To improve the seamless navigation ability of an integrated Global Positioning System (GPS)/inertial navigation system in GPS-denied environments, a hybrid navigation strategy called the ...self-learning square-root- cubature Kalman filter (SL-SRCKF) is proposed in this article. The SL-SRCKF process contains two innovative steps: 1) it provides the traditional SRCKF with a self-learning ability, which means that navigation system observations can be provided continuously, even during long-term GPS outages; and 2) the relationship between the current Kalman filter gains and the optimal estimation error is established, which means that the optimal estimation accuracy can be improved by error compensation. The superiority of the proposed SL-SRCKF strategy is verified via experimental results and prominent advantages of this approach include: 1) the SL-SRCKF comprises two cycle filtering systems that work in a tightly coupled mode, and this allows more accurate error correction results to be obtained during GPS outages; 2) the system's error prediction ability is effectively improved by introducing a long short-term memory, which provides much better performance than other neural networks, such as random forest regression or the recursive neural network; and 3) under different (30, 60, and 100 s) GPS outage conditions, the long-term stability of SL-SRCKF is much better than that of other error correction approaches.
The article discusses the using of the rubidium - 87 quantum frequency standard for satellite navigation systems. A method for improving the parameters of the microwave excitation signal to enhance ...the short-term and long-term stability of the standard is proposed. The results of experimental studies are presented.
•MEMS-INS, GPS and polarization compass are integrated together for navigation.•A multi-rate algorithm is proposed to solve the problem caused by different sampling frequencies.•Singular value ...decomposition is used to improve the numerical stability of the algorithm.
The microelectromechanical system-inertial navigation system (MEMS-INS) is one of the most widely used sensor systems in navigation but must be used with other sensors because of its error accumulation characteristics. The information fusion method for integrated navigation systems based on filtering technology is thus very important. This paper introduces a new adaptive Kalman filter for nonlinear integrated systems. An improved multi-rate strong tracking square-root cubature Kalman filter (MR-STSCKF) for a MEMS-INS/Global Positioning System (GPS)/polarization compass integrated navigation system is proposed. The proposed filter is used to estimate the system covariance adaptively. The proposed approach can overcome the problem of the inconsistency between the sampling frequencies of different sensors while maintaining the high precision of the integrated navigation results. Experimental results demonstrate that the proposed MR-STSCKF algorithm is effective in improving the accuracy of the MEMS-INS/GPS/polarization compass integrated navigation system with a high sampling frequency.
This paper focuses on solving state estimation problems of nonlinear systems whose states evolve on matrix Lie groups and measurements live in the Euclidean space under non-Gaussian measurement ...noise. Building upon the maximum correntropy criterion and the invariant Kalman filtering theory, a generalized robust extended Kalman filtering algorithm on matrix Lie groups is derived from maximizing a novel optimization cost function. This cost function is introduced by considering the correntropy and the system geometry structure. Then, two alternative versions of the proposed filtering algorithm are provided according to the distinct processing approaches of the nonlinear measurement function in the cost function. A Gaussian-Newton method on Lie groups and a fixed-point iterative method are applied to update the posterior estimation. In addition, sufficient conditions are given to ensure the convergence of fixed-point iterative steps. Extensive Monte-Carlo simulations on an INS/GPS integrated navigation system and experiments on open-source datasets validate the theoretical analysis and illustrate the effectiveness and robustness of the proposed filter.
Accurate positioning is an essential requirement of autonomous vehicular navigation system (AVNS) for safe driving. Although the vehicle position can be obtained in global position system friendly ...environments, in GPS denied environments (such as suburb, tunnel, forest, or underground scenarios) the positioning accuracy of AVNS is easily reduced by the trajectory error of the vehicle. In order to solve this problem, the plane, sphere, cylinder and cone are often selected as the ground control targets to eliminate the trajectory error for AVNS. However, these targets usually suffer from the limitations of incidence angle, measuring range, scanning resolution, and point cloud density, etc. To bridge this research gap, an adaptive continuum shape constraint analysis (ACSCA) method is presented in this article to design a new target with optimized identifiable specific shape to eliminate the trajectory error for AVNS. First of all, according to the proposed ACSCA method, we conduct extensive numerical simulations to explore the optimal ranges of the vertexes and the faces for target shape design, and based on these trials, the optimal target shape is found as icosahedron, which composes of ten vertexes, 20 faces and combines the properties of plane and volume target. Moreover, the algorithm of automatic detection and coordinate calculation is developed to recognize the icosahedron target and calculate its coordinates information for AVNS. Finally, a series of experimental investigation were performed to evaluate the effectiveness of the designed icosahedron target in GPS denied environments. The experimental results demonstrate that compared with the plane, sphere, cylinder and cone targets, the developed icosahedron target can produce better performances than the above targets in terms of the clustered minimum registration error, ambiguity and range of field-of-view; also can significantly improve the positioning accuracy of AVNS in GPS denied environments.
We report on a new test of the gravitational redshift and thus of local position invariance, an integral part of the Einstein equivalence principle, which is the foundation of general relativity and ...all metric theories of gravitation. We use data spanning 1008 days from two satellites of Galileo, Europe's global satellite navigation system, which were launched in 2014, but accidentally delivered on elliptic rather than circular orbits. The resulting modulation of the gravitational redshift of the onboard atomic clocks allows the redshift determination with high accuracy. Additionally, specific laser ranging campaigns to the two satellites have enabled a good estimation of systematic effects related to orbit uncertainties. Together with a careful conservative modeling and control of other systematic effects we measure the fractional deviation of the gravitational redshift from the prediction by general relativity to be (0.19±2.48)×10^{-5} at 1 sigma, improving the best previous test by a factor 5.6. To our knowledge, this represents the first reported improvement on one of the longest standing results in experimental gravitation, the Gravity Probe A hydrogen maser rocket experiment back in 1976.
Navigation systems engineering is a red-hot area. More and more technical professionals are entering the field and looking for practical, up-to-date engineering know-how. This single-source reference ...answers the call, providing both an introduction to overall systems operation and an in-depth treatment of architecture, design, and component integration. This book explains how satellite, on-board, and other navigation technologies operate, and it gives practitioners insight into performance issues such as processing chains and error sources. Providing solutions to systems designers and engineers, the book describes and compares different integration architectures, and explains how to diagnose errors. Moreover, this hands-on book includes appendices filled with terminology and equations for quick referencing.
Cellular systems evolved from a dedicated mobile communication system to an almost omnipresent system with unlimited coverage anywhere and anytime for any device. The growing ubiquity of the network ...stirred expectations to determine the location of the mobile devices themselves. Since the beginning of standardization, each cellular mobile radio generation has been designed for communication services, and satellite navigation systems, such as Global Positioning System (GPS), have provided precise localization as an add-on service to the mobile terminal. Self-contained localization services relying on the mobile network elements have offered only rough position estimates. Moreover, satellite-based technologies suffer a severe degradation of their localization performance in indoors and urban areas. Therefore, only in subsequent cellular standard releases, more accurate cellular-based location methods have been considered to accommodate more challenging localization services. This survey provides an overview of the evolution of the various localization methods that were standardized from the first to the fourth generation of cellular mobile radio, and looks over what can be expected with the new radio and network aspects for the upcoming generation of fifth generation.
The integrated navigation system, which consists of the global navigation satellite system (GNSS) and inertial navigation system (INS), is widely used in various platforms. However, when the GNSS ...signal is unavailable, the GNSS/INS integrated navigation system will be converted into INS working alone. The error will gradually diverge. To solve this problem, this article constructs a hybrid neural network model composed of convolutional neural network (CNN) and gated recurrent unit (GRU). It combines the Pseudo-measurement information of GNSS predicted by the model with INS for integrated navigation to compensate for the interruption of GNSS and correct the error of INS. At the same time, considering that the predicted GNSS position information has a significant error, if the estimation is not accurate, the filtering accuracy of the system will decrease. Therefore, this article proposes an improved robust adaptive Kalman filter (IRAKF) algorithm to estimate the measurement noise covariance matrix for GNSS pseudo-measurement information. The actual road test results show that the addition of the CNN-GRU model and IRAKF algorithm improves the overall accuracy of the system.
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