scholarly journals Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 590 ◽  
Author(s):  
Shizhuang Wang ◽  
Xingqun Zhan ◽  
Yawei Zhai ◽  
Baoyu Liu
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
High Efficiency ◽  
Hypothesis Test ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Decision Strategy ◽  
Integration Model ◽  
State Prediction ◽  
Tightly Coupled

To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.

scholarly journals Tightly Coupled GNSS/INS Integration with Robust Sequential Kalman Filter for Accurate Vehicular Navigation

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 561 ◽  
Author(s):  
Yi Dong ◽  
Dingjie Wang ◽  
Liang Zhang ◽  
Qingsong Li ◽  
Jie Wu
Keyword(s):  
Kalman Filter ◽  
Computational Efficiency ◽  
Estimation Method ◽  
Accurate Solution ◽  
Global Navigation Satellite Systems ◽  
Loose Coupling ◽  
Tight Coupling ◽  
Integration Model ◽  
Performance Improvements ◽  
Tightly Coupled

With the development of multi-constellation multi-frequency Global Navigation Satellite Systems (GNSS), more and more observations are available for tightly coupled GNSS/Inertial Navigation System (INS) integration. Concerning the accuracy, robustness, and computational burden issues in the integration, we proposed a robust and computationally efficient implementation. The new tight integration model uses pseudorange, Doppler and carrier phase simultaneously, to achieve the maximum possible navigation accuracy for a single receiver. The resultant high-dimensional observation vector is then processed by a sequential Kalman Filter (KF) to improve the computational efficiency in the measurement update step. Based on the innovation of the sequential KF, a robust estimation method with Gaussian test is further devised to detect and adapt the faults in individual GNSS channels. Two field vehicular tests are conducted to evaluate the performance improvements of the proposed method, compared with loose coupling and conventional tight coupling. Test results in favorable environments indicate that the proposed method can significantly improve the velocity and attitude accuracy by 69.42% and 47.16% over loose coupling and by 64.75% and 30.88% over conventional tight coupling, respectively. Moreover, the computational efficiency is also improved by about 53.09% for the proposed method, compared with batch KF processing. In GNSS challenging environments, the proposed method also shows superiority in terms of velocity and attitude accuracy, and better bridging capability during the GNSS partial or complete outages. These results demonstrate that the proposed method is able to provide a more robust and accurate solution in real-time vehicular navigation.

scholarly journals Integration of GNSS-IMU for increasing the observation accuracy in Condensed Areas (Infrastructure and Forest Canopies)

2019 ◽  
Vol 94 ◽  
pp. 03015
Author(s):  
Mokhamad Nur Cahyadi ◽  
Irene Rwabudandi
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Kalman Filters ◽  
Measurement Unit ◽  
Gps Receiver ◽  
Satellite Navigation System ◽  
Loosely Coupled ◽  
Advantages And Disadvantages ◽  
Gps Satellites ◽  
Tightly Coupled

Position determination using satellite navigation system has grown significantly. It provides geospatial with global coverage called GNSS (Global Navigation System Satellite). GNSS satellites consists of GLONASS, GPS (Global positioning system) and Galileo.GPS is the most commonly used system and it is known to its capability to determine 3D position on the surface of the earth. In order to determine the position, a GPS receiver must be able to receive signals from at least four GPS satellites. However, the determination of position in condensed areas such as tunnels, area surrounded by high rise buildings, highly forested and in other closely-knit areas is not achieved because satellite signals cannot reach the receiver in the above-mentioned areas and also others where the signals are reflected before being received by a GPS receiver. In this paper, we present the algorithm to fuse GPS and the inertial measurement unit (IMU) to enable positioning in the above-mentioned Condensed Areas. The standard deviations of the two measurements show that GPS-IMU is better than GPS alone, the standard deviation when satellite outages occurred is - 4.57475 for GPS-IMU measurements and 0.218675 for GPS observations. We presented the results in graphics and it shows that GPS measurements are easily disturbed by external influence such as multipath but GPS-IMU graphic is continuous and robust. The advantages and disadvantages of GPS and INS are complementary and make them work together to enable the accurate measurements in the areas mentioned above. Integration of INS and GNSS can be classified into three types, loosely coupled Kalman filter, tightly coupled Kalman filters and ultratight coupled Kalman filter. In this research we used loosely coupled Kalman filter and tightly coupled Kalman filters to combine GPS and INS in one system.

Multi-Sensor Integrated Navigation in Urban and Indoor Environments

2018 ◽  
pp. 397-442 ◽  
Author(s):  
Mohamed Atia
Keyword(s):  
Sensor Fusion ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Integrated Navigation ◽  
Satellite Systems ◽  
Tightly Coupled ◽  
Sensor Processing ◽  
Global Navigation Satellite ◽  
Dense Urban Environment

The art of multi-sensor processing, or “sensor-fusion,” is the ability to optimally infer state information from multiple noisy streams of data. One major application area where sensor fusion is commonly used is navigation technology. While global navigation satellite systems (GNSS) can provide centimeter-level location accuracy worldwide, they suffer from signal availability problems in dense urban environment and they hardly work indoors. While several alternative backups have been proposed, so far, no single sensor or technology can provide the desirable precise localization in such environments under reasonable costs and affordable infrastructures. Therefore, to navigate through these complex areas, combining sensors is beneficial. Common sensors used to augment/replace GNSS in complex environments include inertial measurement unit (IMU), range sensors, and vision sensors. This chapter discusses the design and implementation of tightly coupled sensor fusion of GNSS, IMU, and light detection and ranging (LiDAR) measurements to navigate in complex urban and indoor environments.

scholarly journals Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2954 ◽  
Author(s):  
Ralf Ziebold ◽  
Daniel Medina ◽  
Michailas Romanovas ◽  
Christoph Lass ◽  
Stefan Gewies
Keyword(s):  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Primary Source ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Doppler Velocity ◽  
Satellite Systems ◽  
Tightly Coupled ◽  
The Baltic

Currently Global Navigation Satellite Systems (GNSSs) are the primary source for the determination of absolute position, navigation, and time (PNT) for merchant vessel navigation. Nevertheless, the performance of GNSSs can strongly degrade due to space weather events, jamming, and spoofing. Especially the increasing availability and adoption of low cost jammers lead to the question of how a continuous provision of PNT data can be realized in the vicinity of these devices. In general, three possible solutions for that challenge can be seen: (i) a jamming-resistant GNSS receiver; (ii) the usage of a terrestrial backup system; or (iii) the integration of GNSS with other onboard navigation sensors such as a speed log, a gyrocompass, and inertial sensors (inertial measurement unit—IMU). The present paper focuses on the third option by augmenting a classical IMU/GNSS sensor fusion scheme with a Doppler velocity log. Although the benefits of integrated IMU/GNSS navigation system have been already demonstrated for marine applications, a performance evaluation of such a multi-sensor system under real jamming conditions on a vessel seems to be still missing. The paper evaluates both loosely and tightly coupled fusion strategies implemented using an unscented Kalman filter (UKF). The performance of the proposed scheme is evaluated using the civilian maritime jamming testbed in the Baltic Sea.

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 UAVS ENHANCED NAVIGATION IN OUTDOOR GNSS DENIED ENVIRONMENT USING UWB AND MONOCULAR CAMERA SYSTEMS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 665-672 ◽  
Author(s):  
S. Zahran ◽  
A. Masiero ◽  
M. M. Mostafa ◽  
A. M. Moussa ◽  
A. Vettore ◽  
...  
Keyword(s):  
Navigation System ◽  
Low Cost ◽  
Global Navigation Satellite Systems ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Satellite Systems ◽  
Monocular Camera ◽  
Camera Systems ◽  
Global Navigation Satellite ◽  
The Cost

<p><strong>Abstract.</strong> The demand for small Unmanned Aerial Vehicles (UAVs) is massively increasing these days, due to the wide variety of applications utilizing such vehicles to perform tasks that may be dangerous or just to save time, effort, or cost. Small UAVs navigation system mainly depends on the integration between Global Navigation Satellite Systems (GNSS) and Inertial Measurement Unit (INS) to estimate the Positions, Velocities, and Attitudes (PVT) of the vehicle. Without GNSS such UAVs cannot navigate for long periods of time depending on INS alone, as the low-cost INS typically exhibits massive accumulation of errors during GNSS absence. Given the importance of ensuring full operability of the UAVs even during GNSS signals unavailability, other sensors must be used to bound the INS errors and enhance the navigation system performance. This paper proposes an enhanced UAV navigation system based on integration between monocular camera, Ultra-Wideband (UWB) system, and INS. In addition to using variable EKF weighting scheme. The paper also investigates this integration in the case of low density of UWB anchors, to reduce the cost required for such UWB system infrastructure. A GoPro Camera and UWB rover were attached to the belly of a quadcopter, an on the shelf commercial drone (3DR Solo), during the experimental flight. The velocity of the vehicle is estimated with Optical Flow (OF) from camera successive images, while the range measurements between the UWB rover and the stationary UWB anchors, which were distributed on the field, were used to estimate UAV position.</p>

scholarly journals An Indoor Navigation System Based on Stereo Camera and Inertial Sensors with Points and Lines

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Yang ◽  
Xiaosu Xu ◽  
Tao Zhang ◽  
Yao Li ◽  
Jinwu Tong
Keyword(s):  
Navigation System ◽  
Inertial Sensors ◽  
Indoor Navigation ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Complex Environments ◽  
Stereo Camera ◽  
Tightly Coupled ◽  
Indoor Navigation System ◽  
Better Than

An indoor navigation system based on stereo camera and inertial sensors with points and lines is proposed to further improve the accuracy and robustness of the navigation system in complex indoor environments. The point and line features, which are fast extracted by ORB method and line segment detector (LSD) method, are both employed in this system to improve its ability to adapt to complex environments. In addition, two different representations of lines are adopted to improve the efficiency of the system. Besides stereo camera, an inertial measurement unit (IMU) is also used in the system to further improve its accuracy and robustness. An estimator is designed to integrate the camera and IMU measurements in a tightly coupled approach. The experimental results show that the performance of the proposed navigation system is better than the point-only VINS and the vision-only navigation system with points and lines.

scholarly journals Identification of Multi-Faults in GNSS Signals using RSIVIA under Dual Constellation

2021 ◽  
Author(s):  
Shuchen Liu ◽  
Jan-Jöran Gehrt ◽  
Dirk Abel ◽  
René Zweigel
Keyword(s):  
Fault Detection ◽  
Satellite System ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrity Monitoring ◽  
Hybrid Strategy ◽  
Multiple Faults ◽  
Fault Detection And Identification ◽  
Tightly Coupled ◽  
Gnss Measurements

This publication presents the development of integrity monitoring and fault detection and exclusion (FDE) of pseudorange measurements, which are used to aid a tightly-coupled navigation filter. This filter is based on an inertial measurement unit (IMU) and is aided by signals of global navigation satellite system (GNSS). Particularly, the GNSS signals include global positioning system (GPS) and Galileo. By using GNSS signals, navigation systems suffer from signal interferences resulting in large pseudorange errors. Further, a higher number of satellites with dual-constellation increases the possibility that satellite observations contain multiple faults. In order to ensure integrity and accuracy of the filter solution, it is crucial to provide sufficient fault-free GNSS measurements for the navigation filter. For this purpose, a new hybrid strategy is applied, combining conventional receiver autonomous integrity monitoring (RAIM) and innovative robust set inversion via interval analysis (RSIVIA). To further improve the performance, as well as the computational efficiency of the algorithm, the estimated velocity and its variance from the navigation filter is used to reduce the size of the RSIVIA initial box.  The designed approach is evaluated with recorded data from an extensive real-world measurement campaign, which has been carried out in GATE Berchtesgaden, Germany. In GATE, up to six Galileo satellites in orbit can be simulated. Further, the signals of simulated Galileo satellites can be manipulated to provide faulty GNSS measurements, such that the fault detection and identification (FDI) capability can be validated. The results show that the designed approach is able to identify the generated faulty GNSS observables correctly and improve the accuracy of the navigation solution. Compared with traditional RSIVIA, the designed new approach provides a more timely fault identification and is more computational efficient.

A Study on Fault Detection Method of Redundant Inertial Navigation System on Micro AUV

2014 ◽  
Vol 709 ◽  
pp. 473-479 ◽  
Author(s):  
Kun Peng He ◽  
Yu Ping Shao ◽  
Lin Zhang ◽  
Shou Lei Hu ◽  
Yuan Li
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Inertial Navigation ◽  
Optimization Method ◽  
Fault Detection And Isolation ◽  
Measurement Unit ◽  
Support Vector ◽  
Ratio Test

In order to improve the precision and reliability of the autonomous underwater vehicle (AUV) inertial navigation system, a redundant inertial measurement unit (RIMU) based on micro electromechanical system (MEMS) inertial sensors has been designed, then use support vector machine theory (SVM),construct multi-fault classifier training and combine three-step search parameter optimization method,to achieve rapid, automatic fault detection and isolation (FDI). With Monte Carlo simulation and experimental analysis, SVM method has more obvious advantages than conventional Generalized Likelihood ratio Test (GLT) on false alarm rate, undetected rate and correct isolation rate for common fault sources of RIMU, and can detect and identify the type and number of failure more effectively on redundant systems, and provide a guarantee for fault sensors isolation.

Fault Detection Method Applied to a Redundant Strapdown Inertial Navigation System

2011 ◽  
Vol 179-180 ◽  
pp. 1242-1247 ◽  
Author(s):  
Yu Rong Lin ◽  
Si Yan Guo ◽  
Guang Ying Zhang
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Detection Method ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Measurement Unit ◽  
Detection Scheme ◽  
Vector Error ◽  
Strapdown Inertial Navigation

A fault detection method applied to a redundant strapdown inertial navigation system, which usually undergoes rapid maneuvers, is developed in this paper. First, an improved four-points detection scheme that can significantly reduce the probability of false alarm of the generalized likelihood test(GLT) is present. Then, based on analyzing influences on the fault detection performance caused by the misalignment and scale fator errors and the random bias of a gyroscope, a parity vector error model is constructed and sequently the Kalman filtering scheme to compensate the parity vector error is designed. By example of a redundant measurement unit with four single-freedom-degree gyros, the fault detection method has been analyzed qualitatively and quantitatively through simulation tests. Simulation results demonstrate the favorable performance of the method.

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