Integrated data processing method for GPS and INS field test over Rocky Mountain

IntroductionGPS/INS integrated system exploited the INSand differential GPS pseudo-range and carrierphase technique to promote the accuracy of thedynamic platform navigation and positioning,and increase the reliability and stabilization.Incalculation, Kal…     

惯性导航与卫星导航紧耦合技术发展现状

对紧耦合方案、最优估计滤波算法和硬件开发等几方面进行了总结分析。分析了传统的INS／GPS松组合系统和INS／GPS紧耦合系统的区别;给出了INS／GPS紧耦合系统国内外的发展状况;指出INS／GPS紧耦合系统所涉及的关键技术。     

自适应调零天线技术在组合导航抗干扰中的应用

目前GPS／INS制导控制技术已成为精确制导武器的核心技术。根据GPS导航的特点及GPS／INS制导机理,对压制式干扰对GPS接收机的影响进行了分析,并分析了采用自适应调零天线技术来提高GPS／INS组合式导航抗干扰能力的有效性。     

GPS/INS组合制导技术在现代战争中的应用及发展趋势

现代战争大量使用精确制导武器,广泛应用先进的GPS／INS组合制导技术。分析了GPS／INS组合制导技术的优势,结合其在军事领域的应用,展望了GPS／INS关键技术的发展趋势。     

顾及动力学模型误差影响的GPS/INS组合导航自适应滤波算法

在顾及动力学模型随机误差的情况下,设计了一种GPS/INS自适应滤波算法.针对BP神经网络存在的训练速度慢、易陷入局部极小值等问题,对神经网络学习算法进行了改进.利用神经网络进一步减小系统误差对导航解的影响,给出了顾及动力学模型随机误差和系统误差的GPS/INS自适应滤波算法,并利用实测数据验证了算法的有效性.     

基于GPS/INS的影像实时定位与误差分析

遥感影像目标实时定位的关键是外方位元素的实时获取,在航空遥感平台上加载GPS/INS组合导航系统能够满足这一需求.文中首先给出了基于GPS/INS系统的目标直接定位方法;然后从摄影测量的角度,重点推导了影像内外方位元素对目标定位的误差传递公式;并运用实例验证了公式的有效性.     

基于GPS/INS的影像实时定位与误差分析

遥感影像目标实时定位的关键是外方位元素的实时获取,在航空遥感平台上加载GPS/INS组合导航系统能够满足这一需求。文中首先给出了基于GPS/INS系统的目标直接定位方法;然后从摄影测量的角度,重点推导了影像内外方位元素对目标定位的误差传递公式;并运用实例验证了公式的有效性。     

北斗/INS组合导航关键技术分析

组合导航系统有利于充分利用各导航系统进行信息互补与信息合作,成为导航系统发展的方向。在所有的组合导航系统中,以GPS与惯性导航系统INS组合的系统最为理想,而深组合方式是GPS与惯性导航系统（INS）组合的最优方法。鉴于GPS的不可依赖性,北斗卫星导航系统与INS的组合是组合导航系统的发展趋势,研究其组合模式具有重要意义。通过分析、评述国外INS/GPS深组合导航系统的发展现状,提出我国自主研制INS/北斗深组合导航系统需要解决的关键技术。     

一种GPS/GLONASS/INS数据融合算法

针对卫星导航系统和惯性导航系统(INS)的不同特性,提出了一种GPS/GLONASS/INS数据融合算法。采用差分自适应检测算法、改进码平均相位算法以及位置联合解算方法实现了GPS/GLONASS数据融合,借助于改进的粒子滤波器、INS误差模型建立系统状态方程和观测方程,完成GPS/GLONASS系统速度值和INS系统速度值数据融合,提高组合导航系统精度和可靠性。使用真实数据对数据融合算法性能进行仿真分析,结果表明所设计算法是有效的,能够处理非线性非高斯条件下的滤波估计,提高滤波精度和系统可靠性。     

一种迭代的GPS／INS组合导航滤波算法

简要介绍了GPS/INS松组合导航系统状态方程和观测方程。针对标准Kalman滤波算法存在的状态方程截断误差、噪声统计特性的不确定性以及状态扰动异常的影响,给出了一种应用于GPS/INS组合导航系统的迭代滤波算法。该算法采用迭代策略,不断利用观测信息实时修正状态预报值。实测数据计算结果表明,通过对状态预报值的实时修正,该算法能够很好地抑制状态预报信息的不确定性和扰动异常等对导航解的影响。其滤波解精度明显优于标准Kalman滤波。     

Integrity of an integrated GPS/INS system in the presence of slowly growing errors. Part I: A critical review

This paper (the first part of two to be published in this journal) presents the process and results of a critical review of the integrated GPS and inertial navigation system (INS) architectures, the corresponding failure modes and the existing integrity monitoring methods. The paper concludes that tightly coupled GPS/INS systems have the highest potential for detecting slowly growing errors (SGEs). This is due to access to pseudorange measurements and a relatively simpler configuration compared to the other architectures. The second paper (Part II) takes this further and carries out a detailed characterisation of the performance of the current integrity algorithms for tightly coupled systems and develops a new algorithm that detects SGEs faster than the current methods.     

UKF在GPS/INS组合导航系统中的应用

扩展卡尔曼滤波(EKF)是GPS/INS组合导航系统工程实现中常用的一种数据融合方式。但EKF线性化误差在一定程度上影响了GPS/INS组合导航系统精度的提高。Unscented卡尔曼滤波器(UKF)是一种非线性滤波器,它能有效地减小线性化误差对GPS/INS组合导航系统精度的影响。基于四元数法建立了GPS/INS组合导航系统的非线性误差方程模型;最后通过数字仿真验证了UKF组合导航系统应用中的性能。     

基于高阶AR模型的陀螺随机漂移模型

陀螺随机漂移是影响惯性导航和组合导航精度的重要因素。首先用周期函数拟合和小波变换两种方法分别对随机漂移中的周期噪声进行分析和处理;然后对相关噪声建立高阶AR模型;最后将该模型应用在GPS／INS组合导航Kalman滤波中,并对结果进行分析和比较。结果表明,相比于周期函数拟合,小渡变换不仅能够更好地削弱周期噪声的影响,还能够削弱高频白噪声的影响;相比于一阶马尔科夫过程,高阶AR模型能够更好地描述随机漂移中的相关噪声;基于小渡变换和高阶AR模型的GPS／INS组合导航具有明显的优势。     

基于小波阈值消噪自适应滤波的GPS/INS组合导航

惯导系统中惯性元件误差是影响惯性导航及其组合导航精度的重要因素.在GPS/INS组合导航自适应滤波的基础之上,提出利用小波进行阈值消噪以提高组合导航精度.首先对惯性元件输出信号进行频谱分析,确定相应的多分辨分析尺度以及不同尺度下对高频系数采取的相应措施.对噪声占主要成分的尺度将其高频系数全部置零,对噪声和有用信号共同占有的尺度将其高频系数作阈值处理.利用实测数据进行验证,结果表明这种方法能够有效地削弱惯性元件误差的影响,提高GPS/INS组合导航系统的精度和可靠性.     

卡尔曼滤波与H∞滤波在INS／GPS组合导航中的应用

组合导航利用惯性导航（INS）和全球定位系统（GPS）较强的非相似性和互补性,将两者组合,可以取长补短,充分发挥各自的优点,提高导航系统性能。利用卡尔曼滤波能够有效提高其精度,但卡尔曼滤波的应用要求函数模型和随机模型已知,符合实际,这在实际应用中是很难保证的,一般都是通过经验信息确定。H滤波则具有很强的鲁棒性,抗干扰性强。通过仿真数据处理,结果表明：H滤波比卡尔曼滤波在噪声特性未知时更适用,精度更高。     

Design of minimax robust filtering for an integrated GPS/INS system

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     

基于Kalman滤波的动力学模型误差估计算法

本文分析介绍了模型误差对滤波解和预报残差影响的表达式.随后,针对GPS/INS松组合导航系统观测信息无冗余的情况,给出了基于Kalman滤波的动力学模型误差估计算法.最后利用一个车载实测数据证明了算法的有效性.     

GPS/MEMS INS integrated system for navigation in urban areas

This paper evaluates the performance of a tightly coupled GPS/INS integrated system based on low cost MEMS IMUs in dense urban areas, and investigates two different methods to improve its performance. The first method used is to derive observations from two different constraint equations reflecting the behavior of a typical land vehicle. The first constraint equation is derived assuming that the vehicle does not slip and always remains in contact with the ground. If these assumptions are true the velocity of the vehicle in the plane perpendicular to the forward direction should be zero. The second constraint equation is derived from the fact that the height does not change much in a short time interval in a land vehicular environment. Thus, when a GPS outage occurs (partial/complete), the integrated system combines the INS and constraints-derived virtual measurements to keep the position and velocity errors bounded. This method is suitable for use in real-time applications. The second method is specifically suitable for a post-mission application and involves the use of Rauch-Tung-Striebel (RTS) smoother. The designed system performance is evaluated using two data sets collected in dense urban areas. The obtained results demonstrate the effectiveness of different algorithms considered, in controlling the INS error growth, and indicates the potential of MEMS IMUs for use in land vehicle navigation applications.     

神经网络辅助的GPS/INS组合导航故障检测算法

针对GPS/INS松组合导航系统观测信息无冗余,而且观测信息可能存在故障的情形,提出一种神经网络辅助的组合导航故障检测算法。该算法克服了基于模型的故障检测算法受模型误差影响的局限性;能够自动地对观测信息进行故障的检测、定位和剔除;能够基于故障检测后可靠的观测信息进一步调整动力学模型信息对导航解的贡献;能够在GPS失锁时,较好地进行导航预报。最后利用车载实测数据进行验证,结果表明该算法能够很好地从模型误差中分离出观测信息含有的故障信息,降低了故障检测算法存在的虚警率,避免故障信息对导航解的影响;且GPS失锁时,神经网络的预报输出在一定程度上能够进一步提高导航解的精度。     

Adaptive Kalman Filtering for INS/GPS

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