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.     

GNSS integration with vision-based navigation for low GNSS visibility conditions

In urban canyons, buildings and other structures often block the line of sight of visible Global Navigation Satellite System (GNSS) satellites, which makes it difficult to obtain four or more satellites to provide a three-dimensional navigation solution. Previous studies on this operational environment have been conducted based on the assumption that GNSS is not available. However, a limited number of satellites can be used with other sensor measurements, although the number is insufficient to derive a navigation solution. The limited number of GNSS measurements can be integrated with vision-based navigation to correct navigation errors. We propose an integrated navigation system that improves the performance of vision-based navigation by integrating the limited GNSS measurements. An integrated model was designed to apply the GNSS range and range rate to vision-based navigation. The possibility of improved navigation performance was evaluated during an observability analysis based on available satellites. According to the observability analysis, each additional satellite decreased the number of unobservable states by one, while vision-based navigation always has three unobservable states. A computer simulation was conducted to verify the improvement in the navigation performance by analyzing the estimated position, which depended on the number of available satellites; additionally, an experimental test was conducted. The results showed that limited GNSS measurements can improve the positioning performance. Thus, our proposed method is expected to improve the positioning performance in urban canyons.     

Sensing strides using EMG signal for pedestrian navigation

Navigation applications and location-based services are now becoming standard features in smart phones. However, locating a mobile user anytime anywhere is still a challenging task, especially in GNSS (Global Navigation Satellite System) degraded and denied environments, such as urban canyons and indoor environments. To approach a seamless indoor/outdoor positioning solution, Micro-Electro-Mechanical System sensors such as accelerometers, digital compasses, gyros and pressure sensors are being adopted as augmentation technologies for a GNSS receiver. However, the GNSS degraded and denied environments are typically contaminated with significant sources of error, which disturb the measurements of these sensors. We introduce a new sensor, the electromyography (EMG) sensor, for stride detection and stride length estimation and apply these measurements, together with a digital compass, to a simple pedestrian dead reckoning (PDR) solution. Unlike the accelerometer, which senses the earth gravity field and the kinematic acceleration of the sensor, the EMG sensor senses action potentials generated by the muscle contractions of the human body. The EMG signal is independent of the ambient environment and its disturbance sources. Therefore, it is a good alternative sensor for stride detection and stride length estimation. For evaluating the performance of the EMG sensor, we carried out several field tests at a sports field and along a pedestrian path. The test results demonstrated that the accuracy of stride detection was better than 99.5%, the errors of the EMG-derived travelled distances were less than 1.5%, and the performance of the corresponding PDR solutions was comparable to that of the global positioning system solutions.     

室内定位多传感器的空间配准

针对卫星信号到达地面时较弱、不能穿透建筑物的问题,该文对无法使用卫星定位时的辅助手段之一——室内定位技术作了研究。室内定位多传感器信息融合需要在统一坐标框架下进行,传统的空间配准解决方案多为简单的"松组合"加工。该文给出了多传感器空间配准数学模型,通过构造极大似然函数估计传感器间的位置偏差,采用粗估计与高斯牛顿迭代估计的两步法求解,并用仿真算例验证了该文所采用算法的有效性与正确性。     

Narrowband interference suppression for GPS navigation using neural networks

Since the Global Positioning System (GPS) satellites broadcast signals travel a long distance, the received signals are attenuated below the thermal noise level. Such weak signals are seriously subject to intentional or unintentional interferences from hostile or friendly noise sources. We propose an NN-based predictor for GPS anti-jamming applications. This new method exploits the adaptive notch filter as a cascade filter and the simple structure of Sigma-Pi neural network (Σ-Π NN). The Σ-Π NN can be trained quickly while avoiding the huge exponential computation for updating weights and thresholds in each layer, which allows easy hardware implementation. Simulation results show that its signal-to-noise ratio (SNR) improvement factor exceeds the factors of conventional multilayer perceptron, recurrent neural network, and other compound methods in single-tone and multi-tone continuous wave interference environments. Besides improving SNR, the anti-jamming performances are evaluated by computing root mean squared (RMS) prediction error and space vehicle (SV) observation number. The proposed algorithm provides the desired SV observation number, even for more than four vehicles, increases SNR improvement by about 46 % on average, and reduces RMS by about 27 % in average in both jamming mitigation processes.     

利用导航电子地图更新地形图的研究

根据导航电子地图的内容和更新特点,提出基于现势性较强的导航电子地图更新地形图的方法。利用MapInfo中Tab格式的1∶1万导航电子地图对ARC/INFO中cover格式的1∶1万地形图道路、居民地、植被、水系等变化较快的人文要素进行了更新。通过试验充分验证,文中提出的技术方案是可行的。     

基于GPS\BD2组合的车载导航系统性能分析

中国独立发展的北斗系统已经具备亚太地区的定位导航能力,为研究以北斗系统为主的车载导航技术的应用精度,采用伪距单点定位的方法分别对车载GPS,BD2,GPS\BD2 3种导航模式下的二维导航精度进行对比分析,结果显示定位精度分别为:3.66m,4.76 m,3.01 m,可以看出基于单点定位的北斗二维平面导航精度已达到5 m内,完全满足大众日常的出行要求。组合系统与GPS导航系统对比,组合系统具有更高的精度,是较好的导航模式。并基于visual studio平台编写定位软件,实现对车辆位置和速度信息的提取,监控车辆是否超速。     

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.     

Geoid determination using one-step integration

A residual (high-frequency) gravimetric geoid is usually computed from geographically limited ground, sea and/or airborne gravimetric data. The mathematical model for its determination from ground gravity is based on the transformation of observed discrete values of gravity into gravity potential related to either the international ellipsoid or the geoid. The two reference surfaces are used depending on height information that accompanies ground gravity data: traditionally orthometric heights determined by geodetic levelling were used while GPS positioning nowadays allows for estimation of geodetic (ellipsoidal) heights. This transformation is usually performed in two steps: (1) observed values of gravity are downward continued to the ellipsoid or the geoid, and (2) gravity at the ellipsoid or the geoid is transformed into the corresponding potential. Each of these two steps represents the solution of one geodetic boundary-value problem of potential theory, namely the first and second or third problem. Thus two different geodetic boundary-value problems must be formulated and solved, which requires numerical evaluation of two surface integrals. In this contribution, a mathematical model in the form of a single Fredholm integral equation of the first kind is presented and numerically investigated. This model combines the solution of the first and second/third boundary-value problems and transforms ground gravity disturbances or anomalies into the harmonically downward continued disturbing potential at the ellipsoid or the geoid directly. Numerical tests show that the new approach offers an efficient and stable solution for the determination of the residual geoid from ground gravity data.     

天宫一号高光谱数据辐射校正的CUDA并行优化

高光谱图像经过辐射校正后,消除了探测元的响应差异,能更好地满足专题信息提取的数据要求.利用探测元的列均值、列标准差等统计信息对天宫一号高光谱短波红外数据进行辐射校正检验,并基于GPU CUDA计算模型对均值归一化、矩匹配、相邻列均衡等3种相对辐射校正算法进行了并行计算优化.通过辐射校正计算流程拆分,CPU控制流程逻辑,GPU执行数据级并行计算,并建立CUDA的计算单元与数据单元的映射关系,获得5—7倍的计算加速比,这些辐射校正算法依据图像自身统计信息,且易于进行并行计算优化,满足实时校正的处理时效要求,为未来高光谱数据在轨实时辐射校正提供了新思路.     

多传感器组合导航系统的两级分布式融合算法研究

研究了可用于多传感器组合导航系统的两级分布式最优融合算法,以理论的形式证明了在有、无信息反馈的情况下最终的融合结果是等价的与最优的。仿真结果在验证上述理论的情况下,也说明了来自融合中心的反馈信息可明显提高子滤波的滤波性能。     

Precision spacecraft navigation using a low-cost GPS receiver

Within the PROBA-2 microsatellite mission, a miniaturized single-frequency GPS receiver based on commercial-off-the-shelf (COTS) technology is employed for onboard navigation and timing. A rapid electronic fuse protects against destructive single-event latch-ups (SEL) and enables a quasi-continuous receiver operation despite the inherent sensitivity to space radiation. While limited to single-frequency C/A-code tracking with a narrow-band frontend, the receiver is able to provide precision navigation services through processing of raw GPS measurements on ground as well as a built-in real-time navigation system. In both cases, ionospheric path delays are eliminated through a combination of L1 pseudorange and carrier phase measurements, which also offers a factor-of-two noise reduction relative to code-only processing. By comparison with satellite laser ranging (SLR) measurements, a 0.3-m (3D rms) accuracy is demonstrated for the PROBA-2 reduced dynamic orbit determinations using post-processed GPS orbit and clock products. Furthermore, the experimental onboard navigation system is shown to provide real-time position information with a 3D rms accuracy of about 1?m, which notably outperforms the specification of the Standard Positioning Service (SPS). In view of their lower hardware complexity, mass budget and power requirements as well as the reduced interference susceptibility, legacy C/A-code receivers can thus provide an attractive alternative to dual-frequency receivers even for demanding navigation applications in low Earth orbit.     

UAV navigation system using line-based sensor pose estimation

Abstract

This work presents a mapping and tracking system based on images to enable a small Unmanned Aerial Vehicle (UAV) to accurately navigate in indoor and GPS-denied outdoor environments. A method is proposed to estimate the UAV’s pose (i.e., the 3D position and orientation of the camera sensor) in real-time using only the on-board RGB camera as the UAV travels through a known 3D environment (i.e., a 3D CAD model). Linear features are extracted and automatically matched between images collected by the UAV’s onboard RGB camera and the 3D object model. The matched lines from the 3D model serve as ground control to estimate the camera pose in real-time via line-based space resection. The results demonstrate that the proposed model-based pose estimation algorithm provides sub-meter positioning accuracies in both indoor and outdoor environments. It is also that shown the proposed method can provide sparse updates to correct the drift from complementary simultaneous localization and mapping (SLAM)-derived pose estimates.     

Improved vessel squat modeling for hydrographic and navigation applications using kinematic GNSS positioning

The squat phenomenon, that is, the sinkage of a vessel due to its motion can affect the safety of navigation and reduce the accuracy of hydrographic bathymetry. Therefore, it is necessary to model and predict the squat of vessels as a function of cruise speed. We present a Global Navigation Satellite Systems–based squat modeling method for both hydrographic and navigation applications. For implementation of the proposed method, onboard GPS antennae configurations are offered to model bow squat for full-form ships such as supertankers or ore–bulk–oil carriers as well as stern squat for fine-form vessels such as passenger liners or container ships. In the proposed methodology, the onboard GPS observations are used to determine cruise ground speed, heave, attitude, and controlling the quality of kinematic positioning via fixed baselines. The vessel squat is computed from ellipsoidal height differences of the onboard antennae with respect to a reference state, after removal of all disturbing effects due to roll, pitch, heave, tide, vessel load, and geoidal height variations. The final products of the proposed approach are the analytical squat models usable for hydrographic and navigation applications. As the case study, the method is applied to a survey vessel in the offshore waters of Kish harbor. Numerical results indicate that the experimental precision of the derived analytical squat models is in the range of 0.003–0.028 m. The computed navigation squat of the test vessel at a speed of 12.64 knots is 30 % of the vessel draft and about twice its hydrographic squat. Although the field test was performed on a survey vessel, the method can be applied to any ship at any waterway. The proposed method can address the inevitable demand of reliable squat models for delicate hydrographic projects and high-speed marine traffic.     

GPS navigation processing using the quaternion-based divided difference filter

Divided difference filter (DDF) with quaternion-based dynamic process modeling is applied to global positioning system (GPS) navigation. Using techniques similar to those of the unscented Kalman filter (UKF), the DDF uses divided difference approximations of derivatives based on Stirling’s interpolation formula which results in a similar mean but different posterior covariance compared to the extended Kalman filter (EKF) solutions. The second-order divided difference is obtained from the mean and covariance in second-order polynomial approximation. The quaternion-based dynamic model is adopted for avoiding the singularity problems encountered in the Euler angle method and enhancing the computational efficiency. The proposed method is applied to GPS navigation to increase the navigation estimation accuracy at high-dynamic regions while preserving (without sacrificing) the precision at low-dynamic regions. For the illustrated example, the second-order DDF can deliver about 41–82% accuracy improvement as compared to the EKF. Some properties and performance are assessed and compared to those of the EKF and UKF approaches.     

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by granular computing. It provides a transparent structure despite the traditional multi‐layer neural networks. It also allows the classifier to be applied on a set of rules for each incoming pattern. Drawbacks of original granular computing (GrC) are covered, where some input patterns remained unclassified. The study was applied to classify seismic vulnerability of the statistical units of the city of Tehran, Iran. Slope, seismic intensity, height and age of the buildings were effective parameters. Experts ranked 150 randomly selected sample statistical units with respect to their degree of seismic physical vulnerability. Inconsistency of the experts' judgments was investigated using the induced ordered weighted averaging (IOWA) operator. Fifty‐five classification rules were extracted on which a neural network was based. An overall accuracy of 88%, κ = 0.85 and R² = 0.89 was achieved. A comparison with previously implemented methodologies proved the proposed method to be the most accurate solution to the seismic physical vulnerability of Tehran.     

基于GML 3.0/SVG的导航监控数据发布

本文讨论了GML 3.0/SVG在导航监控数据发布中的优势,在简要介绍地理信息标记语言GML和可伸缩矢量图像SVG的基础上,论述了基于GML 3.0/SVG的导航监控数据发布的系统构成,阐述了应用模式的制定、数据库检索生成GML文档、专有交换格式到GML的转换和GML到SVG的转换等关键技术。最后,进行了系统实现。     

PPP/INS tightly coupled navigation using adaptive federated filter

GPS Solutions - Integration of the global positioning system (GPS) with inertial navigation system (INS) has been very intensively studied and widely applied in recent years. Conventional GPS/INS...     

超轻型飞机GPS导航与摄影控制

低空超轻型飞机可以实现云下飞行,为大比例尺低空数码航空摄影测量提供了航空平台。本文将基于我国的超轻型飞机平台,设计机载低空数码航摄GPS导航和定点曝光控制系统,详细阐述了导航算法,并对"一机两屏"的方案进行了分析。通过自动定点曝光控制,保证航摄影像的前后、左右重叠度,试验结果显示该系统稳定可靠。     

Road hierarchy with integration of attributes using fuzzy-AHP

Road objects in a network data model are categorised into a hierarchical structure in accordance with their functions and capacities. In this study; five road attributes derived from semantic, geometric and topological properties of network data set (i.e. road class, road length and centralities of degree, closeness and betweenness) are utilised for the creation of road network hierarchy. The relationships with each attributes except road class and their effects on the determination of road importance are analysed by using a distribution graph and the equation of Pearson correlation coefficient. For creating road network hierarchy, integration process is achieved through the application of fuzzy analytic hierarchy process assuming attributes as the fuzzy criteria. The integration process is followed by the calculation of new priority attribute that indicates the importance of road objects. At the end of the process, road class, which is the most important attribute, is also used for the validation of proposed methodology. The results show that the new priority value of a road is superior to its each attribute value in hierarchical organisation.