A combined algorithm of improving INS error modeling and sensor measurements for accurate INS/GPS navigation

Although the integrated system of a differential global positioning system (DGPS) and an inertial navigation system (INS) had been widely used in many geodetic navigation applications, it has sometimes a major limitation. This limitation is associated with the frequent occurrence of DGPS outages caused by GPS signal blockages in certain situations (urban areas, high trees, tunnels, etc.). In the standard mechanization of INS/DGPS navigation, the DGPS is used for positioning while the INS is used for attitude determination. In case of GPS signal blockages, positioning is provided using the INS instead of the GPS until satellite signals are obtained again with sufficient accuracy. Since the INS has a very short-time accuracy, the accuracy of the provided INS navigation parameters during these periods decreases with time. However, the obtained accuracy in these cases is totally dependent on the INS error model and on the quality of the INS sensor data. Therefore, enhanced navigation parameters could be obtained during DGPS outages if better inertial error models are implemented and better quality inertial measurements are used. In this paper, it will be shown that better INS error models are obtained using autoregressive processes for modeling inertial sensor errors instead of Gauss–Markov processes that are implemented in most of the current inertial systems and, on the other hand, that the quality of inertial data is improved using wavelet multi-resolution techniques. The above two methods are discussed and then a combined algorithm of both techniques is applied. The performance of each method as well as of the combined algorithm is analyzed using land-vehicle INS/DGPS data with induced DGPS outage periods. In addition to the considerable navigation accuracy improvement obtained from each single method, the results showed that the combined algorithm is better than both methods by more than 30%.     

伪卫星增强GPS方法在变形监测中的应用研究

应用GPS对深开采的露天矿以及深山峡谷中的水库大坝进行变形监测,由于接收到的GPS卫星数目减少,导致GPS定位精度大大降低。采用伪卫星增强GPS的定位技术是解决这个问题的有效途径之一。提出GPS伪卫星组合测量新方法,建立组合观测量模型,开发减小伪卫星多路径效应的方法。基于实际的水电站观测数据分析伪卫星增强GPS提高定位精度的效果,给出GPS伪卫星组合定位的试验结果并验证所开发方法的有效性。     

减小伪卫星多路径效应误差的试验研究

伪卫星定位技术可以补偿GPS系统本身一些先天不足，然而GPS和伪卫星组合进行定位又带来了新的误差源，伪卫星多路径效应就是误差源之一。针对多路径的产生原理及特点，在经典最小二乘理论的基础上，推求稳健最小二乘估计的权函数，并结合实际观测数据验证了稳健最小二乘法消除多路径误差的有效性。     

Ultra-tight GPS/INS/PL integration: a system concept and performance analysis

The architecture of the ultra-tight GPS/INS/PL integration is the key to its successful performance; the main feature of this architecture is the Doppler feedback to the GPS receiver tracking loops. This Doppler derived from INS, when integrated with the carrier tracking loops, removes the Doppler due to vehicle dynamics from the GPS/PL signal thereby achieving a significant reduction in the carrier tracking loop bandwidth. The bandwidth reduction provides several advantages such as: improvement in anti-jamming performance, and increase in post correlated signal strength which in turn increases the dynamic range and accuracy of measurements. Therefore, any degradation in the derived Doppler estimates will directly affect the tracking loop bandwidth and hence its performance. The quadrature signals from the receiver correlator, I (in-phase) and Q (quadrature), form the measurements, whereas the inertial sensor errors, position, velocity and attitude errors form the states of the complementary Kalman filter. To specify a reliable measurement model of the filter for this type of integrated system, a good understanding of GPS/PL signal characteristics is essential. It is shown in this paper that phase and frequency errors are the variables that relate the measurements and the states in the Kalman filter. The main focus of this paper is to establish the fundamental mathematical relationships that form the measurement model, and to show explicitly how the system error states are related to the GPS/PL signals. The derived mathematical relationships encapsulated in a Kalman filter, are tested by simulation and shown to be valid.

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Adaptive GPS/INS integration for relative navigation

Relative navigation based on GPS receivers and inertial measurement units is required in many applications including formation flying, collision avoidance, cooperative positioning, and accident monitoring. Since sensors are mounted on different vehicles which are moving independently, sensor errors are more variable in relative navigation than in single-vehicle navigation due to different vehicle dynamics and signal environments. In order to improve the robustness against sensor error variability in relative navigation, we present an efficient adaptive GPS/INS integration method. In the proposed method, the covariances of GPS and inertial measurements are estimated separately by the innovations of two fundamentally different filters. One is the position-domain carrier-smoothed-code filter and the other is the velocity-aided Kalman filter. By the proposed two-filter adaptive estimation method, the covariance estimation of the two sensors can be isolated effectively since each filter estimates its own measurement noise. Simulation and experimental results demonstrate that the proposed method improves relative navigation accuracy by appropriate noise covariance estimation.     

利用联邦滤波器设计SINS／RDSS／GPS组合导航系统

介绍了Garlson提出的分布式联邦滤波理论，讨论了两类系统设计类型和四种配置模式的特征。首次提出SINS／RDSS／GPS组合导航的设想，并对各子系统特点进行了探讨和对SINS／RDSS子系统进行了建模，建议在实际开发中采用NR配置模式，该模式属于A类联邦滤波系统。通过算例验证了NR模式融合结果的近似最优性、优良的容错性和故障探测能力。     

Integrated GPS/INS navigation system with dual-rate Kalman Filter

A dual-rate Kalman Filter (DRKF) has been developed to integrate the time-differenced GPS carrier phases and the GPS pseudoranges with INS measurements. The time-differenced GPS carrier phases, which have low noise and millimeter measurement precision, are integrated with INS measurements using a Kalman Filter with high update rates to improve the performance of the integrated system. Since the time-differenced GPS carrier phases are only relative measurements, when integrated with INS, the position error of the integrated system will accumulate over time. Therefore, the GPS pseudoranges are also incorporated into the integrated system using a Kalman Filter with a low update rate to control the accumulation of system errors. Experimental tests have shown that this design, compared to a conventional design using a single Kalman Filter, reduces the coasting error by two-thirds for a medium coasting time of 30?s, and the position, velocity, and attitude errors by at least one-half for a 45-min field navigation experiment.     

北斗/INS组合导航关键技术分析

组合导航系统有利于充分利用各导航系统进行信息互补与信息合作,成为导航系统发展的方向。在所有的组合导航系统中,以GPS与惯性导航系统INS组合的系统最为理想,而深组合方式是GPS与惯性导航系统（INS）组合的最优方法。鉴于GPS的不可依赖性,北斗卫星导航系统与INS的组合是组合导航系统的发展趋势,研究其组合模式具有重要意义。通过分析、评述国外INS/GPS深组合导航系统的发展现状,提出我国自主研制INS/北斗深组合导航系统需要解决的关键技术。     

顾及动力学模型误差影响的GPS/INS组合导航自适应滤波算法

在顾及动力学模型随机误差的情况下,设计了一种GPS/INS自适应滤波算法.针对BP神经网络存在的训练速度慢、易陷入局部极小值等问题,对神经网络学习算法进行了改进.利用神经网络进一步减小系统误差对导航解的影响,给出了顾及动力学模型随机误差和系统误差的GPS/INS自适应滤波算法,并利用实测数据验证了算法的有效性.     

惯性导航与卫星导航紧耦合技术发展现状

对紧耦合方案、最优估计滤波算法和硬件开发等几方面进行了总结分析。分析了传统的INS／GPS松组合系统和INS／GPS紧耦合系统的区别；给出了INS／GPS紧耦合系统国内外的发展状况；指出INS／GPS紧耦合系统所涉及的关键技术。     

A JTIDS/INS/DGPS navigation system with pseudorange differential information transmitted over Link-16: design and implementation

As the battle environment becomes more complicated, the demand for higher accuracy and better anti-jam capacity of navigation has been increasing. The conventional JTIDS/INS/GPS integrated navigation cannot meet the demands of certain situations such as precision strike and formation flight. A new system that introduces the differential GPS into JTIDS/INS/GPS integration system is proposed to improve the navigation performance in the modern combined operations. In this system, the differential information of DGPS is transmitted through the communication data link of Link-16. As a result, the system resources are efficiently utilized and the controllability and anti-jam performance of the system are significantly enhanced. A hybrid slot allocation protocol (HSAP) that combines a static slot allocation algorithm and a dynamic slot allocation algorithm and the corresponding source-chosen mechanisms are proposed. The performance of the JTIDS/INS/GPS integration navigation using the differential GPS information from one or multiple base stations is studied and compared with that of the system without using the differential GPS information. Furthermore, the performance of the integration navigation using HSAP is compared with that of the system using static slot allocation algorithm. We show that navigation accuracy based on the differential GPS is improved, and using HSAP also leads to higher localization accuracy.     

A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications

The combined navigation system consisting of both global positioning system (GPS) and inertial navigation system (INS) results in reliable, accurate, and continuous navigation capability when compared to either a GPS or an INS stand-alone system. To improve the overall performance of low-cost micro-electro-mechanical systems (MEMS)-based INS/GPS by considering a high level of stochastic noise on low-cost MEMS-based inertial sensors, a highly complex problems with noisy real data, a high-speed vehicle, and GPS signal outage during our experiments, we suggest two approaches at different steps: (1) improving the signal-to-noise ratio of the inertial sensor measurements and attenuating high-frequency noise using the discrete wavelet transform technique before data fusion while preserving important information like the vehicle motion information and (2) enhancing the positioning accuracy and speed by an extreme learning machine (ELM) which has the characteristics of quick learning speed and impressive generalization performance. We present a single-hidden layer feedforward neural network which is employed to optimize the estimation accuracy and speed by minimizing the error, especially in the high-speed vehicle and real-time implementation applications. To validate the performance of our proposed method, the results are compared with an adaptive neuro-fuzzy inference system (ANFIS) and an extended Kalman filter (EKF) method. The achieved accuracies are discussed. The results suggest a promising and superior prospect for ELM in the field of positioning for low-cost MEMS-based inertial sensors in the absence of GPS signal, as it outperforms ANFIS and EKF by approximately 50 and 70%, respectively.     

附加约束条件对GNSS/INS组合导航结果的影响分析

针对车载全球导航卫星系统/惯性导航系统（global navigation satellite system/inertial navigation system,GNSS/INS）组合导航中卫星信号中断,惯性导航系统单独导航误差积累较大的问题,提出了附加载体运动条件约束的卡尔曼（Kalman）滤波解算方法。通过利用载体固有的运动约束,包括近似高程约束、近似速度约束和近似姿态约束,减少载体自由度和模型参数;通过引入新的观测类型,增加观测冗余,可以加强Kalman滤波解,提高在GNSS信号中断时组合导航系统的定位精度,实现无缝导航。     

New jump trajectory determination method using low-cost MEMS sensor fusion and augmented observations for GPS/INS integration

Objective information on athletic maneuvers for performance evaluation has become highly desired in sports such as skiing, snowboarding, and mountain biking. Body-mounted devices, incorporating low-cost microelectromechanical, inertial navigation units, and global positioning system (GPS) receivers, to calculate sport-specific key performance variables (KPVs) and provide real-time feedback, are now commercially available. However, algorithms implemented for such purposes still lack accuracy and power efficiency. A new GPS/INS (inertial navigation system) integration algorithm is proposed to determine the trajectory of an athlete executing jumps while skiing, snowboarding, mountain biking etc. KPVs, such as jump horizontal distance, vertical height, and drop, are calculated from the trajectory. A new sensor error compensation scheme is developed using sensor fusion and linear Kalman filters (LKF). The LKF parameters are varied to address the fluctuating dynamics of the athlete during a jump. The extended Kalman filter used for GPS/INS integration has an observation vector augmented with sensor error measurements derived from sensor fusion. The performance of the proposed algorithm is evaluated through experimental field tests. For the determination of jump horizontal distance, height, and drop, the proposed algorithm has errors of 14.3 cm (5.5 %), 1.6 cm (38 %), and 6.7 cm (9.4 %), respectively. Errors in KPVs for a set of jumps were first determined with respect to the true KPVs, and then the errors for all the jumps were averaged to calculate the absolute and percentage errors. The accuracy achieved is deemed to fulfill the expectations of both recreational and professional athletes.     

基于伪卫星的改善GPS几何精度因子的研究

随着人们活动范围的日益扩大和周边环境的日益复杂,高精度GPS导航技术逐渐成为国内外研究的重点。GPS系统的定位精度在很大程度上取决于参与定位卫星的数目和几何布局,而几何精度因子(GDOP)正是衡量定位卫星几何布局优劣的量度。文章从几何精度因子着手,从理论上证明了伪卫星对GPS系统GDOP的改善,分析了伪卫星数量对GPS系统定位精度的影响。借助于仿真实验,结果表明,在GPS导航定位中,伪卫星能够显著增强卫星几何图形结构、提高测量精度、改善精度因子从而提高定位精度。     

扩展区间Kalman滤波器及其在GPS/INS组合导航中的应用

针对具有不确定动态模型参数的 GPS/INS 组合导航系统,首先介绍一种新型的区间Kalman滤波器,讨论了GPS/INS 组合系统中模型参数不确定性的问题,分析了惯性传感器建模中相关时间常数的区间特性,并建立了适合非线性特性的GPS/INS组合系统的扩展区间卡尔曼滤波器.计算结果表明,扩展区间卡尔曼滤波器对非线性GPS/INS组合系统是很有效的,它能给出组合系统导航误差的上下界,这对组合系统的设计具有指导的意义.     

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。     

GNSS/INS组合导航系统定位精度分析

GNSS/INS组合导航系统近年来得到了快速发展,应用领域越来越广泛。组合导航的定位精度是一个重要的研究方向,本文将应用于航空遥感领域的高精度GNSS/INS组合导航系统放置在地面平台上,采集试验数据,通过与NRTK定位结果比较,对组合导航系统定位精度进行分析,得出GNSS/INS组合导航系统的定位精度可达到厘米级的试验结论。     

GPS/IMU组合定位定向系统设计

介绍了一种组合低成本的GPS和IMU(Inertial Measurement Unit)来获得高精度姿态、位置和速度信息的方法。文中介绍了GPS／IMU组合系统的设计方案及系统的硬件和软件设计，并给出了实验结果。该组合系统将来安装于炮兵测地车后，不仅可以提高测地的精度，而且在采样率、可靠性等方面比单独采用GPS定位的方法更有优势。