一种SINS/GPS深组合导航系统技术问题分析

为了满足高动态用户及强干扰条件下的应用需求,提出了一种基于卫星信号矢量跟踪的SINS/GPS深组合导航方法,设计了基于FPGA硬件平台的实施方案。利用组合卡尔曼滤波器反馈回路取代了传统接收机中独立、并行的跟踪环路,能够同时完成所有可视卫星信号的跟踪和导航信息处理;通过矢量跟踪算法对所有可视卫星信号进行集中处理,能够增强跟踪通道对信号载噪比变化的适应能力,从而提高接收机在强干扰或信号中断条件下的跟踪性能;根据SINS导航参数和星历信息推测GPS伪码相位和多普勒频移等参数,用以辅助卫星信号的捕获和跟踪,能够大大缩短接收机的搜索捕获时间,并增强接收机在高动态条件下的跟踪性能。基于矢量跟踪的深组合方法不仅在GPS信号短暂中断期间,能够保证系统的导航精度和可靠性,而且在强干扰环境中能够维持较好的伪码相位和载波频率跟踪性能。     

Performance of GPS and GPS/SINS navigation in the CE-5T1 skip re-entry mission

A CE-5T1 spacecraft completed a high-speed skip re-entry to the earth after a circumlunar flight on October 31, 2014. In addition to the strapdown inertial navigation system (SINS), a lightweight GPS receiver with rapid acquisition was developed as a navigation sensor in the re-entry capsule. The GPS receiver effectively solved the poor accuracy problem of long-term navigation using only the SINS. In contrast to ground users and low-earth-orbit spacecraft, numerous factors, including high altitude and kinetic characteristics in high-speed skip re-entry, are important for GPS positioning feasibility and were presented in accordance with the flight data. GPS solutions started at nearly 4900 km orbital altitude during the phases of re-entry process. These solutions were combined by an inertial measurement unit in a loosely coupled integrated navigation method and SINS navigation initialization. A simplified GPS/SINS navigation filter for limited resources was effectively developed and implemented on board for spacecraft application. Flight data estimation analyses, including trajectory, attitude, position distribution of GPS satellite, and navigation accuracy, were presented. The estimated accuracy of position was better than 42 m, and the accuracy of velocity was better than 0.1 m/s.     

神经网络在SINS/GPS组合定位中的应用

地籍测量中,单一系统无法满足定位要求,组合定位技术应运而生. 其中,捷联惯性导航系统(SINS)和GPS组合定位应用最为广泛.在卫星信号受到干扰失效区域,系统进入纯SINS解算,定位误差会逐渐累积,无法满足定位精度要求. 针对此问题,提出一种长短期记忆(LSTM)神经网络辅助的组合定位算法. 根据LSTM神经网络能够有效运用于长距离时间序列的特性,在GPS有效区域,用卡尔曼滤波(KF)算法对SINS/GPS信号进行数据融合得到精确定位信息,同时利用惯性测量单元(IMU)、GPS和SINS输出信息对神经网络进行训练;在GPS失效区域,利用训练好的神经网络预测GPS位置信息,使得系统能继续用卡尔曼滤波器滤波. 最后结合地籍测量特点,设计了仿真实验,证明了该算法在GPS信号失效时可以有效抑制系统误差发散、提高定位精度,在不同运动状态下依然可以满足定位精度要求、鲁棒性强.      

一种基于多普勒频移的GPS自适应滤波算法研究

针对接收机的动态模型对GPS定位精度的影响,提出了一种基于多普勒频移观测的高动态GPS自适应滤波算法。该算法利用GPS伪距测量值以及利用信号载波的多普勒频移所获得的伪距率测量值,在GPS动态滤波中同时观测伪距和伪距率。借助于移动目标的运动矢量模型以及GPS定位误差模型建立了滤波方程。重点讨论了运用该模型进行Kalman滤波的实现过程。仿真实验表明,该模型与传统的方差自适应模型相比,位置精度提高了32%、速度精度提高了25%,应用本文算法能够提高定位精度和改善接收机的动态性能,拓宽高精度、高动态导航的应用范围。     

GPS卫星信号Doppler频移的计算与分析

在GPS导航定位系统中，多普勒频率偏移直接影响接收机性能。为了克服多普勒频率偏移的影响，提高接收机的GPS信号捕获速度，对多普勒频移估计算法进行研究。通过分析可视卫星的判定、Doppler频移计算方法，基于NewStar150GPS原理实验平台，使用C＋＋语言编程，开发Doppler频移计算程序。实验结果表明，Doppler频移的大小与a有关。当a〈90°时，多普勒频移为正，用户接收机收到的频率比卫星发射的频率要低。当a〉90°时，多普勒频移为负；当a=90°时，多普勒频移为0。     

An intelligent and autonomous MEMS IMU/GPS integration scheme for low cost land navigation applications

An intelligent scheme to integrate inertial navigation system/global positioning system (GPS) is proposed using a constructive neural network (CNN) to overcome the limitations of current schemes, namely Kalman filtering (KF). The proposed CNN technique does not require prior knowledge or empirical trials to implement the proposed architecture since it is able to construct its architecture “on the fly,” based on the complexity of the vehicle dynamic variations. The proposed scheme is implemented and tested using Micro-electro-mechanical systems inertial measurement unit data collected in a land-vehicle environment. The performance of the proposed scheme is then compared with the multi-layer feed-forward neural networks (MFNN) and KF- based schemes in terms of positioning accuracy during GPS signal outages. The results are then analyzed and discussed in terms of positioning accuracy and learning time. The preliminary results presented in this article indicate that the positioning accuracy were improved by more than 55% when the MFNN and CNN-based schemes were implemented. In addition, the proposed CNN was able to construct the topology by itself autonomously on the fly and achieve similar prediction performance with less hidden neurons compared to MFNN-based schemes.     

<Emphasis Type="Italic">sub</Emphasis>ATTO indoor GPS – pitfalls,solutions and performance using a conventional correlator

A key requirement for emergency call location (e.g. E911), for robust operation of location-based m-commerce systems and for telematics systems is that the location technology be able to operate in urban canyons and inside buildings. We start from a definition of the target environments, which includes multi-level parking garages, office buildings and homes, but not underground parking garages or tunnels. Based on experience in these target environments and understanding of typical applications we derive specific requirements for sensitivity and acquisition speed. The primary problems associated with weak signal operation are as follows. (1) In conventional GPS receivers sampling at the correlator output typically occurs at a sampling interval of the order of 1 ms. With weak signals, however, the signal-to-noise ratio of these samples is too low to support lock-in of a phase-locked or frequency-locked loop. (2) With weak signals, the signal-to-noise ratio is too low to support the extraction of the 50BPS navigation message from the signal. Therefore, aiding data is required from an external source. (3) Because the data cannot be extracted, it is not possible for the receiver to synchronize to the incoming bits, words or subframes. Therefore, it is not possible to construct pseudoranges without prior information. (4) The paper describes Sigtec Navigation's subATTO technology. This technology provides sensitivity down to –185 dBW (19 dBHz assuming NF of 1.5 dB and no other implementation loss). This is 5 dB below an attoWatt (10^–18 W) and has been shown to provide reliable positioning inside buildings, multi-level parking garages and in urban canyons without any aiding at all. The paper describes the patented signal processing scheme, how ambiguity resolution and time synchronization are achieved, the wireless assistance technique, the acquisition strategy and the use of scanning channels. Results are presented from trials in a multi-level parking garage. The results obtained in most parking garages are similar to these in terms of availability of fixes, signal strengths received and location accuracy achieved. The performance achieved in multi-level parking garages is rarely worse than this. One of the major impediments to practical application of weak signal-processing schemes is the limited dynamic range imposed by the GPS C/A code signal structure. This problem is discussed along with the problems of multipath distortion in the context of telematics operation in urban canyons. A realistic urban accuracy goal of 20 m for 95% of fixes is proposed based on experience with GPS and dead reckoning. Enhancements under development will provide sensitivity of –188 dBW, which will provide continuous availability within a broader range of indoor environments. For practical applications, this will require the use of modern 'search engine' hardware for acceptable acquisition speed. As the paper shows, this sensitivity is near the practical limit of sensitivity with acceptable acquisition times and dynamic capability. Electronic Publication     

弱GPS/GLONASS信号捕获算法研究

本文给出了GPS/GLONASS双模接收机的总体设计方案,重点对弱信号环境下的接收机信号捕获进行了讨论,采用并行码相位搜索方法和改进的循环相关算法分别对GLONASS信号和GPS信号进行捕获;并利用真实数据对双模接收机性能进行了仿真研究,重点对接收机捕获弱信号的能力,以及在不同信噪比和不同累加数据长度下的捕获概率进行了讨论,结果表明该双模接收机在不需要较长数据长度的情况下能够捕获低信噪比环境下的卫星导航信号,提高了接收机的灵敏度。     

面向城市道路的多传感器融合定位导航技术

方便地获取高精度、高可靠的轨迹数据是交通、旅游等行业智能化发展的关键。鉴于此，本文设计一款集GPS、SINS和OBD于一体的多源车载组合导航系统来收集轨迹数据，提出数据融合的方法来弥补因传感器噪声导致的位置累积误差，并在GPS失锁时有效预测轨迹位置信息。此方法通过梯度提升与决策树相结合建立INS误差补偿模型，并引用粒子群算法优化模型的回归参数，可有效避免误差积累；再利用联邦滤波器实现GPS、SINS和OBD数据融合，提高了轨迹信息的准确性。实际道路测试证明，基于此方法的组合导航系统，在多种路况下可收集连续精准的轨迹数据。     

A test-bed implementation of an acquisition system for indoor positioning

Indoor GNSS signals are typically received with poor signal-to-noise ratio, which impairs the acquisition stage of common global positioning system (GPS) receivers. Extending the coherent integration time increases the acquisition sensitivity, but the data-bit-rate limits the maximum achievable performance. Non-coherent processing also improves the detection performance, but indoor signals require a large amount of accumulations resulting in significant squaring loss. Moreover, both strategies have high computational complexity which fixes demanding requirements for stand alone mass-market terminals operating in real time. A sensitivity–complexity trade-off is therefore mandatory. Assisted-GPS, which is included in 3GPP specifications, reduces the overall acquisition complexity and enhances sensitivity. In this paper we describe a low-complexity-assisted data-wipe-off technique that enables the high-sensitivity acquisition of GPS signals. The method is based on the acquisition of the strongest signal in order to obtain information that eases the acquisition of the weaker ones. The analysis also addresses sources of sensitivity loss, such as Doppler effects and local oscillator inaccuracies. A test campaign with real signals and integration times up to 2 s validates the method, demonstrating the effectiveness of the proposed technique in indoor environments.     

An enhanced bit-wise parallel algorithm for real-time GPS software receiver

Software-based global positioning system (GPS) receivers perform all the baseband signal processing and the high level functions on a general purpose processor. The heavy computational loads of the signal correlation in baseband processing make it difficult for software receivers to operate in real time. In order to improve the real-time performance, an enhanced bit-wise parallel algorithm has been developed in this study. The enhanced algorithm has been implemented and tested in a 12 channels real-time GPS software receiver. The system consists of a radio frequency front end, a data acquisition board and software that runs on a laptop with a Pentium-M 1.5 GHz processor running the Window^® XP operating system. The data acquisition board packs the 2-bit intermediate frequency samples with a 2-bit in/8-bit out shift register and transfers the packed samples to laptop through a USB port. The software running on the laptop performs all the baseband and navigation processing in real time. The test results show that the enhanced algorithm significantly improves the real-time performance of the software receiver by reducing the computational operations for signal correlation by 50% compared with the existing bit-wise parallel algorithm. Furthermore, the enhanced algorithm also reduces the amount of required memory for storing data for signal correlation.     

基于卡尔曼滤波和改进DBSCAN聚类组合的GPS定位算法

实时获取智能移动终端的地理位置信息是增强现实（AR）实景智能导航系统实现的关键,为了提高智能终端GPS定位的精度,提出了一种基于卡尔曼滤波与改进的具有噪声的基于密度的聚类方法（DBSCAN）结合的GPS组合定位优化方法. 通过对GPS系统采集到的位置坐标数据进行卡尔曼滤波,去除较大的数据波动,控制定位误差范围,采用DBSCAN聚类算法进行分类去噪和二次聚类,对类中数据求得算术均值和类间数据总数进行加权求重心,确定位置坐标. 实验结果表明,提出的算法能有效提高GPS单点定位精度,减少定位误差,同时很好地满足了AR实景智能导航系统实时性和鲁棒性的要求.      

基于GPS软件接收机的SINS／GPS紧组合系统仿真

为了系统验证SINS／GPS紧组合系统的性能,基于GPS软件接收机,进行了仿真系统构建。仿真系统由轨迹发生器、GPS中频信号模拟器、IMU信号模拟器、GPS软件接收机、SINS导航解算模块、组合滤波算法和导航性能分析模块等部分构成,其中详细设计了GPS软件接收机中的捕获和跟踪算法、SINS解算以及基于伪距和伪距率的组合滤波算法。仿真结果表明：紧组合导航系统收敛性较好,能够一定程度上抑制惯导系统误差的积累,有较好的导航性能。设计的该系统满足紧组合导航系统性能验证的需要,也为后续的超紧组合研究奠定了良好的基础。     

Integrated Navigation System and Experiment of a Low-Cost and Low-Accuracy SINS/GPS

When SINS (strap-down inertial navigation system) is combined with GPS, the observability of the course angle is weak. Although the course angle error is improved to some extent through Kalman filtering, the course angle still assumes a divergent trend. This trend is aggravated further when using low-cost and low-accuracy SINS. In order to restrain this trend, a method that uses AHRS to substitute for SINS course angle information is put forward aimed at the hardware component characteristic of the low-cost and low-accuracy SINS including AHRS (attitude and heading reference system) and IMU (inertial measurement unit). Real static and dynamic experiments show that the method can restrain the divergent trend of the navigation system angle effectively, and the positioning accuracy is high.     

Integrated navigation system and experiment of a low-cost and low-accuracy SINS/GPS

When SINS (strap-down inertial navigation system) is combined with GPS, the observability of the course angle is weak. Although the course angle error is improved to some extent through Kalman filtering, the course angle still assumes a divergent trend. This trend is aggravated further when using low-cost and low-accuracy SINS. In order to restrain this trend, a method that uses AHRS to substitute for SINS course angle information is put forward aimed at the hardware component characteristic of the low-cost and low-accuracy SINS including AHRS (attitude and heading reference system) and IMU (inertial measurement unit). Real static and dynamic experiments show that the method can restrain the divergent trend of the navigation system angle effectively, and the positioning accuracy is high.     

State transformation extended Kalman filter for GPS/SINS tightly coupled integration

Extended Kalman filter (EKF) is a widely used estimator for integrated navigation systems, and it works well in general situations. However, in adverse conditions such as partially observable environments and highly dynamic maneuvers, the performance of the traditional EKF-based strap-down inertial navigation system (SINS)/GPS integrated navigation system is easily to be affected by the dynamic changes of the specific force, thus leading to the problem of error covariance inconsistency. Though the inconsistency problem can be overcome to some extent if the system matrix, the states and the error covariance matrix are propagated as fast as possible in the SINS calculation rate, the problem cannot be fully solved. State transformation extended Kalman filter (ST-EKF) mechanization, with a new converted velocity error model for the SINS, is proposed, which can also be used to solve the inconsistency problem. In the ST-EKF, the specific force vector in the system error model is replaced by the nearly constant gravity vector for local navigation. Since the propagation and the updating of the ST-EKF can be executed simultaneously in the updating interval, the computation cost is greatly reduced compared with the traditional EKF. Experiments for the GPS/SINS tightly coupled navigation, including linear vibration Monte Carlo test and an unmanned aerial vehicle flight test, are implemented to evaluate the performance of the proposed ST-EKF. The results show that the proposed ST-EKF has superior performance to the traditional EKF, especially in partially observable situations.     

浅谈GPS/SINS组合导航系统的设计

SINS/GPS组合导航系统是一种性能较好的导航系统,它结合了GPS的高精度定位,误差无积累及INS的自主性、实时性等优点。两者的结合可使导航系统的成本下降,可靠性增加,精度提高。本文概要地介绍了SINS/GPS的研究背景、结构组成、建立了系统的总体设计方案,给出详细的软件设计框图.并介绍实现系统各个功能的软件算法。实际应用结果表明:该系统的导航精度、成本、体积等指标均达到了设计要求。     

一种低成本、低精度SINS/GPS组合导航系统及试验研究

在SINS（捷联式惯性导航系统）与GPS组合时，航向角的可观测性较弱，经过卡尔曼滤波后，航向角误差虽有所改善，但仍呈发散趋势，当采用低成本、低精度SINS时，该趋势进一步加剧。为了抑制该发散趋势，针对某型低成本、低精度SINS硬件组成（包括AHRS（姿态和航向参考系统）与IMU（惯性测量器件））的特点，提出了利用AHRS代替SINS航向角信息的方法。实际静态与动态试验表明，利用该方法可有效地抑制组合系统航向角的发散趋势，定位精度较高。     

基于双差伪距/伪距率的GPS/SINS紧组合导航

GPS/SINS组合导航系统的观测模型直接关系着导航系统的精度。提出了一种基于双差伪距/伪距率的GPS/SINS紧组合观测模型。分析了采用双差伪距和采用双差伪距/伪距率两种观测模型对组合导航输出参数精度的影响。实测数据结果表明,采用双差伪距和采用双差伪距/伪距率作为观测值均能实现组合导航系统的收敛。引入双差伪距率观测值明显改善了系统的可观测性,不仅提高了组合导航中速度和姿态角的估计精度,也加快了速度误差和姿态角误差估计的收敛速度。     

弱GPS信号捕获算法的仿真实验研究

针对低信噪比环境下导航的需要,文章对低信噪比环境下弱GPS信号捕获算法进行了分析,重点对相关累加、非相关累加以及多重数据位循环相关累加捕获算法的信号处理流程和算法捕获性能进行讨论。通过特定的硬件装置获得真实的GPS信号,利用Matlab对上述3种常用的弱GPS信号捕获算法进行计算机仿真研究。根据理论分析和仿真结果可以看出,多重数据位循环相关捕获算法更适合检测低信噪比环境下的GPS信号。