A Map-Matching Algorithm forGPS/DR Integrated Navigation Systems Basedon Dempster-Shafer Evidence Reasoning

GPS (Global Positioning System) has been widely used in car navigation systems. Most car navigation systems estimate the car position from GPS and DR (dead reckoning). However, the unknown GPS noise characteristic and the unbounded DR accumulation of errors over time make the position information with undesirable position errors. The map matching can improve the position accuracy and availability of the vehicular position system. In this paper, general principle of map matching is investigated according to segmentation and feature extraction, and a map matching algorithm based on D-S (Dempster-Shafer) evidence reasoning for GPS integrated navigation system is proposed, which can find the exact road on which a car moves. For the experiments, a car navigation system is developed with some sensors and the field test demonstrates the effectiveness and applicability of the algorithm for the car location and navigation.

A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

DESIGN & REALIZATION ON GPS/INS INTEGRATED NAVIGATION SYSTEM

讨论了应用卡尔曼滤波器的ＧＰＳ／ＩＮＳ组合导航系统，其中包括理论、方案与实际的ＩＮＳ和ＧＰＳ接收机的结合，研究的重点是对系统误差的建模及组合的实现，以及松组合和紧组合等各种组合方式等。该ＧＰＳ／ＩＮＳ组合导航开发系统，可以有效和经济地用于组合导航系统的研究和设计，在实际应用中起到了非常有效的作用。该系统不仅仅是一个计算机辅助设计软件，而且可以对实际获得的惯导和ＧＰＳ数据进行半物理仿真。开发系统的核心软件可以方便地转换成实际组合系统中的工程应用软件。应用结果表明，开发系统的设计思想和组合方案是成功的，可以取得良好的导航结果。

SINS/CNS/GPS integrated navigation algorithm based on UKF

A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter （UKF） to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle＇s attitude and has less variables than the quaternion. Also, the UKF method is simplified to estimate the system error model, which can both lead to less calculation and reduce algorithm implement time. In the information fusion section, a modified federated Kalman filter is proposed to solve the singular covariance problem. Specifically, the new algorithm is applied to maneuvering vehicles, and simulation results show that this algorithm is more accurate than the linear integrated navigation algorithm.

IAE-adaptive Kalman filter for INS/GPS integrated navigation system

A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter （SKF） assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering （IAE-AKF） algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAEAKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra- ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

GPS/VISNAV integrated relative navigation and attitude determination system for ultra-close spacecraft formation flying

For the improvement of accuracy and better fault-tolerant performance, a global position system （GPS）/vision navigation （VISNAV） integrated relative navigation and attitude determination approach is presented for ultra-close spacecraft formation flying. Onboard GPS and VISNAV system are adopted and a federal Kalman filter architecture is used for the total navigation system design. Simulation results indicate that the integrated system can provide a total improvement of relative navigation and attitude estimation performance in accuracy and fault-tolerance.

Accuracy improvement of GPS/MEMS-INS integrated navigation system during GPS signal outage for land vehicle navigation

To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.

Application of Adaptive Kalman Filtering Algorithm in IMU/GPS Integrated Navigation System

The IMU(inertial measurement unit) error equations in the earth fixed coordinates are introduced firstly. A fading Kalman filtering is simply introduced and its shortcomings are analyzed, then an adaptive filtering is applied in IMU/GPS integrated navigation system, in which the adaptive factor is replaced by the fading factor. A practical example is given. The results prove that the adaptive filter combined with the fading factor is valid and reliable when applied in IMU/GPS integrated navigation system.

APPLYINGSUCCESSIVEORTHOGONALIZATIONDECENTRALIZEDKALMANFILTERTOGPS／INSINTEGRATEDNAVIGATIONSYSTEM

逐次正交化分布式卡尔曼滤波器是对大系统进行状态估计的一种新方法。本文讨论了在GPS／INS组合导航系统中，应用逐次正交化分布式卡尔曼滤波器的原理和特点。首先，将组合导航系统分成三个子系统，然后采用开环和闭环校正相结合的方法，用两种组合方式对系统进行仿真。结果表明：本方法是有效并可实现的；在三个子系统串行运算的条件下，实际提高运行速度约两倍，并且保持了最优精度。本文所得的结论对于由多个实际子导航系统，如INS，GPS，地形匹配，罗兰C，多普勒雷达。

An Integrated Air Data / GPS Navigation System for Helicopters

In this study, the integration of two navigation systems Air Data System (ADS) and Global Positioning System (GPS) was aimed. ADS is a widely used navigation system which measures static and total air pressure and the air temperature. ADS has high sampling frequency and poor accuracy, on the other hand, another navigation system GPS has high accuracy compared to ADS but lower sampling frequency.Kalman Filter is used to integrate and minimize the errors of the two navigation systems. By this integration a navigation system with high sampling frequency and high accuracy is aimed. Another object is to calculate the wind speed with high accuracy.

INS-GNSS Integrated Navigation Algorithm Based on TransGAN

With the rapid development of autopilot technology,a variety of engi-neering applications require higher and higher requirements for navigation and positioning accuracy,as well as the error range should reach centimeter level.Single navigation systems such as the inertial navigation system(INS)and the global navigation satellite system(GNSS)cannot meet the navigation require-ments in many cases of high mobility and complex environments.For the purpose of improving the accuracy of INS-GNSS integrated navigation system,an INS-GNSS integrated navigation algorithm based on TransGAN is proposed.First of all,the GNSS data in the actual test process is applied to establish the data set.Secondly,the generator and discriminator are constructed.Borrowing the model structure of generator transformer,the generator is constructed by multi-layer transformer encoder,which can obtain a wider data perception ability.The generator and discriminator are trained and optimized by the production countermeasure network,so as to realize the speed and position error compensa-tion of INS.Consequently,when GNSS works normally,TransGAN is trained into a high-precision prediction model using INS-GNSS data.The trained Trans-GAN model is emoloyed to compensate the speed and position errors for INS.Through the test analysis offlight test data,the test results are compared with the performance of traditional multi-layer perceptron(MLP)and fuzzy wavelet neural network(WNN),demonstrating that TransGAN can effectively correct the speed and position information when GNSS is interrupted,with the high accuracy.

基于VB-EKF的GPS/INS松组合导航定位算法

针对应用在无人机(Unmanned Aerial Vehicle,UAV)中的全球定位系统/惯性导航系统(GPS/INS)松组合导航非线性系统受到外界噪声干扰导致量测噪声在滤波时不断变化,从而造成滤波精度下降等问题,提出一种变分贝叶斯扩展卡尔曼滤波(VB-EKF)算法。该算法利用EKF(Extended Kalman Filter)将非线性系统中的状态函数和量测函数展开为线性方程,并将两个不同的导航系统数据进行融合,避免了单系统导航定位发散的问题。考虑到组合系统中量测噪声的时变特性,引入变分贝叶斯算法进行改进,有效解决了系统滤波精度下降问题。仿真结果表明,VB-EKF较EKF算法可有效提高滤波稳定性,进而提高系统导航定位精度。

基于GPS-惯性数据的激光大气数据系统标定方法

激光大气数据系统由于系统测量精度较高以及测量区域范围较大,目前尚无有效的高精度标定手段。针对这一问题,提出一种直线折返测速法,可以消除大气中风速的影响,将消除风速影响后的空速数据与GPS-惯性组合导航系统测量的地速数据进行对比分析,以评价激光大气数据系统真空速测量性能。分析了所提方法的各类测量误差以及理论极限精度。飞行实验结果表明,所提方法测量精度优于0.5 m/s,接近理论误差极限,可以用来标定激光大气数据系统,为激光大气数据系统标定技术的发展和应用提供参考。

Ground Alignment of Inertial Navigation System with the Aid of GPS Signals

惯性导航系统地面初始对准是惯导系统的关键技术之一，由于系统的观测性弱，仅水平对准效果好，而方位对准效果较差．针对这一问题，本文提出采用双天线将ＧＰＳ载波相位作为外部参考信号引入惯导系统地面对准，和惯导系统相组合共同完成初始对准．仿真结果表明，组合对准不仅能使方位快速对准，而且还能提高水平对准的精度，同时可对陀螺漂移作出快速估计．

GPS/Galileo/BDS-3广播星历精度分析

为了分析当前GPS(Global Positioning System)、Galileo(Galileo Navigation Satellite System)和BDS-3(Beidou Navigation Satellite System with Global Coverage)广播星历的精度,详细分析研究了各种偏差改正及消除方法,并尽可能地消除了系统误差和粗差对评估结果的影响。选取2021-11-01/12-31共61天MGEX(multi-GNSS experiment)发布的多系统混合广播星历与武汉大学分析中心发布的事后精密星历数据进行实验,对GPS、Galileo和BDS-3近期广播星历精度进行对比分析,实验结果表明:3个系统广播星历整体精度由高到低依次是Galileo、BDS-3和GPS,其空间信号测距误差的RMS(root mean square)分别优于0.17、0.25和0.37 m,整体轨道精度的RMS分别优于0.17、0.12和0.25 m,BDS-3广播星历的轨道精度最高,钟差误差的RMS分别优于0.15、0.23和0.27 m,Galileo广播星历的钟差精度最高。对于GPS卫星的广播星历,blockⅢA卫星钟差和轨道精度均优于其他GPS类型卫星。

GPS/INS组合导航终端的全向位置驱离算法研究

随着微惯性导航系统技术的发展,越来越多的无人平台装备了GPS/INS组合导航终端并具备了一定的抗欺骗能力,但现有的卫星导航欺骗方法难以满足对欺骗目标任意方向的位置驱离需求。为解决这一问题,提出了一种GPS/INS组合导航终端的全向位置驱离算法,可用于对非法无人平台管控的场景。该方法以卫星欺骗信号的设计为突破口,首先分析了卫星导航欺骗对组合导航位置和状态参数的影响,接着将卫星导航欺骗对组合导航状态参数的影响量作为求取欺骗信号的约束条件,进而确定位置驱离所需卫星欺骗信号的欺骗量,接着利用确定的欺骗量建立全向位置驱离模型,最终利用建立的模型生成对应的欺骗信号实现对组合导航终端的全向位置驱离任务。试验验证了本文设计的算法能够实现对组合导航终端360°任意方向的位置驱离。

适用于高高原机场GPS/BDS导航快速选星算法分析

卫星导航系统是民航飞机导航的主要手段之一。因高高原机场环境复杂多变,单一GPS导航系统难以满足现实需求。基于GPS/BDS双模导航能显著提高导航性能,具有明显优势。但这将带来大的卫星冗余,增加了计算量的同时会引入更多低导航质量的卫星。为此,针对高高原机场情况,提出一种基于卫星仰角和方位角的快速选星算法,可以减少计算负担,同时自动选出高质量卫星信号,满足实际的导航需求。最后,选取4个典型的高高原机场场景对算法进行了充分的仿真实验分析。仿真结果验证了所提方法的有效性。

手持GPS导航及定位精度分析研究

为解决手持GPS量值溯源难题,本文从基线场观测墩固定间距直接比较法、观测墩坐标点法及室内便携式导航信号源法,对手持GPS导航及定位精度进行研究分析。实验证明:基线场观测墩固定间距比较法可将其控制在±5m以内;观测墩坐标点法受外界环境条件影响较大,造成测量精度(±10m以内)明显下降;室内便携式导航信号源法受外界环境条件影响较小,测量精度较高,且控制在±3m以内,满足精度等级±5m检测需求。

GPS/BDS组合导航系统的精密单点定位模型

为了改善单卫星导航系统由于卫星数量较少,导致定位误差高的问题,设计全球定位系统(GPS)/北斗卫星导航系统(BDS)组合导航系统的精密单点定位模型:线性组合处理GPS与BDS的伪距和载波相位观测值,并构建GPS/BDS组合导航系统的组合观测值;接下来利用协方差成形自适应卡尔曼滤波方法,对组合导航系统导航过程中的卫星钟差、电离层延迟等误差实施滤波;再利用滤波处理后组合导航系统的广播星历,获取卫星轨道以及钟差;然后利用经验模型修正对流层延迟,获取相位观测值与伪距的综合改正数;最后利用综合改正数修正组合导航系统的观测方程,构建精密单点定位模型。实验结果表明,GPS/BDS组合导航系统采用所构建模型进行精密单点定位,不同方向定位误差均低于10 cm,证明了提出模型的有效性。