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.     

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。     

基于惯性鞋载传感器的人员自主定位粒子滤波方法

为了解决卫星导航信号被遮挡条件下定位问题,该文提出一种基于惯性鞋载传感器的高精度人员自主定位方法。该方法通过经典的拓展卡尔曼滤波辅助的零速更新(ZUPT-aided EKF)算法解算鞋尖惯性测量数据得到人员的初步运动轨迹,并创新性地提出一种粒子滤波框架下利用建筑物结构先验知识对轨迹进行修正的方法。根据大多数建筑物的结构,将行走平面划分为8个方向,包含4个主方向(走廊朝向)和4个辅助方向。根据粒子偏离8方向的程度按照高斯函数对粒子的权值进行更新,并用剩余重采样的方法避免了粒子的退化。实测数据验证了该文提出的方法,结果表明:该方法比轨迹修正前和传统轨迹修正的方法有更好的精度,在861 m的复杂轨迹下定位误差仅为2.7 m,定位精度优于0.5%;同时该方法有较好的一致性,不同楼层间的行走定位误差保持在2 m内, 可以进行稳定持续地定位。     

GNSS/INS组合导航在飞行校验系统中的应用

目前的飞行校验系统以 GPS 作为空间基准,这种定位方式在精度、性能、可靠性方面都存在着固有缺陷。本文介绍的同步定位-姿态-导航(SPAN,Synchronous Position,Attitude and Navigation)技术集合了两种不同但互补的技术：全球导航卫星系统(GNSS,Global Navigation Satellite System)定位技术和惯性导航技术。通过二者的结合,SPAN 技术可以达到取长补短的目的,组合后的导航系统无论在精度、性能、可靠性方面都优于单独的子系统。将这项技术应用于飞行校验系统,利用 GNSS 定位的绝对精度加上惯性测量单元(IMU)陀螺和加速计测量的稳定性,就可以在卫星信号丢失或者高动态应用环境下仍然提供持续、稳定、可靠的位置及姿态数据。     

Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications

The relatively high cost of inertial navigation systems (INSs) has been preventing their integration with global positioning systems (GPSs) for land-vehicle applications. Inertial sensors based on microelectromechanical system (MEMS) technology have recently become commercially available at lower costs. These relatively lower cost inertial sensors have the potential to allow the development of an affordable GPS-aided INS (INS/GPS) vehicular navigation system. While MEMS-based INS is inherently immune to signal jamming, spoofing, and blockage vulnerabilities (as opposed to GPS), the performance of MEMS-based gyroscopes and accelerometers is significantly affected by complex error characteristics that are stochastic in nature. To improve the overall performance of MEMS-based INS/GPS, this paper proposes the following two-tier approach at different levels: 1) improving the stochastic modeling of MEMS-based inertial sensor errors using autoregressive processes at the raw measurement level and 2) enhancing the positioning accuracy during GPS outages by nonlinear modeling of INS position errors at the information fusion level using neuro-fuzzy (NF) modules, which are augmented in the Kalman filtering INS/GPS integration. Experimental road tests involving a MEMS-based INS were performed, which validated the efficacy of the proposed methods on several trajectories.      

GPS/DR组合导航性能增强技术

为了提高全球定位系统(GPS)信号短时中断时地面车辆自主导航精确度,提出了GPS信号中断时采用车辆不完全约束条件和里程仪速度信息作为量测,辅助惯性导航系统实现车辆航位推算(DR)自主导航的方案;推导了该方案的GPS/DR组合导航的卡尔曼滤波方程;并进行了计算机仿真研究和地面车载试验,结果显示GPS信号中断90 s,DR自主导航误差为20 m,能够满足部分地面车辆短时高精确度自主定位要求。     

基于联邦卡尔曼滤波算法的水面无人作业船导航精度研究

为了实现无人作业船的自动导航,搭建了以Trimble BD982 GNSS系统为核心的差分定位系统。通过试验及分析得出了该系统的定位精度。为了进一步提高系统的导航精度,引入了联邦卡尔曼滤波算法,设计了基于航向姿态参考系统(AHRS)与GPS结合导航模式的卡尔曼滤波器。通过仿真试验表明该滤波器能明显提升系统的导航精度。     

Use of Deeply Integrated GPS/INS Architecture and Laser Scanners for the Identification of Multipath Reflections in Urban Environments

This paper investigates the constructive use of multipath reflections of Global Positioning System (GPS) signals for navigation in urban environments. Urban navigation applications are generally characterized by a significant presence of multipath signals. In order to maintain reliable and accurate navigation capabilities, it is critical to distinguish between direct signal and multipath. At the same time, multipath reflections can be exploited as additional measurements for those cases where the number of direct path satellites is insufficient to compute the navigation solution. The paper develops a method for the identification of multipath reflections in received satellite signals: i.e., multipath is separated from direct signal and a line-of-site between the GPS receiver and a multipath reflecting object is determined. Once multipath reflections are identified, they can be used constructively for navigation. The method presented in the paper exploits an open-loop batch-processing GPS receiver, laser scanner and inertial navigation system (INS) to identify multipath reflections in received satellite signals. Experimental GPS, inertial and laser scanner data collected in real urban environments are applied to demonstrate identification of multipath reflections.     

基于改进神经网络增强自适应UKF的组合导航系统

基于微机电系统(MEMS)的惯性器件和全球定位系统(GPS)的组合导航系统在卫星信号失锁时存在误差发散的问题,该文提出一种基于人工蜂群算法(ABC)改进的径向基函数(RBF)神经网络增强改进的自适应无迹卡尔曼滤波算法(AUKF)。在GPS信号失锁的情况下利用训练好的神经网络输出预测信息来对捷联惯导系统进行误差校正。最后通过车载半实物仿真实验验证该方法的性能。实验结果表明该方法在失锁情况下对于捷联惯导系统的误差发散有较为明显的抑制效果。     

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。     

Malicious Traffic classification Using Long Short-Term Memory (LSTM) Model

Malicious traffic classification is the initial and primary step for any network-based security systems. This traffic classification systems include behavior-based anomaly detection system and Intrusion Detection System. Existing methods always relies on the conventional techniques and process the data in the fixed sequence, which may leads to performance issues. Furthermore, conventional techniques require proper annotation to process the volumetric data. Relying on the data annotation for efficient traffic classification may leads to network loops and bandwidth issues within the network. To address the above-mentioned issues, this paper presents a novel solution based on artificial intelligence perspective. The key idea of this paper is to propose a novel malicious classification system using Long Short-Term Memory (LSTM) model. To validate the efficiency of the proposed model, an experimental setup along with experimental validation is carried out. From the experimental results, it is proven that the proposed model is better in terms of accuracy, throughput when compared to the state-of-the-art models. Further, the accuracy of the proposed model outperforms the existing state of the art models with increase in 5% and overall 99.5% in accuracy.

     

Bayesian Surface and Underwater Navigation

A common framework for maritime surface and underwater (UW) map-aided navigation is proposed as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a distance measuring equipment (DME) which is compared with the information obtained from various databases. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. For the described system, the fundamental navigation performance expressed as the CramÉr–Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. Two detailed examples of different navigation applications are discussed: surface navigation using a radar sensor and a digital sea chart and UW navigation using a sonar sensor and a depth database. In extensive Monte Carlo simulations, the performance is shown to be close to the CRLB. The estimation performance for the surface navigation application is in comparison with usual GPS performance. Experimental data are also successfully applied to the UW application.     

卫星导航系统上行注入的研究现状与发展趋势

上行注入系统是卫星导航系统的生命线,是卫星导航系统不可或缺的一部分。详细研究了国外全球卫星导航系统和区域卫星导航系统的上行注入方案：全球定位系统（GPS）正由GPS II阶段过渡到GPS III阶段,上行注入也从全球布站的单星直接注入方案转变为本土布站的基于星间链路转发的注入方案,“伽利略”（Galileo）采取了全球布站的单星直接注入方案,而俄罗斯全球导航卫星系统（GLONASS）、准天顶卫星导航系统(QZSS)和印度区域导航卫星系统(IRNSS)采取了本土布站的单星直接注入方案。随着各大全球系统逐步研究、规划和开发星间链路,基于星间链路转发的上行注入方案势必成为全球系统的上行注入方案发展趋势,链路协同注入和高速传输也必然成为该方案的关键技术。     

基于北斗卫星导航系统非组合精密单点定位算法的精密授时精度研究

全球导航卫星系统(GNSS)精密单点定位(PPP)技术具有操作简单、成本低廉、定位精度高等优点，在精密授时领域得到了广泛应用。针对现有研究中消电离层PPP模型的组合噪声大、卫星系统多采用全球定位系统(GPS)及实时动态场景少等问题，该文采用非组合PPP模型对北斗卫星导航系统(BDS)精密授时精度开展了研究，并利用Kalman滤波静态模型和动态加速模型进行参数估计。给出了静态和实时动态场景PPP处理策略，并利用超快速预报星历保证动态实时性。结果表明:在静态和实时动态条件下，非组合PPP模型精密授时精度均优于消电离层组合PPP模型；在静态条件下，考虑到BDS和GPS定位精度相当，且亚太地区BDS定位精度更高，BDS授时精度略高于GPS；在实时动态条件下，BDS和GPS精密授时精度在2 ns以内，但BDS授时精度略低于GPS，这是由于空间位置精度因子(PDOP)、载体速度突变和环境因素等的影响，而当完成收敛后，两种系统的授时精度相当，其钟差项中误差均在0.3 m以内。     

城市峡谷中基于GDOP和NHC的GPS/IMU组合导航方法

针对在林荫道、高架桥和隧道等城市峡谷中全球定位系统(GPS)信号易发生遮挡、 干扰,导致导航定位精度低、连续性和可靠性差的问题,提出了一种基于几何精度因子(GDOP) 和非完整性约束(NHC)的 GPS/惯性测量单元(IMU)组合导航方法。其中,通过利用 GDOP 实现卫星定位构型优劣和定位精度的衡量,从而构建基于 GDOP 值的 GPS/IMU 组合导航量测更新模型。同时,为了进一步提高组合导航系统的精度,将 NHC 应用于该组合导航系统中。实验结果表明,所提方法水平和三维定位均方根误差分别是 3.88 m 和 4.78 m,相比于传统 GPS/IMU 组合导航方法的 9.25 m 和 13.53 m 分别提高了 58.06%和 64.67%,可以有效抑制 GPS 信号受环境干扰影响组合导航定位精度和可靠性的问题。     

BDS/GPS组合导航RAIM可用性分析

采用RINEX数据实验比较了北斗卫星导航系统(BDS)和全球定位系统(GPS)电离层Klobuchar模型在BDS/GPS组合导航中的性能。选择相对较优的电离层模型分析了基于卫星星座类型和卫星类型两种加权情况下,单一北斗、单一GPS与BDS/GPS组合导航系统在非精密进近(NPA)阶段和一类垂直引导进近(APVⅠ)阶段的RAIM可用性。实验表明,在BDS/GPS组合导航中,BDS的Klobuchar模型在中国区域内能修正大量的电离层误差,相比于GPS的Klobuchar电离层模型,电离层延迟修正有0-3.4364 m左右的提高。对于两种加权RAIM算法,在NPA阶段,单一导航RAIM可用性为90%左右,BDS/GPS组合导航的RAIM可用性达到100%。而在APVⅠ阶段,单一导航不具备APVⅠ能力,组合导航APV I阶段RAIM可用性达到100%,具有提供APV I应用的能力。      

一种改进的视觉/IMU导航系统初始化方法

针对基于单目相机和惯性测量单元(IMU)组成的视觉导航系统需要精确初始化的问题,该文提出了一种减少处理时间同时提高精度的系统初始化方法。首先,对采集数据中的陀螺仪偏置进行单独估计并分离,实现估计陀螺仪偏置和求解系统初始化封闭解问题的解耦;然后,采用基于紧耦合的线性模型求取初始化问题的封闭解;最后,将所提出的改进初始化方法应用于经典的VinsMono框架中。实验表明,改进方法可有效提高视觉惯性导航系统的初始化精度和效率。     

An improved location service scheme in urban environments with the combination of GPS and mobile stations

Satellite‐based apositioning and navigation technology in urban environments is widely studied. As a solution to GPS signal degradation in urban environment, the wireless network‐aided location scheme has been put into practice, which benefits our everyday life. However, there still exist many potential advantageous points that can be exploited to improve the performance of the location service especially in urban environments. This paper proposes a GPS‐based positioning and navigation with the aid of wireless network (GPONAWIN) positioning scheme that utilizes the existing resources of wireless communication networks to provide cheaper and more accurate urban positioning service with higher survivability. After the proposed scheme is described in detail, outperformances over the existing scheme are also analyzed. The GPONAWIN scheme can achieve higher information utilization efficiency, less wireless channel expenditure, more accurate positioning, better expandability to the multiple satellite positioning and navigation systems, and lower cost. Copyright © 2012 John Wiley & Sons, Ltd.     

Modified Unscented Kalman Filter for a Multirate INS/GPS Integrated Navigation System

Instead of the extended Kalman filter, the unscented Kalman filter (UKF) has been used in nonlinear systems without initial accurate state estimates over the last decade because the UKF is robust against large initial estimation errors. However, in a multirate integrated system, such as an inertial navigation system (INS)/Global Positioning System (GPS) integrated navigation system, it is difficult to implement a UKF‐based navigation algorithm in a low‐grade or mid‐grade microcontroller, owing to a large computational burden. To overcome this problem, this letter proposes a modified UKF that has a reduced computational burden based on the basic idea that the change of probability distribution for the state variables between measurement updates is small in a multirate INS/GPS integrated navigation filter. The performance of the modified UKF is verified through numerical simulations.     

基于北斗RDSS的GPS区域差分技术

本文提出了一种基于北斗RDSS的GPS区域差分技术的实现方案,该方案克服了差分GPS技术受到参考站和用户站之间距离的限制。本系统中基准型GPS接收机伪距修正数的计算、用户接收机导航解算所需的卫星星历数据、时间数据、伪距信息和伪距修正数的提取以及差分算法实现和接收机定位算法全部在DSP＋FPGA中实现。通过试验数据分析比较可见,采用基于北斗RDSS的GPS区域差分技术,对GPS导航定位精度有明显提高,该技术为未来实现高精度GPS导航定位有一定的参考价值。