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.

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 Optimal Fault Detection and Exclusion Applied in GNSS Positioning

2013 ◽  
Vol 66 (5) ◽  
pp. 683-700 ◽  
Author(s):  
Ling Yang ◽  
Nathan L. Knight ◽  
Yong Li ◽  
Chris Rizos
Keyword(s):  
Fault Detection ◽  
Global Navigation Satellite System ◽  
Single Point ◽  
Satellite System ◽  
Navigation Satellite ◽  
Gnss Positioning ◽  
Practical Strategy ◽  
Receiver Autonomous Integrity Monitoring ◽  
Global Navigation Satellite ◽  
Made In

In Global Navigation Satellite System (GNSS) positioning, it is standard practice to apply the Fault Detection and Exclusion (FDE) procedure iteratively, in order to exclude all faulty measurements and then ensure reliable positioning results. Since it is often only necessary to consider a single fault in a Receiver Autonomous Integrity Monitoring (RAIM) procedure, it would be ideal if a fault could be correctly identified. Thus, fault detection does not need to be applied in an iterative sense. One way of evaluating whether fault detection needs to be reapplied is to determine the probability of a wrong exclusion. To date, however, limited progress has been made in evaluating such probabilities. In this paper the relationships between different parameters are analysed in terms of the probability of correct and incorrect identification. Using this knowledge, a practical strategy for incorporating the probability of a wrong exclusion into the FDE procedure is developed. The theoretical findings are then demonstrated using a GPS single point positioning example.

scholarly journals A Subset-Reduced Method for FDE ARAIM of Tightly-Coupled GNSS/INS

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4847
Author(s):  
Weichuan Pan ◽  
Xingqun Zhan ◽  
Xin Zhang ◽  
Shizhuang Wang
Keyword(s):  
Global Navigation Satellite System ◽  
Inertial Navigation System ◽  
Satellite System ◽  
Civil Aviation ◽  
Navigation Satellite ◽  
Tightly Coupled ◽  
Simulation Results ◽  
Receiver Autonomous Integrity Monitoring ◽  
Global Navigation Satellite ◽  
Advanced Receiver

The advanced receiver autonomous integrity monitoring (advanced RAIM, ARAIM) is the next generation of RAIM which is widely used in civil aviation. However, the current ARAIM needs to evaluate hundreds of subsets, which results in huge computational loads. In this paper, a method using the subset excluding entire constellation to evaluate the single satellite fault subsets and the simultaneous multiple satellites fault subsets is presented. The proposed ARAIM algorithm is based on the tight integration of the global navigation satellite system (GNSS) and inertial navigation system (INS). The number of subsets that the proposed GNSS/INS ARAIM needs to consider is about 2% of that of the current ARAIM, which reduces the computational load dramatically. The detailed fault detection (FD) process and fault exclusion (FE) process of the proposed GNSS/INS ARAIM are provided. Meanwhile, the method to obtain the FD-only integrity bound and the after-exclusion integrity bound is also presented in this paper. The simulation results show that the proposed GNSS/INS ARAIM is able to find the failing satellite accurately and its integrity performance is able to meet the integrity requirements of CAT-I precision approach.

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.

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