基于条纹匹配的InSAR/INS组合导航方法

现存的组合导航系统存在诸多问题：地形辅助导航系统分辨率较低;GPS/INS导航系统中GPS信号易受干扰;SAR/INS导航系统无法实现三维定位且无法获得平台的姿态信息.针对以上问题本文提出了基于条纹匹配的InSAR/INS组合导航方法：该方法将InSAR系统获得的干涉条纹与DEM生成的干涉条纹进行匹配,得到的定位偏移用以反演平台的位置和姿态信息,最后将反演结果与IMU信息进行组合滤波得到导航输出.该组合导航系统有以下优势：干涉条纹中包含地形信息和平台姿态信息;干涉相位对横滚角敏感,可通过干涉相位高精度反演平台的横滚角;InSAR系统具有较高精度的三维定位能力.本文主要介绍了基于条纹匹配的InSAR/INS组合导航的原理和方法,最后通过仿真和实测数据验证了条纹匹配和观测量反演算法的可行性.     

基于BP神经网络辅助的车载组合导航算法研究

针对INS/GPS组合导航系统在GPS信号被遮挡时,GPS接收机失锁导致导航精度迅速下降的问题,提出了基于BP神经网络辅助的组合导航算法。即在GPS信号锁定的时候,采用卡尔曼滤波对INS/GPS信号进行数据融合得到实时的精确位置,同时利用组合导航输出信息对BP神经网络进行实时在线训练;一旦GPS失锁,利用之前训练好的神经网络对INS系统进行误差补偿,解决精度迅速下降问题。通过跑车实验证明,速度精度在0.2m/s以内,位置精度为25m以内,该算法对INS/GPS组合导航系统有效。     

基于GPS/GIS/DR的低成本车载导航仪研究

惯性器件与全球卫星定位系统(GPS)的组合导航成为目前车载导航的主流.无论在精度、性能、可靠性等各方面,GPS/DR组合导航系统都优于单独的GPS导航系统.在GPS信号丢失时,车载导航仪(GPS/GIS/DR)能利用陀螺自主导航,不间断提供导航信息并保持跟踪.     

一种改进EKF的双模导航系统的定位算法

针对提高GPS/BDS双模导航系统的伪距单点定位精度问题,研究了GPS/BDS双模导航系统定位的解算方法,并对组合系统构建数学模型,引入扩展卡尔曼滤波(EKF)器进行定位解算,初始化权矩阵时通过牛顿迭代法改进矩阵求逆运算,并通过使用分段函数改进EKF中的权矩阵P,提出一种基于EKF的GPS/BDS导航定位解算方法。理论分析和实验结果表明,通过对比单GPS系统和最小二乘解算方法,利用双模系统并通过EKF能将定位精度在水平方向提高10%,高程方向提高43%,并极大地减小了运算量,提高系统的可靠性和完好性。     

伽利略和GPS卫星导航系统几何精度因子比较分析

欧洲独立开发的新一代全球卫星导航系统一一伽利略系统定于2005年发送导航信号，2008年全部建成使用。这对于广大导航用户是一个好消息，伽利略系统对GPS系统是个有力的补充和冲击。本文将从导航卫星定位的重要参数——几何精度因子(GDOP)分析出发，比较两个系统在中国地区的定位性能，仿真结果表明伽利略系统具有与GPS系统相媲美的定位精度．这样，当发生紧急情况时，如果GPS不可用，伽利略系统就是一个很好的替代系统。     

基于北斗的多系统组合定位方法研究

基于多系统组合的导航定位技术应用日益广泛,采用多系统组合导航克服了单一星座系统定位中存在的定位精度差、可见星少、可靠性低等问题。本文提出了基于北斗卫星导航系统的多系统组合定位方法,详细推导了基于北斗的多系统组合用户定位算法模型。比较分析了单一北斗卫星导航系统和北斗、GPS、Galileo 多系统组合条件下服务性能,得出在多系统组合条件下可以极大改善用户观测条件,进而提高了用户导航定位使用效能。     

一种简化的车载自适应组合导航算法

为解决复杂路况下车载组合导航系统存在的卫星导航系统信号衰弱、断续导致信号观测性差和组合滤波器稳定性下降甚至发散等问题,采用了一种简化的、易于工程实际应用的车载自适应组合导航算法,利用数据检测方法对卫导原始观测数据进行评估,根据评估结果构造自适应滤波因子,实时更新滤波器量测噪声协方差阵,提高滤波器对观测信息变化的适应能力。通过实际动态跑车试验,表明这种简化的自适应组合导航算法在卫导信号断续情况下,仍能保证3 m(RMS)的定位精度、0.04 m/s(RMS)的测速精度,较常规Kalman滤波定位精度提高近30%,测速精度提高达70%,能满足城市、山区等恶劣场景下车载导航的需求。     

基于自适应卡尔曼滤波的RFID/SINS组合导航研究

在室内导航定位中,射频识别(Radio Frequency Identification, RFID)技术具有信号穿透性强、成本低廉等诸多优点,能够有效代替GPS完成室内组合导航。针对室内惯性导航误差发散和滤波中噪声参数不确定的问题,提出了基于自适应卡尔曼滤波(Adaptive Kalman Filtering, AKF)的RFID/SINS组合导航系统,通过RFID定位系统抑制惯性导航误差发散,并应用AKF将噪声参数与量测输出参数关联实现实时更新。对AKF和标准卡尔曼滤波(Kalman Filtering, KF)下的RFID/SINS组合导航系统进行了仿真和实验。结果表明,在AKF下组合导航系统平均定位误差降低了10%,位置稳定性提升了7.4%,定位误差保持在0.07 m左右。基于AKF的RFID/SINS组合导航系统能够满足室内高精度定位导航的需求。     

低轨卫星定位的导航卫星可见性与GDOP分析

针对全球卫星导航系统对低轨卫星定位的动态观测几何问题,对于不同低轨卫星轨道高度、不同截止高度角和不同卫星导航系统的情形,进行了导航卫星可见性分析,并在此基础上分析了几何精度因子（GDOP）。低轨卫星高度在几百km内变化时对可见性与几何精度因子的影响很小,而不同截止高度角则影响较大;另外与采用单个GPS系统相比,采用GPS—Galileo组合卫星导航系统对低轨卫星定位时,可见导航卫星数目明显增加,GDOP数值减小,且即使在截止高度角较大时也能得到较好的GDOP。     

基于DSP的Galileo/GPS联合导航定点算法研究

文章对比了GPS、Galileo及Galileo/GPS联合导航系统的性能,研究了基于最小二乘单点定位定点解算工程实现算法。仿真结果表明,Galileo/GPS联合导航较独立系统可见星数目、GDOP值及定位精度有明显的改善和提高。通过TMS320C6416 DSP硬件平台测试表明,研究的定点解算算法较浮点解算算法定位精度变化很小,具有较快的解算处理时间,为Galileo/GPS联合导航的实际应用打下了基础。     

GPS与DTMB组合定位系统的改进选星算法研究

针对GPS卫星信号在楼群密集的城市和室内存在定位盲区而无法单独完成定位的难题,提出GPS-DTMB组合导航定位方法。在多源信号组合导航定位系统中,为了解决导航定位精度与运算复杂度间的矛盾,研究改进的加权行列式选星算法在GPS与DTMB组合定位系统中的可行性,与传统最小GDOP选星算法相比较,改进的选星算法具有运算复杂度低、计算消耗时间短的优点,并且对比分析在不同的组合卫星数目中,该算法与直接利用传统最小GDOP选星算法相比较的偏差大小,仿真表明,在15种组合卫星中,偏差小于0.1的概率在90%左右,能够得到满意的性能,证明了此方法的实用性。     

机载航电系统中多源信息模糊自适应融合技术

为了保障导航的精度与可靠性,目前常采用多源信息融合导航。针对ADS/IRS/GPS组合导航系统,采用基于自适应信息分配的联邦滤波结构处理信息融合问题,并把模糊推理技术融入到ADS/IRS卡尔曼滤波器中,对IRS/GPS子滤波器采用紧组合模式,上述结构算法能对多源导航信息进行最优融合与处理。建立了ADS/IRS模糊自适应卡尔曼滤波模型以及IRS/GPS紧组合滤波模型,设计了自适应信息分配的联邦滤波算法,并进行了仿真,仿真结果验证了设计算法的有效性。     

空地制导弹药的MIMU/GPS组合导航系统研究

针对空地精确制导弹药对低成本、高精度和高可靠性导航系统的需求,设计了MIMU/GPS组合导航系统.该系统采用了MEMS的IMU和GPS进行组合,利用位置、速度组合模式,采用渐消自适应Kalman滤波器估计并修正惯导的误差.通过地面跑车试验进行了验证,试验结果表明:组合导航系统误差得到了有效的抑制,导航精度满足空地制导弹药的导航要求.     

BDS/GPS组合导航系统选星算法研究

针对双系统组合导航模式下常用的最佳选星法存在运算量大、硬件设计复杂、实时性差等缺点,提出了基于 BDS/GPS 双系统组合导航模式的模糊选星算法。首先说明了靶场试验中采用 BDS/GPS 双系统组合导航模式的必要性,然后对组合导航系统定位精度的关键影响因子、 可用星数量及选择方式进行了分析,并给出了基于 BDS/GPS 双系统组合导航模式的模糊选星算法的详细解算步骤。实验结果表明,该算法简单易行,定位精度高,实时性好,对靶场试验品的导航定位具有重要参考价值。     

车载GPS/DR组合导航系统数据融合算法研究

本文介绍了车载ＧＰＳ／ＤＲ组合导航系统的设计,建立了表示车辆加速度及影响组合导航系统定位精度的主要误差的数学模型。针对迭代扩展组合卡尔曼滤波算法中,由于ＤＲ系统误差的引入导致滤波效果不好的问题,提出了一种新的组合导航系统的数据融合算法。对实际跑车数据的处理结果表明,该算法在提高组合导航系统的定位精度及可靠性和完整性方面是有效的。     

Promoting navigation system efficiency during GPS outage via cascaded neural networks: A novel AI based approach

Acquiring precise navigation data is a vital process to unmanned vehicles. Although Inertial Navigation System and Global Positioning System (INS/GPS) integrated system provides accurate and continuous navigation solution, the navigation solution accuracy degrades during GPS outages. In order to provide accurate and continuous navigation data during GPS outages, a novel architecture of a cascaded neural networks is proposed to estimate velocity and position errors during GPS signal blockage to handle the time dependency and non-linearity modeling. An ablation study is conducted to grasp the proposed model hyper parameters and their impacts on overall accuracy. Various scenarios are carried out by building and using different grades IMU models. These models are examined through two distinct trajectories for flying and ground platforms to assure the proposed algorithm applicability scope and efficiency. Furthermore, a comparative analysis is carried out based on a real-field test to evaluate the proposed algorithm efficiency and navigation accuracy during GPS outages. The proposed system proves its superiority against the legacy Extended Kalman Filter (EKF) and the advanced Recurrent Neural Network (RNN) based systems in terms of navigation performance during GPS outages periods.     

A Low-Order DGPS-Based Vehicle Positioning System Under Urban Environment

The vehicle positioning system is a key component in functions such as vehicle guidance, driver alert and assistance, and vehicle automation. Since installing a low-cost global positioning system (GPS) or inertial navigation system (INS) unit is becoming a common practice in vehicle applications, its involvement in vehicle guidance and vehicle safety deserves a closer investigation. Typical vehicle applications require high reliability, low cost, and sufficient accuracy under all operational conditions. For GPS-based positioning, urban driving with its complicated maneuvers, frequent GPS blockage, and multipath, are some of the most difficult driving environments. This paper explores the feasibility of a low-order vehicle positioning system functioning under an urban environment. The equipped vehicle has a midrange differential GPS (DGPS) unit and few relatively simple in-vehicle sensors. A low-order integration is explored by utilizing a vehicle model-based extended Kalman filter (EKF) to incorporate in-vehicle motion sensors and to largely avoid direct integration of INS signals. Further, the characteristics of DGPS measurements under urban environments are investigated, and novel DGPS noise processing techniques are proposed to reduce the chances of exposing the EKF to undesirable DGPS measurements due to common DGPS problems such as blockage and multipath. A resulting fourth order EKF based positioning system is successfully implemented in the test vehicle to demonstrate the feasibility of the proposed design. Experimental results illustrate the ability of the system to meet the accuracy and robustness requirements in the presence of blockage and multipath under a typical urban driving environment.     

A Bimodal Approach for Land Vehicle Localization

In this paper, we present a novel idea to integrate a low cost inertial measurement unit (IMU) and Global Positioning System (GPS) for land vehicle localization. By taking advantage of positioning data calculated from an image based on photogrammetry and stereo‐vision techniques, errors caused by a GPS outage for land vehicle localization were significantly reduced in the proposed bimodal approach. More specifically, positioning data from the photogrammetric approach are fed back into the Kalman filter to reduce and compensate for IMU errors and improve the performance. Experimental results are presented to show the robustness of the proposed method, which can be used to reduce positioning errors caused by a low cost IMU when a GPS signal is not available in urban areas.     

Smart framework for GNSS‐based navigation in urban environments

Intelligent Transport System applications require accurate and reliable positioning. When stand‐alone global positioning system (GPS) is used in urban areas, the results dramatically degrade, because of signal outages and multipath. This paper unveils new signal processing techniques for carrier phase‐based navigation in urban environments. The techniques identify multipath or weak signals using carrier phase based receiver autonomous integrity monitoring, and abnormal signals and situational measurements are eliminated or estimated. To estimate the carrier phase during a short time period when GPS signals is blocked, this paper uses carrier phase statistics. The performance of the proposed method is verified, through a car test. The test environment has many signal outages and multipath because of high buildings. Many abnormal signal conditions occurred during the test, and the results confirmed that the proposed method performed better than the basic stand‐alone GPS approach when compared with GPS/inertial navigation system (INS) integrated navigation results. Copyright © 2015 John Wiley & Sons, Ltd.