A scheme for weak GPS signal acquisition aided by SINS information

In order to enhance the acquisition performance of global positioning system (GPS) receivers in weak signal conditions, a high-sensitivity acquisition scheme aided by strapdown inertial navigation system (SINS) information is proposed. The carrier Doppler shift and Doppler rate are pre-estimated with SINS aiding and GPS ephemeris, so that the frequency search space is reduced, and the dynamic effect on the acquisition sensitivity is mitigated effectively. Meanwhile, to eliminate the signal-to-noise ratio gain attenuation caused by data bit transitions, an optimal estimation of the unknown data bits is implemented with the Viterbi algorithm. A differential correction method is then utilized to improve the acquisition accuracy of Doppler shift and therefore to meet the requirement of carrier-tracking loop initialization. Finally, the reacquisition experiments of weak GPS signals are implemented in short signal blockage situations. The simulation results show that the proposed scheme can significantly improve the acquisition accuracy and sensitivity and shorten the reacquisition time.     

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...     

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%.     

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.     

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.     

GPS设备海上动态定位精度检测新方法

为解决GPS设备海上动态定位精度检测的问题,提出了利用高精度全站仪对GPS设备动态跟踪进行动态定位精度评定.阐述了检测基准数据的采集和处理,以及利用惯性测量设备提供的姿态数据将GPS定位数据归算到检测基准.简要介绍了进行动态定位精度评定所采用的方法,并对系统的误差进行了分析.最后通过实验验证了新方法的有效性.     

Comparing DGPS corrections prediction using neural network, fuzzy neural network, and Kalman filter

Position information obtained from standard global positioning system (GPS) receivers has time variant errors. For effective use of GPS information in a navigation system, it is essential to model these errors. A new approach is presented for improving positioning accuracy using neural network (NN), fuzzy neural network (FNN), and Kalman filter (KF). These methods predict the position components’ errors that are used as differential GPS (DGPS) corrections in real-time positioning. Method validity is verified with experimental data from an actual data collection, before and after selective availability (SA) error. The result is a highly effective estimation technique for accurate positioning, so that positioning accuracy is drastically improved to less than 0.40 m, independent of SA error. The experimental test results with real data emphasize that the total performance of NN is better than FNN and KF considering the trade-off between accuracy and speed for DGPS corrections prediction.     

区域地壳水平运动模型的Euler-GABP神经网络构建

针对传统的欧拉(Euler)矢量法在具有复杂构造运动的区域地区内构建GPS速度场模型精度低的问题,该文提出了欧拉矢量法结合遗传算法(GA)和BP神经网络方法来构建区域GPS速度场。以云南区域1999—2015年间的GPS速度场为实验数据,分别使用欧拉矢量法、遗传神经网络(GABP)、欧拉矢量结合神经网络(Euler-BP)和欧拉矢量的遗传神经网络(Euler-GABP)法构建区域速度场模型。实验结果表明,相比其他3种方法,该文提出的Euler-GABP方法在构建区域GPS速度场模型是可行和有效的且精度高。     

High-rate precise point positioning (PPP) to measure seismic wave motions: an experimental comparison of GPS PPP with inertial measurement units

High-rate GPS has been widely used to construct displacement waveforms and to invert for source parameters of earthquakes. Almost all works on internal and external evaluation of high-rate GPS accuracy are based on GPS relative positioning. We build an experimental platform to externally evaluate the accuracy of 50-Hz PPP displacement waveforms. Since the shake table allows motion in any of six degrees of freedom, we install an inertial measurement unit (IMU) to measure the attitude of the platform and transform the IMU displacements into the GPS coordinate system. The experimental results have shown that high-rate PPP can produce absolute horizontal displacement waveforms at the accuracy of 2–4 mm and absolute vertical displacement waveforms at the sub-centimeter level of accuracy within a short period of time. The significance of the experiments indicates that high-rate PPP is capable of detecting absolute seismic displacement waveforms at the same high accuracy as GPS relative positioning techniques, but requires no fixed datum station. We have also found a small scaling error of IMU and a small time offset of misalignment between high-rate PPP and IMU displacement waveforms by comparing the amplitudes of and cross-correlating both the displacement waveforms.     

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.     

基于多传感器的民航客机定位

为获取民航客机落地前的高精度位置,本文探讨了一种基于多传感器的民航客机高精度定位方法。在飞机飞行过程中,机载数据记录器会记录整个飞行过程中GPS接收机、无线电高度表、惯性导航系统等传感器数据;在飞机落地后,利用无线电高度表数据、GPS数据进行位置差分,再利用惯性导航数据进一步优化,可大幅提高落地前垂直定位精度。结果表明,通过本文方法在机场跑道入口前2 km,飞机定位精度大幅提高,垂直定位精度可以达米级,满足事后调查需求。     

卷积神经网络GPS坐标转换方法

GPS坐标转换方法对于GPS空间定位系统至关重要。目前已有很多方法被提出用于转换GPS坐标,但效果并不是很显著。究其原因,是因为大多数都存在模型误差和投影误差。针对目前方法的不足,本文利用深度学习对非结构化数据处理的优势,提出了一种基于卷积神经网络(CNN)的GPS坐标转换方法。该方法将GPS数据转化为非结构化图片数据,以其作为CNN的输入层来训练GPS坐标转换模型,这样能够最小化满足对数据的预处理要求,无监督地从数据中学习出有效特征。试验结果表明,该方法与传统坐标转换方法相比,具有更高的转换精度。     

Ionospheric and receiver DCB-constrained multi-GNSS single-frequency PPP integrated with MEMS inertial measurements

Single-frequency precise point positioning (SF-PPP) is a potential precise positioning technique due to the advantages of the high accuracy in positioning after convergence and the low cost in operation. However, there are still challenges limiting its applications at present, such as the long convergence time, the low reliability, and the poor satellite availability and continuity in kinematic applications. In recent years, the achievements in the dual-frequency PPP have confirmed that its performance can be significantly enhanced by employing the slant ionospheric delay and receiver differential code bias (DCB) constraint model, and the multi-constellation Global Navigation Satellite Systems (GNSS) data. Accordingly, we introduce the slant ionospheric delay and receiver DCB constraint model, and the multi-GNSS data in SF-PPP modular together. In order to further overcome the drawbacks of SF-PPP in terms of reliability, continuity, and accuracy in the signal easily blocking environments, the inertial measurements are also adopted in this paper. Finally, we form a new approach to tightly integrate the multi-GNSS single-frequency observations and inertial measurements together to ameliorate the performance of the ionospheric delay and receiver DCB-constrained SF-PPP. In such model, the inter-system bias between each two GNSS systems, the inter-frequency bias between each two GLONASS frequencies, the hardware errors of the inertial sensors, the slant ionospheric delays of each user-satellite pair, and the receiver DCB are estimated together with other parameters in a unique Kalman filter. To demonstrate its performance, the multi-GNSS and low-cost inertial data from a land-borne experiment are analyzed. The results indicate that visible positioning improvements in terms of accuracy, continuity, and reliability can be achieved in both open-sky and complex conditions while using the proposed model in this study compared to the conventional GPS SF-PPP.     

GPS/INS紧组合的INS辅助周跳探测与修复

建立了GPS/INS紧组合定位模型,改正惯性器件误差及电离层折射误差,对不同组合观测量的误差影响进行了分析,构造不同观测值组合,提出了基于惯性信息辅助的GPS周跳自适应探测方法,分析了INS定位误差对周跳探测的影响,给出了周跳探测误报率及修复成功率评价指标,提出了一种周跳检测阈值自适应确定方法。利用实测组合导航试验数据验证本文的算法,文中模拟了不同的单历元多周跳及信号失锁条件,结果表明,在GPS信号完全失锁20 s内,该方法能准确检测和修复所有周跳,中断时间的延长降低了周跳修复的成功率;GPS信号部分失锁时,在模拟的90 s中断时段内仍能修复所有周跳;模拟了170历元的5 s间隔密集周跳,周跳探测成功率为100%,正确修复率为99.41%。     

Attitude determination by integration of MEMS inertial sensors and GPS for autonomous agriculture applications

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies, which has widespread usage in industry, is also regarded as an ideal solution for automated agriculture because it fulfils the accuracy, reliability and availability requirements of industrial and agricultural applications. Agriculture applications use position, velocity and heading information for automated vehicle guidance and control to enhance the yield and quality of the crop, and in order to vary the application of fertilizer and herbicides according to soil heterogeneity at sub-field level. A loosely coupled GPS/INS integration algorithm known as “AhrsKf” is introduced for automated agriculture vehicle guidance and control utilizing MEMS inertial sensors and GPS. The AhrsKf can produce high-frequency attitude solutions for the vehicle’s guidance and control system, by using inputs from a single survey grade L1/L2 antenna, eliminating the need for the previous two antenna solutions. Given its agricultural application, the AhrsKf has been implemented with some specific design features to improve the accuracy of the attitude solution including, temperature compensation of the inertial sensors, and the aid of plough lines of farm lands. To evaluate the AhrsKf solution, two benchmarking tests have been conducted by using a three-antenna GPS system and NovAtel’s SPAN-CPT. The results have demonstrated that the AhrsKf solution is stable and can correctly track the movement of the farming vehicle.     

GPS/Pseudolite/INS integration: concept and first tests

This paper discusses the introduction of pseudolites (ground-based GPS-like signal transmitters) into existing integrated GPS/INS systems in order to provide higher availability, integrity, and accuracy in a local area. Even though integrated GPS/INS systems can overcome inherent drawbacks of each component system (line-of-sight requirement for GPS, and INS errors that grow with time), performance is nevertheless degraded under adverse operational circumstances. Some typical examples are when the duration of satellite signal blockage exceeds an INS bridging level, resulting in large accumulated INS errors that cannot be calibrated by GPS. Such a scenario, unfortunately, is a common occurrence for certain kinematic applications. To address such shortcomings, both pseudolite/INS and GPS/pseudolite/INS integration schemes are proposed here. Typically, the former is applicable for indoor positioning where the GPS signal is unavailable for use. The latter would be appropriate for system augmentation when the number and geometry of visible satellites is not sufficient for accurate positioning or attitude determination. In this paper, some technical issues concerned with implementing these two integration schemes are described, including the measurement model, and the appropriate integration filter for INS error estimation and correction through GPS and pseudolite (PL) carrier phase measurements. In addition, the results from the processing of simulated measurements, as well as field experiments, are presented in order to characterize the system performance. As a result, it has been established that the GPS/PL/INS and PL/INS integration schemes would make it possible to ensure centimeter-level positioning accuracy even if the number of GPS signals is insufficient, or completely unavailable. Electronic Publication     

<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     

Attitude estimation based on fusion of gyroscopes and single antenna GPS for small UAVs under the influence of vibration

An attitude estimation method is presented for small unmanned aerial vehicles (UAVs) powered by a piston engine which is the major source of vibration. Vibration of the engine significantly degrades the accuracy of the inertial measurement unit, especially for low-cost sensors that are based on micro electro-mechanical system. Therefore, a vibration model for a small UAV is proposed in order to examine the influence of vibration on attitude estimation with different sensors. The model is derived based on spectrum analysis with short-time Fourier transform. The vibration is compared with the drift of the gyroscope in order to examine the impact on attitude estimation. An attitude estimation method that fuses the gyroscopes and single antenna global positioning system (GPS) is proposed to mitigate the influence of engine vibration and gyroscope drift. The quaternion-based extended Kalman filter is implemented to fuse the sensors. This filter fuses the angular rates measured by the gyroscopes and the pseudo-attitude derived from the GPS velocity to estimate the attitude of the UAV. Simulations and experiment results indicate that the proposed method performs well both in short-term and long-term accuracy even though the gyroscopes are affected by drift and vibration noise, while the pseudo-attitude contains severe noise.     

无初始化的GPS辅助空中三角测量

简要介绍了以航线为单元 ,通过相位平滑递推算法在航求解双差载波相位观测量整周相位模糊度 ,进而确定各摄站空间位置的无初始化GPS动态定位原理 ,对某测区一组实际航摄资料进行GPS数据处理和GPS辅助光束法区域网平差 ,证实所介绍的算法是正确的、有效的 ,所获取的GPS摄站坐标可满足空中三角测量的精度要求 ,并且用该数据与经初始化GPS动态定位获取的GPS摄站坐标数据进行GPS辅助光束法区域网平差的总体精度是完全一致的。     

基于反射靶标的激光SLAM定位方法在港口无人车辆上的应用

针对港口岸边作业区和堆场内部等特定场景影响无人车辆高精度定位的问题,本工作以Cartographer 同步定位与建图(SLAM)作为算法基础,研究了基于反射靶标的无人车辆高精度定位方法,设计优化了反射靶标参数与布置方法. 针对影响基于反射靶标的激光SLAM定位精度的关键参数、行车速度和抖动干扰,设计实现了测试系统并进行了多参数对比. 针对港口场景下易发生的大抖动干扰导致激光SLAM定位失效的问题,分析了定位失效的产生机理,进而研究了基于惯性导航系统(IMU)和激光SLAM的复合定位技术,提出了大抖动干扰情况下基于反射靶标的激光SLAM定位误差的抑制方法. 实验结果表明:上述方案提高了无人车辆在港口特定场景下的定位精度和定位方法鲁棒性.