Intelligent fault detection and fusion for INS/GPS navigation system

2013 ◽  
Author(s):  
M. A. Jaradat ◽  
M. F. Abdel-Hafez ◽  
K. Saadeddin ◽  
M. A. Jarrah
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Gps Navigation

An Accurate Land-Vehicle MEMS IMU/GPS Navigation System Using 3D Auxiliary Velocity Updates

Navigation ◽  
2007 ◽  
Vol 54 (3) ◽  
pp. 177-188 ◽  
Author(s):  
XIAOJI NIU ◽  
SAMEH NASSAR ◽  
NASER EL-SHEIMY
Keyword(s):  
Navigation System ◽  
Land Vehicle ◽  
Gps Navigation ◽  
Mems Imu

Study on GPS Navigation Navigation System

2014 ◽  
Vol 556-562 ◽  
pp. 3313-3316
Author(s):  
Hao Ran Song
Keyword(s):  
Global Positioning System ◽  
Navigation System ◽  
Systems Design ◽  
Design System ◽  
Factors Affecting ◽  
Gps Navigation ◽  
Communications Technologies ◽  
Global Positioning ◽  
Depth Study ◽  
System Requirements

Currently, GPS global positioning system has been in the areas of precise positioning, navigation, timing has been widely used. By GPS-OEM combined with computer and communications technologies, users are able to easily and independently developed to meet the specific needs of GPS systems. Design system based on GPS navigation navigation system requirements package, in-depth study of the main factors affecting the precision of navigation. Several factors made corresponding solutions, implements navigation navigation system the main functions of the software.

scholarly journals An Integration Method of Inertial Navigation System and Three-Beam Lidar for the Precision Landing

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyue Zhang ◽  
Pengbo Liu ◽  
Chunxi Zhang
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Integration Method ◽  
Inertial Navigation ◽  
Measurement Model ◽  
Reconstruction Method ◽  
Integrated Navigation ◽  
Fault Resistance ◽  
Vehicle Test

To ensure the high accuracy, independence, and reliability of the measurement system in the unmanned aerial vehicle (UAV) landing process, an integration method of inertial navigation system (INS) and the three-beam Lidar is proposed. The three beams of Lidar are, respectively, regarded as an independent sensor to integrate with INS according to the conception of multisensor fusion. Simultaneously, the fault-detection and reconstruction method is adopted to enhance the reliability and fault resistance. First the integration method is described. Then the strapdown inertial navigation system (SINS) error model is introduced and the measurement model of SINS/Lidar integrated navigation is deduced under Lidar reference coordinate. The fault-detection and reconstruction method is introduced. Finally, numerical simulation and vehicle test are carried out to demonstrate the validity and utility of the proposed method. The results indicate that the integration can obtain high precision navigation information and the system can effectively distinguish the faults and accomplish the reconstruction to guarantee the normal navigation when one or two beams of the Lidar malfunction.

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.

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