Fault Identification Method of GNSS/INS Integrated Navigation System Based on the Fusion of Chi-Square Test and Multiple Solution Separation Algorithm

2021 ◽  
pp. 558-569
Author(s):  
Xin Li ◽  
Kun Fang ◽  
Xiao Li ◽  
Jichao Dong ◽  
Zhipeng Wang
Keyword(s):  
Navigation System ◽  
Multiple Solution ◽  
Fault Identification ◽  
Integrated Navigation ◽  
Separation Algorithm ◽  
Chi Square ◽  
Integrated Navigation System ◽  
Identification Method ◽  
Chi Square Test

scholarly journals A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5909
Author(s):  
Guangle Gao ◽  
Shesheng Gao ◽  
Genyuan Hong ◽  
Xu Peng ◽  
Tian Yu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Reliability ◽  
Statistical Characteristics ◽  
Integrated Navigation ◽  
Chi Square ◽  
Integrated Navigation System ◽  
Robust Kalman Filter ◽  
Chi Square Test ◽  
Spectral Redshift

In order to achieve a highly autonomous and reliable navigation system for aerial vehicles that involves the spectral redshift navigation system (SRS), the inertial navigation (INS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integrated system is designed and the spectral-redshift-based velocity measurement equation in the INS/SRS/CNS system is derived. Furthermore, a new chi-square test-based robust Kalman filter (CSTRKF) is also proposed in order to improve the robustness of the INS/SRS/CNS navigation system. In the CSTRKF, the chi-square test (CST) not only detects measurements with outliers and in non-Gaussian distributions, but also estimates the statistical characteristics of measurement noise. Finally, the results of our simulations indicate that the INS/SRS/CNS integrated navigation system with the CSTRKF possesses strong robustness and high reliability.

A Dynamic Vector-Formed Information Sharing Algorithm Based on Two-State Chi Square Detection in an Adaptive Federated Filter

2018 ◽  
Vol 72 (1) ◽  
pp. 101-120 ◽  
Author(s):  
Jianxin Xu ◽  
Zhi Xiong ◽  
Jianye Liu ◽  
Rong Wang
Keyword(s):  
Information Sharing ◽  
Navigation System ◽  
Detection Algorithm ◽  
Distribution Coefficients ◽  
Fault Isolation ◽  
Integrated Navigation ◽  
Chi Square ◽  
Integrated Navigation System ◽  
Filter Architecture ◽  
Soft Faults

The accuracy and fault tolerance of filters are directly affected by the filter architecture and algorithm, thus influencing navigation performance. The chi square detection used in the conventional reset federated filter is not sensitive to soft faults, and it is easy to cause the health subsystem to be polluted through information sharing. It is a challenge to design an adaptive reset federated filter to improve the performance of the navigation system. Therefore, taking the Strapdown Inertial Navigation System/Global Positioning System/Celestial Navigation System/Synthetic Aperture Radar (SINS/GPS/CNS/SAR) integrated navigation system as an example, an adaptive federated filter architecture for vector-formed information sharing without a fault isolation module is designed in this paper. The proposed method uses the two-state chi square detection algorithm to calculate the parameters corresponding to each state, making the state with higher accuracy obtain a greater information distribution coefficient. In addition, according to the value of vector-formed information sharing, an adaptive coefficient of measurement noise is designed. This improves the adaptability of the navigation system to soft faults. Simulation results show that the accuracy of the proposed algorithm has the same performance compared with the conventional method under normal circumstances. When the sensor has a soft fault, the adaptive federated filter algorithm proposed in this paper can adaptively adjust the distribution coefficients, eliminate the influence of the fault information and improve the precision of the navigation system. The approach described in this paper can be used in multi-sensor integrated navigation. It will have better performance in engineering applications.

scholarly journals Ship Security Relative Integrated Navigation with Injected Fault Measurement Attack and Unknown Statistical Property Noises

2020 ◽  
Vol 8 (5) ◽  
pp. 305 ◽  
Author(s):  
Yongjing Wang ◽  
Yi Wang ◽  
Xiaoliang Feng
Keyword(s):  
Navigation Systems ◽  
Integrated Navigation ◽  
Chi Square ◽  
Integrated Navigation System ◽  
Statistical Property ◽  
Estimate Error ◽  
Chi Square Test ◽  
Ship Security ◽  
Navigation Method ◽  
Energy Property

In this work, the ship relative integrated navigation approaches are studied for the navigation scenarios with the measurements disturbed by unknown statistical property noises and with the injected fault measurement attacks. On the basis of the limited energy property of system noises, the navigation states are estimated by the local finite horizon H∞ filter to satisfy the performance index function. Then, the local estimates are fused in the relative integrated navigation system with the weight fusion parameters obtained by using the local estimate error measurements. Further, the injected fault measurement attacks are considered in the relative integrated navigation systems. Due to the system noises and the measurement noises having unknown statistical property, the classical Chi-square test can hardly be utilized to detect the injected fault measurements. Therefore, a secure relative integrated navigation method is proposed with a distance-based clustering detector. The finial simulation results illustrate the effectiveness of the proposed relative integrated navigation approach and the proposed secure relative integrated navigation approach.

scholarly journals Attack Detection/Isolation via a Secure Multisensor Fusion Framework for Cyberphysical Systems

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Arash Mohammadi ◽  
Chun Yang ◽  
Qing-wei Chen
Keyword(s):  
Attack Detection ◽  
Cyberphysical Systems ◽  
Integrated Navigation ◽  
Multisensor Fusion ◽  
Chi Square ◽  
Integrated Navigation System ◽  
Injection Attacks ◽  
Chi Square Test ◽  
Non Gaussian ◽  
Fusion Framework

Motivated by rapid growth of cyberphysical systems (CPSs) and the necessity to provide secure state estimates against potential data injection attacks in their application domains, the paper proposes a secure and innovative attack detection and isolation fusion framework. The proposed multisensor fusion framework provides secure state estimates by using ideas from interactive multiple models (IMM) combined with a novel fuzzy-based attack detection/isolation mechanism. The IMM filter is used to adjust the system’s uncertainty adaptively via model probabilities by using a hybrid state model consisting of two behaviour modes, one corresponding to the ideal scenario and one associated with the attack behaviour mode. The state chi-square test is then incorporated through the proposed fuzzy-based fusion framework to detect and isolate potential data injection attacks. In other words, the validation probability of each sensor is calculated based on the value of the chi-square test. Finally, by incorporation of the validation probability of each sensor, the weights of its associated subsystem are computed. To be concrete, an integrated navigation system is simulated with three types of attacks ranging from a constant bias attack to a non-Gaussian stochastic attack to evaluate the proposed attack detection and isolation fusion framework.

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