Jingxiang Gao

Bio: Jingxiang Gao is an academic researcher from China University of Mining and Technology. The author has contributed to research in topics: Global Positioning System & GNSS applications. The author has an hindex of 12, co-authored 43 publications receiving 414 citations.

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

Abstract: The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.

An Approach to Improve the Positioning Performance of GPS/INS/UWB Integrated System with Two-Step Filter

Abstract: The integration of Inertial Navigation System (INS) and Global Positioning System (GPS) single-point-positioning (SPP) mode cannot meet the requirements of high-accuracy navigation. Range observation through ultra-wideband (UWB) is an effective means to enhance the reliability and accuracy of GPS/INS integrated navigation, particularly in environments where GPS availability is poor. Because it is difficult for UWB signal to achieve large-scale intervention coverage, an enhanced GPS/INS/UWB integrated scheme with positioning error correction is proposed to improve the position accuracy in the UWB signal outage scenario. The position difference between the GPS/INS integrated solution and the GPS/INS/UWB integrated solution is predicated as the error correction for GPS/INS/UWB integrated navigation in a UWB signal challenging environment. Position correction information in the north and east directions is input to the two-step filter to decrease the error of GPS/INS integrated navigation in single-point-positioning. In order to validate the proposed method, a real experiment is conducted. The results indicate that the enhanced GPS/INS/UWB integrated scheme with positioning error correction is able to improve the position accuracy of GPS/INS/UWB integrated navigation when UWB signal is unavailable.

PPP/INS tightly coupled navigation using adaptive federated filter

Abstract: 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 integrated systems that receive pseudorange and Doppler observations can only attain meter-scale accuracy. An INS has also been integrated with double-differenced GPS measurements that remove GPS errors, although this increases system cost. Following the availability of real-time precise orbit and clock products, a precise point positioning PPP/INS tightly coupled navigation system is presented here. Because various types of measurements such as pseudorange, carrier phase and Doppler are available, an adaptive federated filter method is proposed and applied to the PPP/INS integrated system to improve filter efficiency and adaptivity. Provided that the federated local filter and the adaptive filter are equivalent in form, an information allocation factor in the federated filter is constructed based on the adaptive filter factor. Simulation analyses for different INS grades show that the tactical grade INS can provide higher initial value accuracy for PPP. An experiment was performed to validate the new algorithm, and the results indicate that the INS can improve PPP accuracy, especially under challenging positioning conditions. PPP solution accuracy in the east, north and down components can improve by 45, 47 and 24 %, respectively, during partial GPS satellite blockages. The resolution accuracy of the proposed adaptive federated filter is similar to that of a centralized Kalman filter. The proposed method can also realize parallel filter computing and remove the influence of dynamic model errors.

An Improved WiFi/PDR Integrated System Using an Adaptive and Robust Filter for Indoor Localization

Abstract: Location-based services (LBS) are services offered through a mobile device that take into account a device’s geographical location. To provide position information for these services, location is a key process. GNSS (Global Navigation Satellite System) can provide sub-meter accuracy in open-sky areas using satellite signals. However, for indoor and dense urban environments, the accuracy deteriorates significantly because of weak signals and dense multipaths. The situation becomes worse in indoor environments where the GNSS signals are unreliable or totally blocked. To improve the accuracy of indoor positioning for location-based services, an improved WiFi/Pedestrian Dead Reckoning (PDR) integrated positioning and navigation system using an adaptive and robust filter is presented. The adaptive filter is based on scenario and motion state recognition and the robust filter is based on the Mahalanobis distance. They are combined and used in the WiFi/PDR integrated system to weaken the effect of gross errors on the dynamic and observation models. To validate their performance in the WiFi/PDR integrated system, a real indoor localization experiment is conducted. The results indicate that the adaptive filter is better able to adapt to the circumstances of the dynamic model by adjusting the covariance of the process noise and the robust Kalman filter is able to mitigate the harmful effect of gross errors from the WiFi positioning.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

贝叶斯滤波与平滑 (Bayesian filtering and smoothing)

Abstract: Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

Integration of UAV-Based Photogrammetry and Terrestrial Laser Scanning for the Three-Dimensional Mapping and Monitoring of Open-Pit Mine Areas

Abstract: This paper presents a practical framework for the integration of unmanned aerial vehicle (UAV) based photogrammetry and terrestrial laser scanning (TLS) with application to open-pit mine areas, which includes UAV image and TLS point cloud acquisition, image and cloud point processing and integration, object-oriented classification and three-dimensional (3D) mapping and monitoring of open-pit mine areas. The proposed framework was tested in three open-pit mine areas in southwestern China. (1) With respect to extracting the conjugate points of the stereo pair of UAV images and those points between TLS point clouds and UAV images, some feature points were first extracted by the scale-invariant feature transform (SIFT) operator and the outliers were identified and therefore eliminated by the RANdom SAmple Consensus (RANSAC) approach; (2) With respect to improving the accuracy of geo-positioning based on UAV imagery, the ground control points (GCPs) surveyed from global positioning systems (GPS) and the feature points extracted from TLS were integrated in the bundle adjustment, and three scenarios were designed and compared; (3) With respect to monitoring and mapping the mine areas for land reclamation, an object-based image analysis approach was used for the classification of the accuracy improved UAV ortho-image. The experimental results show that by introduction of TLS derived point clouds as GCPs, the accuracy of geo-positioning based on UAV imagery can be improved. At the same time, the accuracy of geo-positioning based on GCPs form the TLS derived point clouds is close to that based on GCPs from the GPS survey. The results also show that the TLS derived point clouds can be used as GCPs in areas such as in mountainous or high-risk environments where it is difficult to conduct a GPS survey. The proposed framework achieved a decimeter-level accuracy for the generated digital surface model (DSM) and digital orthophoto map (DOM), and an overall accuracy of 90.67% for classification of the land covers in the open-pit mine.