A State‐Domain Robust Chi‐Square Test Method for GNSS/INS Integrated Navigation

Yu, Zhangjun; Zhang, Qiuzhao; Yu, Ke; Zheng, Nanning

doi:10.1155/2021/1745383

Cited by 10 publications

(2 citation statements)

References 20 publications

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“…Monitoring using parameter (10) allows you to determine the state of the DS as a whole without diagnosing which element of the observation vector the violation is associated with. This approach is traditional [5,6] for DS monitoring. In practice, there is a need to diagnose DS with a depth up to the element of the observation vector.…”

Section: Fault Detection By the χ 2 Chi-square Criterionmentioning

confidence: 99%

Application of the Kalman Filter in Monitoring, Diagnosis, and Fault Parrying Problems for Observable Dynamical Systems

Chernodarov

2024

Applications and Optimizations of Kalman Filter and Their Variants

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This section is devoted to approaches for the formation, estimation and application of diagnostic parameters, as well as stochastic models in monitoring problems. The procedures for increasing the reliability and depth of dynamic systems monitoring are substantiated based on the decomposition of diagnostic models, the use of combined statistical criteria and the processing of observations in “forward” and “backward” time using the extended Kalman filter. The possibility of detecting and parrying anomalous observations using robust filtering procedures is shown. Such procedures are based on the use of an influence function that establishes the level of confidence for formed observations. U-D modification of the Kalman filter with an influence function in the observation selection loop is presented. Here U is an upper triangular matrix with unit diagonal elements and D is a diagonal matrix. Results of the mathematical simulation are given.

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Section: Fault Detection By the χ 2 Chi-square Criterionmentioning

confidence: 99%

Application of the Kalman Filter in Monitoring, Diagnosis, and Fault Parrying Problems for Observable Dynamical Systems

Chernodarov

2024

Applications and Optimizations of Kalman Filter and Their Variants

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“…To improve the efficiency of fault detection and enhance the fault-tolerant of the system. Yu et al [16] proposes a state chi-square fault detection algorithm, based on residual chi-square. This algorithm utilizes the disparity between the previous innovation residual and the subsequent innovation residual to create the fault detection function.…”

Section: Introductionmentioning

confidence: 99%

A GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width

Zhao,

Wang,

Gao

et al. 2024

Meas. Sci. Technol.

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The Global Navigation Satellite System/Strapdown Inertial Navigation System (GNSS/SINS) integrated navigation system is an important technology for UAV measurement and vehicle movement measurement. But in the operational process of the GNSS/SINS integrated navigation system, the Global Navigation Satellite System (GNSS) signal is vulnerable to external interference, resulting in abnormal system measurement data, and system faults. These faults will reduce the navigation and positioning performance of the system and reduce the measurement accuracy of the system. Aiming at this problem, a GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width is proposed. The algorithm evaluates the system measurement data based on the innovation residual and incorporates the sliding average filter to design the fault detection function based on the smooth bounded width layer. Accurate detection of system faults using fault detection function. The fault detection function value is used to construct the robust cofactor matrix to correct the measurement error in real-time, to improve the accuracy and robustness of the state estimation. The experimental results show that: The proposed algorithm in the paper compares with two traditional robust adaptive algorithms based on smooth bounded layer fault detection and residual chi-square fault detection. The fault detection rates for small step faults show an increase of more than 44.26% and 9.54%, respectively. Similarly, for slowly varying faults, the fault detection rates exhibit an increase of more than 29.32% and 13.56%, respectively. Throughout the fault, the filtering accuracy demonstrates an increase of more than 16.52% and 15.47%, respectively. The algorithm effectively improves the measurement accuracy of the GNSS/SINS integrated navigation system.

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Monitoring and Adaptive Robust Protection of the Integrity of GNSS/SINS Observations in Urban Environments

Chernodarov

2022

IFAC-PapersOnLine

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A State‐Domain Robust Chi‐Square Test Method for GNSS/INS Integrated Navigation

Cited by 10 publications

References 20 publications

Application of the Kalman Filter in Monitoring, Diagnosis, and Fault Parrying Problems for Observable Dynamical Systems

Application of the Kalman Filter in Monitoring, Diagnosis, and Fault Parrying Problems for Observable Dynamical Systems

A GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width

Monitoring and Adaptive Robust Protection of the Integrity of GNSS/SINS Observations in Urban Environments

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