scholarly journals A Novel Linear Model Based on Code Approximation for GNSS/INS Ultra-Tight Integration System

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3192 ◽  
Author(s):  
Zhe Yan ◽  
Xiyuan Chen ◽  
Xinhua Tang
Keyword(s):  
Navigation System ◽  
Satellite System ◽  
Linearization Method ◽  
Positioning Errors ◽  
Physical Experiments ◽  
Integration Navigation ◽  
The One ◽  
Global Navigation Satellite ◽  
Order Components ◽  
Tight Integration

The superiority of a global navigation satellite system (GNSS)/inertial navigation system (INS) ultra-tight integration navigation system has been widely verified. For those systems with centralized structure based on coherent-accumulation measurements (I/Q), the conversion from I/Q signals to navigation information is implemented by an observation equation. As a result, the model is highly complex and nonlinear, exerting essential influence on system performance. Based on the analysis of previous studies, a novel model and its linearization method are proposed, aiming at the integrity, stability and implicit nonlinear factors. Unlike the one-order precision in the common Jacobian matrix, two-order components are partly reserved in this model, which makes it possible for higher positioning accuracy and better convergence. For the positioning errors caused by ignoring code-loop deviation, a method to approximate code-phase is proposed without introducing new measurements. Consequently, the effect of code error can be significantly reduced, especially when the tracking loops are unstable. In the end, using real-sampled satellite signals, semi-physical experiments are carried out and the effectiveness and superiority of new methods are proved.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation

2018 ◽  
Vol 8 (11) ◽  
pp. 2322 ◽  
Author(s):  
Lin Zhao ◽  
Mouyan Wu ◽  
Jicheng Ding ◽  
Yingyao Kang
Keyword(s):  
Navigation System ◽  
Density Ratio ◽  
Satellite System ◽  
The Arctic ◽  
Observation Data ◽  
Dual Frequency ◽  
Vector Tracking ◽  
Time Observation ◽  
Global Navigation Satellite ◽  
Navigation Accuracy

The strategic position of the polar area and its rich natural resources are becoming increasingly important, while the northeast and northwest passages through the Arctic are receiving much attention as glaciers continue to melt. The global navigation satellite system (GNSS) can provide real-time observation data for the polar areas, but may suffer low elevation problems of satellites, signals with poor carrier-power-to-noise-density ratio (C/N0), ionospheric scintillations, and dynamic requirements. In order to improve the navigation performance in polar areas, a deep-coupled navigation system with dual-frequency GNSS and a grid strapdown inertial navigation system (SINS) is proposed in the paper. The coverage and visibility of the GNSS constellation in polar areas are briefly reviewed firstly. Then, the joint dual-frequency vector tracking architecture of GNSS is designed with the aid of grid SINS information, which can optimize the tracking band, sharing tracking information to aid weak signal channels with strong signal channels and meet the dynamic requirement to improve the accuracy and robustness of the system. Besides this, the ionosphere-free combination of global positioning system (GPS) L1 C/A and L2 signals is used in the proposed system to further reduce ionospheric influence. Finally, the performance of the system is tested using a hardware simulator and semiphysical experiments. Experimental results indicate that the proposed system can obtain a better navigation accuracy and robust performance in polar areas.

scholarly journals A Multi-Sensor Tight Fusion Method Designed for Vehicle Navigation

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2551 ◽  
Author(s):  
Qifeng Lai ◽  
Hong Yuan ◽  
Dongyan Wei ◽  
Ningbo Wang ◽  
Zishen Li ◽  
...  
Keyword(s):  
Navigation System ◽  
Rapid Development ◽  
Satellite System ◽  
Urban Environments ◽  
Sensor Technology ◽  
Vehicle Navigation ◽  
Noise Compensation ◽  
Vehicle Navigation System ◽  
Tightly Coupled ◽  
Global Navigation Satellite

Using the Global Navigation Satellite System (GNSS), it is difficult to provide continuous and reliable position service for vehicle navigation in complex urban environments, due to the natural vulnerability of the GNSS signal. With the rapid development of the sensor technology and the reduction in their costs, the positioning performance of GNSS is expected to be significantly improved by fusing multi-sensors. In order to improve the continuity and reliability of the vehicle navigation system, we proposed a multi-sensor tight fusion (MTF) method by combining the inertial navigation system (INS), odometer, and barometric altimeter with the GNSS technique. Different fusion strategies were presented in the open-sky, insufficient satellite, and satellite outage environments to check the performance improvement of the proposed method. The simulation and real-device tests demonstrate that in the open-sky context, the error of sensors can be estimated correctly. This is useful for sensor noise compensation and position accuracy improvement, when GNSS is unavailable. In the insufficient satellite context (6 min), with the help of the barometric altimeter and a clock model, the accuracy of the method can be close to that in the open-sky context. In the satellite outage context, the error divergence of the MTF is obviously slower than the traditional GNSS/INS tightly coupled integration, as seen by odometer and barometric altimeter assisting.

scholarly journals Fault Detection and Exclusion Method for a Deeply Integrated BDS/INS System

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1844
Author(s):  
Junren Sun ◽  
Zun Niu ◽  
Bocheng Zhu
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Satellite System ◽  
Navigation Satellite ◽  
Vector Tracking ◽  
Tightly Coupled ◽  
Tracking Loops ◽  
Deep Integration ◽  
Navigation Service ◽  
Global Navigation Satellite

The Inertial Navigation System (INS) is often fused with the Global Navigation Satellite System (GNSS) to provide more robust and superior navigation service, especially in degraded signal environments. Compared with loosely and tightly coupled architectures, the Deep Integration (DI) architecture has better tracking and positioning performance. Information is shared among channels, and the assistant information from INS helps to reduce the dynamic stress of tracking loops. However, this vector tracking architecture may result in easy propagation of errors among tracking channels. To solve this problem, a Fault Detection and Exclusion (FDE) method for the deeply integrated BeiDou Navigation Satellite System (BDS)/INS navigation system is proposed in this paper. This method utilizes pre-filters’ outputs and integration filter’s estimations to form test statistics. These statistics can help to detect and exclude both step errors and Slowly Growing Errors (SGEs) correctly. The monitoring capability of the method was verified by a simulation which was based on a software receiver. The simulation results show that the proposed FDE method works effectively. Additionally, the method is convenient to be implemented in real-time applications because of its simplicity.

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