Online INS/GPS integration with a radial basis function neural network

Most of the present navigation systems rely on Kalman filtering to fuse data from global positioning system (GPS) and the inertial navigation system (INS). In general, INS/GPS integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and growth of position errors with time for INS. Present Kalman filtering INS/GPS integration techniques have some inadequacies related to the stochastic error models of inertial sensors, immunity to noise, and observability. This paper aims to introduce a multi-sensor system integration approach for fusing data from INS and GPS utilizing artificial neural networks (ANN). A multi-layer perceptron ANN has been recently suggested to fuse data from INS and differential GPS (DGPS). Although being able to improve the positioning accuracy, the complexity associated with both the architecture of multi-layer perceptron networks and its online training algorithms limit the real-time capabilities of this technique. This article, therefore, suggests the use of an alternative ANN architecture. This architecture is based on radial basis function (RBF) neural networks, which generally have simpler architecture and faster training procedures than multi-layer perceptron networks. The INS and GPS data are first processed using wavelet multi-resolution analysis (WRMA) before being applied to the RBF network. The WMRA is used to compare the INS and GPS position outputs at different resolution levels. The RBF-ANN module is then trained to predict the INS position errors and provide accurate positioning of the moving platform. Field-test results have demonstrated that substantial improvement in INS/GPS positioning accuracy could be obtained by applying the combined WRMA and RBF-ANN modules.     

Multi-sensor optimal data fusion for INS/GPS/SAR integrated navigation system

INS/GPS/SAR integrated navigation system represents the trend of next generation navigation systems with the high performance of independence, high precision and reliability. This paper presents a new multi-sensor data fusion methodology for INS/GPS/SAR integrated navigation systems. This methodology combines local decentralized fusion with global optimal fusion to enhance the accuracy and reliability of integrated navigation systems. A decentralized estimation fusion method is established for individual integrations of GPS and SAR into INS to obtain the local optimal state estimations in a parallel manner. A global optimal estimation fusion theory is studied to fuse the local optimal estimations for generating the global optimal state estimation of INS/GPS/SAR integrated navigation systems. The global data fusion features a method of variance upper finiteness and a method of variance upper bound to ensure that the global optimal state estimation can be achieved under a general condition. Experimental results demonstrate that INS/GPS/SAR integrated navigation systems achieved by using the proposed methodology have a better performance than INS/GPS integrated systems.     

Design and Development of a Real-Time DSP and FPGA-Based Integrated GPS-INS System for Compact and Low Power Applications

Emphasis of the present work is on an elegant real-time solution for GPS/INS integration. Micro-electro mechanical system (MEMS) based inertial sensors are light but not accurate enough for inertial navigation system (INS) applications. An integrated INS/GPS system provides better accuracy compared with either INS or GPS, used individually. This paper describes an improved design and fabrication of a loosely coupled INS-GPS integrated system. The systems currently available use commercial off-the-shelf (COTS) hardware and are, therefore, not optimized for compact, single supply, and low power requirements. In the proposed system, a digital signal processor (DSP) is used for inertial navigation solution and Kalman filter computations. A field programmable gate array (FPGA) is used for creating an efficient interface of the GPS with the DSP. Direct serial interface of the GPS involve tedious processing overhead on the navigation processor. Therefore, a universal asynchronous receiver transmitter (UART) and dual port random axis memory (DPRAM) are created on the FPGA itself. This also reduces the total chip count, resulting in a compact system. The system is designed to give real time processed navigation solutions with an update rate of 100 Hz. All the details of this work are presented.     

基于强跟踪滤波的GPS/INS组合导航系统对准技术研究

针对卡尔曼滤波鲁棒性较差的问题,研究了基于强跟踪滤波方法的GPS/INS对准。建立了GPS/INS组合导航系统对准的误差模型,对机载装备系统进行GPS/INS组合导航系统的对准仿真分析,验证该方案的可行性及强跟踪滤波器的性能。仿真结果表明,采用强跟踪滤波能够根据残差的变化求出渐消因子,能够在机动过程中有效跟踪系统状态量,从而提高对准精度和速度。采用强跟踪滤波的GPS/INS组合导航系统对准技术可以保证对准的快速性及对准精度,对工程应用具有重要的参考价值。     

Intelligent SINS/RDSS Integrated Algorithms for Land Vehicle Navigation

This is a discussion of the design of strap-down inertial navigation systems (SINS) and radio determination satellite service (RDSS) integrated navigation algorithms. The research aims at testing the effectiveness of artificial intelligence (AI)-aided Kalman filtering (KF) approaches for land vehicle applications. A back-propagation neural network (BPNN)-aided K*F algorithm and a fuzzy inference-based KF algorithm are presented in order to overcome the time delay of RDSS positioning provided by a double-star positioning system in China. Traditional KF causes biased solutions, and indeed, leads to filter instability easily since the time delay of RDSS positioning, in an active mode, is hard to be modeled and sometimes suffers from RDSS outages. Therefore, a fuzzy inference is used to correct the variance matrix of KE measurement noises adaptively; and a trained BPNN corrects the outputs of the Kalman filter. The algorithms proposed herein have been verified on real SINSIRDSS data. collected in land vehicle tests and are compared with other approaches. The results demonstrate that fuzzy inference-based KF algorithms improve the positioning accuracy to over 40 % better than KF algorithms, and BPNN-aided KF algorithms have the same precision as GPS which is the reference station In dynamic experiments without RDSS outages. The test results with RDSS outages indicate that the fuzzy inference-based KF is feasible but with positioning errors of hundreds of meters, so the BPNN-aided KF is designed to efficiently compensate for RDSS outages and improve system performance.     

GPS/惯性组合方式讨论与导航精度分析

 本文讨论GPS/惯性组合两种方式的优缺点。并以GPS伪距和伪距率与惯导组合为例,按GPS测量误差的不同,以及使用差分机等情况,仿真计算了机载使用的组合导航性能,进行了详细的精度分析。结果表明,这种组合的导航精度比GPS和惯导各自的导航精度高。在采用差分GPS机与惯导组合后,位置误差将进一步减少,使组合导航具有开辟例如飞机进场着陆等新的使用领域的可能性。     

一种适用于跑步状态的惯性/零速/GPS室内外无缝组合导航定位方法

目前,行人导航定位技术已经深入社会的众多领域,受到诸多学者的广泛关注。针对行人跑步状态,研究了一种惯性/零速/GPS室内外无缝组合导航定位方法。首先提出了可靠的、适用于行人跑步零速检测的方法,有效提高了在行人跑步状态下的零速检测的准确性。针对GPS信号容易受到高楼、高架等环境的干扰及在室内容易完全丢失的特点,提出了基于BP神经网络的GPS可用信号筛选方法,提高了GPS信息的可靠性与精准性。在此基础上,研究了基于可变量测的Kalman滤波器,实现了惯性/零速/GPS信息的有效融合,显著提高了在行人跑步状态下的导航定位精度。试验结果表明,所提出的这种适用于跑步状态的惯性/零速/GPS室内外无缝组合导航定位方法的平均定位误差可减小到行人跑步总里程的1%以内。     

基于对偶四元数的INS/GPS组合导航算法研究

在基于对偶四元数的捷联惯导解算方法的基础上,推导了以惯性系作为导航系的惯导误差方程,在此基础上设计了卡尔曼滤波组合导航算法。通过激光惯导跑车采集数据,进行了仿真分析,试验结果表明,该组合导航算法能有效的消除惯导累积的速度误差和位置误差,相比于目前广泛应用的INS/GPS组合导航算法,本文描述了INS/GPS组合导航的另一种实现方式,获得了相当的精度,具有一定的工程应用价值。     

地磁辅助的无缝INS/GPS组合导航研究

INS/GPS组合导航已经成为当前无人机导航系统的主要实现形式,由于GPS信号容易受到干扰,在恶劣的电磁环境下信号易丢失,从而导致GPS卫星信号失锁而无法使用。地磁导航作为一种无源导航方法,其难以受到外界干扰且具有较强的自主性,从而为克服GPS在干扰情况下无法对INS误差实现持续无缝修正的不足提供了很好的途径。针对INS/GPS组合导航中GPS卫星信号失锁的情况,设计提出了使用地磁匹配导航进行辅助实现无人机无缝导航的实现方案,设计了基于地磁特征的地磁匹配算法和地磁匹配辅助的INS/GPS组合无缝自主导航算法,并通过仿真验证了采用地磁匹配辅助导航方法,可以在GPS无效的情况下,实现对INS导航误差的持续无缝修正,从而提高导航系统性能。     

GPS／INS组合导航系统松、紧耦合性能比较

对GPS/INS组合导航系统的紧耦合算法进行了理论推导。详细分析了当INS元器件性能变差时,GPS/INS组合导航系统在不同耦合模式下的定位精度变化规律,得出采用紧耦合模式的组合导航系统比一般的松耦合方式能获得更好的定位精度;并通过仿真验证了该结论的正确。     

用于SAR运动补偿的DGPS/SINS组合系统研究

使用考虑位置误差相关项的伪距率观测模型 ,研究了用于合成孔径雷达运动补偿的差分 GPS/ SINS伪距率组合系统。结果表明 ,组合系统的长期位置精度能达到 1 m左右。 GPS数据更新率低于 INS,在 GPS测量时间间隔内 ,组合系统的性能仅由 INS决定。虽然 INS误差随时间积累 ,在 GPS数据更新率为 1 s的情况下 ,即使采用中等精度的惯性仪表 ,其相对位置精度为厘米级 (这里相对位置精度指组合系统在 GPS测量时间间隔内位置误差的变化范围)。     

Integration of GPS and low cost INS for pedestrian navigation aided by building layout

In outdoor environments, GPS is often used for pedestrian navigation by utilizing its signals for position computation, but in indoor or semi-obstructed environments, GPS signals are often unavailable. Therefore, pedestrian navigation for these environments should be realized by the integration of GPS and inertial navigation system （INS）. However, the lowcost INS could induce errors that may result in a large position drift. The problem can be minimized by mounting the sensors on the pedestrian＇s foot, using zero velocity update （ZUPT） method with the standard navigation algorithm to restrict the error growth. However, heading drift still remains despite using ZUPT measurements since the heading error is unobservable. Also, tbot mounted INS suffers from the initialization ambiguity of position and heading from GPS. In this paper, a novel algorithm is developed to mitigate the heading drift problem when using ZUPT. The method uses building lay- out to aid the heading measurement in Kalman filter, and it could also be combined for the initial- ization. The algorithm has been investigated with real field trials using the low cost Microstrain 3DM-GX3-25 inertial sensor, a Leica GS10 GPS receiver and a uBlox EVK-6T GPS receiver. It could be concluded that the proposed method offers a significant improvement in position accuracy for the long period, allowing pedestrian navigation for nearly40 min with mean position error less than 2.8 m. This method also has a considerable effect on the accuracy of the initialization.     

基于神经网络的航天器GPS/INS组合定姿系统

基于GPS和惯性技术的组合导航系统是近年来导航系统的研究热点和主要发展方向.目前基于卡尔曼滤波方法的算法在稳定性、计算量、算法鲁棒性以及系统可观测性等方面仍然存在问题.基于神经网络技术研究了一种新的GPS/INS组合定姿自适应卡尔曼滤波方法,理论分析表明,该方法不但对姿态信息具有较好的估计性能,而且对系统模型的精确性、噪声特性具备良好的鲁棒性.最后,利用模拟数据对所研究算法进行了分析计算,与传统的卡尔曼滤波方法进行了比较、分析,结果表明所设计组合算法在精度、稳定性以及鲁棒性等方面较传统卡尔曼方法具有良好的特性.     

基于RTS滤波的GPS+BDS 非差非组合PPP/INS紧组合模型

GPS高精度定位技术在动态复杂环境中,其定位精度、可靠性和连续性因卫星信号频繁失锁而变差。为此,提出了采用基于RTS滤波(Rauch-Tung-Striebel Filter)的GPS+BDS非差非组合PPP(Precise Point Positioning)与INS(Inertial Navigation System)紧组合模型的策略来克服GPS在动态定位中的弱点。其中,采用GPS+BDS双系统观测数据,可提高PPP解算中的可用卫星数,改善星站间定位几何强度和提高PPP收敛速度;采用PPP/INS紧组合,利用INS的自主定位特性和短期高精度特性,可有效改善复杂环境下的定位精度和连续性;采用RTS滤波,可进一步提高PPP/INS紧组合性能。首先推导了GPS+BDS非差非组合函数模型、PPP/INS紧组合函数模型和RTS滤波函数模型,然后利用一组车载动态数据,对动态GPS PPP、GPS+BDS PPP、GPS/INS紧组合、GPS+BDS PPP/INS紧组合和基于RTS的GPS+BDS PPP/IMU紧组合的定位、测速和定姿性能进行分析。实验结果表明,该方案可有效提高定位(58%～72%)、测速(74%～82%)和定姿(4%～23%)精度,特别是对卫星失锁期间的定位性能改善尤为明显。     

GPS Aided Inertial Navigation

The Global Positioning System is an extremely accurate satellite-based navigation system which, after its completion in 1989, will provide users worldwide, 24 hour. all weather coverage. A joint research project among Boeing, Rockwell-Collins, and Northrop has been completed in which a GPS receiver was integrated with a low-cost strap-down inertial navigation system and a flight computer. A Kalman filter in the latter allows in-fight alignment and calibration of the INS. In addition, feedback from the INS to the GPS receiver improves the system's ability to reacquire satellite signals after outages. The resulting system combines the accuracy of GPS with the jamming immunity and autonomy of inertial navigation. System tests were conducted in which a Boeing owned T-33 jet aircraft was flown through known test pattern to align and calibrate the INS. Earlier tests, including tests against an airborne jammer, were conducted in a modified passenger bus.     

GPS/INS integration on the Standoff Land Attack Missile (SLAM)

The Standoff Land Attack Missile (SLAM) is a worldwide, all-weather, precision-strike weapon system deployed from carrier-based aircraft. In the primary mode of operation, target location and other mission data are generated from intelligence sources available on the aircraft carrier and loaded into the missile prior to aircraft takeoff. After missile launch, the SLAM inertial navigation system (INS) guides the missile along the planned trajectory. Updating the missile INS from the Global Positioning System (GPS) during flight provides precise midcourse navigation and enhances target acquisition by accurate, on-target pointing of the SLAM Maverick seeker. The GPS/INS avionics and software integration used for SLAM are described in detail, along with some of the design tradeoffs that led to the approach. The avionics configuration integrates the Harpoon midcourse guidance unit, which includes a strapdown inertial sensor package and digital processor, with a Rockwell-Collins single-channel, sequential GPS receiver processor unit (RPU), a derivative of the GPS phase-III user equipment. In addition to the GPS receiver elements the RPU contains the navigation processor, which executes the SLAM navigation, Kalman filter algorithms, and other guidance algorithms including seeker pointing. Flight-test results of the SLAM GPS-aided INS are also included     

基于Starlink机会信号/INS的组合导航方法

针对全球卫星导航系统(GNSS)因频点单一、落地功率低、易受电磁干扰以及存在覆盖较差区域等潜在的被拒止或被干扰导致的导航系统性能降低甚至失效的问题,提出了一种基于星链(Starlink)机会信号融合惯性导航系统(INS)的飞行器动态组合导航方法。首先分析了星链信号体制,建立了基于星链星座卫星下行机会信号的瞬时多普勒定位观测模型,设计了一种基于频率细分的快速最大似然多普勒频率估计方法,然后建立了基于扩展卡尔曼滤波(EKF)的Starlink机会信号/INS的组合导航模型,并对该导航方法进行了实验及分析。结果表明,该方法可为飞行器提供长航时、连续、高精度的导航。动态飞行情况下,该方法可实现平均优于25 m的三维定位精度和平均优于0.1 m/s的速度估计精度,比相同观测时间下的惯导精度提高了1～2个数量级,显著提高了飞行器的导航精度,可为战略导航提供方法和技术支撑。     

基于Simulink的组合导航系统的FDIR技术仿真分析

为解决组合导航系统子系统故障时导航精度降低这一问题,根据事先制定的重构规则进行系统重构,使得导航系统发生故障时仍能正常工作地思想,制定了INS/GPS/ADS组合导航系统的重构规则,并采用Simulink进行了仿真。结果表明,在发生故障时,基于联邦滤波组合导航系统的重构技术能保证系统在故障时仍能以一定的精度进行导航。     

在组合导航系统中Kalman滤波技术的应用

在导航系统中引入Kalman滤波技术,主要是为了减小导航定时的参数误差并提高系统的定位精度,以INS/GPS组合导航系统为背景,设计位置速度组合模式的卡尔曼滤波器,并对组合导航系统进行仿真研究,结果表明组合导航系统在导航精度和稳定性方面较单一的导航系统都有提高。