题，提出了一种相关程度未知条件下的估计量最优融合算法，并由此得到了一个顾及噪声相关性的的卡尔曼滤波新算法；通过对一套SINS／GPS数据的计算，证明了新算法是成功的。 相似文献
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针对级联式SINS／GPS导航系统中组合滤波器的测量噪声与系统噪声的相关问题，提出了一种相关程度未知条件下的估计量最优融合算法，并由此得到了一个顾及噪声相关性的卡尔曼滤波新算法；通过对一套SINS／GPS数据的计算，证明了新算法是成功的。 相似文献
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惯性／ＧＰＳ／高程匹配／景像匹配组合导航是用飞于低空空防、导地誓由 有效措施,其中景像匹配技术在组合导航中起极其重要的作用保 整个导航系统最终的命中精度。 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
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The problem of navigation systems with uncertain noise is considered. A minimax robust filtering which can minimize the worst
performance under noise uncertainties using the game theory is proposed. This new filter is applied to an integrated GPS/INS
navigation system. A high dynamics aircraft trajectory is designed to test the new filter. The results show that minimax robust
filtering performs better than standard Kalman filtering when noise parameters of an inertial measurement unit change their
statistical properties.
Received: 21 October 1997 / Accepted: 26 May 1999 相似文献
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为了系统验证SINS／GPS紧组合系统的性能,基于GPS软件接收机,进行了仿真系统构建。仿真系统由轨迹发生器、GPS中频信号模拟器、IMU信号模拟器、GPS软件接收机、SINS导航解算模块、组合滤波算法和导航性能分析模块等部分构成,其中详细设计了GPS软件接收机中的捕获和跟踪算法、SINS解算以及基于伪距和伪距率的组合滤波算法。仿真结果表明：紧组合导航系统收敛性较好,能够一定程度上抑制惯导系统误差的积累,有较好的导航性能。设计的该系统满足紧组合导航系统性能验证的需要,也为后续的超紧组合研究奠定了良好的基础。 相似文献
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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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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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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