scholarly journals Navigation Switching Strategy-Based SINS/GPS/ADS/DVL Fault-Tolerant Integrated Navigation System

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Weiqi Li ◽  
Rui Zhang ◽  
Hongjie Lei
Keyword(s):  
Data Processing ◽  
Navigation System ◽  
Fault Tolerant ◽  
Integrated Navigation ◽  
Sensor Faults ◽  
Sensor Failure ◽  
Switching Strategy ◽  
Integrated Navigation System ◽  
Serial Data ◽  
Federated Filtering

Considering the conventional federated filtering-based fault-tolerant integrated navigation system is difficult to be implemented by serial data processing circuits, this paper presents navigation switching strategy-based SINS/GPS/ADS/DVL fault-tolerant integrated navigation system to guarantee the reliability of integrated navigation system under sensor faults. When sensor failure appears, SINS and fault-free sensors are selected successively to form an integrated navigation system, such that reliable navigation parameters can be obtained. The simulation tests are implemented to verify that the SINS/GPS/ADS/DVL integrated navigation system can provide reliable navigation parameters when ADS and DVL are disabled.

scholarly journals Research on Wavelet Singularity Detection Based Fault-Tolerant Federated Filtering Algorithm for INS/GPS/DVL Integrated Navigation System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianhua Cheng ◽  
Daidai Chen ◽  
Rene Landry Jr. ◽  
Lin Zhao ◽  
Dongxue Guan
Keyword(s):  
Navigation System ◽  
Fault Tolerant ◽  
Integrated Navigation ◽  
Singularity Detection ◽  
Integrated Navigation System ◽  
Lipschitz Exponent ◽  
Filtering Algorithm ◽  
Federated Filtering ◽  
Soft Faults ◽  
Modulus Maxima

Soft faults in navigation sensors will lead to the degradation of the accuracy and reliability of integrated navigation system. To solve this problem, a wavelet analysis and signal singularities based soft fault detection method are given out. To find signal singularities and detect the faults, the modulus maxima values are calculated after the wavelet transform of original signal. By calculating the Lipschitz exponent using the modulus maxima value at the fault point, the fault types are distinguished. Then, a fault-tolerant federated filtering algorithm for the calibration of INS/GPS/DVL integrated navigation system is proposed. Simulations are conducted and results show that sensor soft faults can be detected accurately. By effectively isolating the fault and refactoring information, the accuracy and reliability of navigation system are improved.

scholarly journals A SINS/BDS Integrated Navigation Method Based on Classified Weighted Adaptive Filtering

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Xuchao Kang ◽  
Guangjun He ◽  
Xingge Li
Keyword(s):  
Adaptive Filtering ◽  
Navigation System ◽  
Fault Tolerant ◽  
Hypothesis Test ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Covariance Matching ◽  
Uncertain Model ◽  
Navigation Accuracy ◽  
Navigation Method

Aiming at the problem that the accuracy and stability of SINS/BDS integrated navigation system decrease due to uncertain model and observation anomalies, a SINS/BDS integrated navigation method based on classified weighted adaptive filtering is proposed. Firstly, the innovation covariance matching technology is used to detect whether there is any abnormality in the system as a whole. Then the types of anomalies are distinguished by hypothesis test. Different types of anomalies have different effects on state estimation. Based on the dynamic changes of innovation, different adaptive weighting methods are adopted to correct navigation information. The simulation results show that this method can effectively improve the fault-tolerant performance of integrated navigation system in complex environment with unknown anomaly types. When both model anomalies and observation anomalies exist, the speed and position accuracy are increased by 42% and 24% compared with the standard KF, 38% and 22% compared with the innovation orthogonal adaptive filtering, which has higher navigation accuracy.

Application of Federated Filter to AUV Based on Terrain-Aided SINS

2014 ◽  
Vol 711 ◽  
pp. 338-341 ◽  
Author(s):  
Qi Wang ◽  
Cheng Shan Qian ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filtering ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Filtering Method ◽  
Long Time ◽  
Federated Filtering ◽  
Strapdown Inertial Navigation

To improve the navigation precision and reliability of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Federated Filter (FF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and Federated Filtering method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the Federated Filtering method is able to improve the long-time navigation precision and reliability, relative to the traditional Kalman Filtering method.

scholarly journals A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaosu Xu ◽  
Peijuan Li ◽  
Jian-juan Liu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Fault Tolerant ◽  
Doppler Velocity ◽  
Observation Data ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Data Set ◽  
Integrated Navigation System ◽  
Short Period

The Kalman filter (KF), which recursively generates a relatively optimal estimate of underlying system state based upon a series of observed measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system model and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless data set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF) algorithm for the integrated navigation system composed of a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), and a magnetic compass (MCP) is proposed. The evolutionary artificial neural networks (EANN) are used in self-learning and training of the intelligent data fusion algorithm. The proposed algorithm can significantly outperform the traditional KF in providing estimation continuously with higher accuracy and smoothing the KF outputs when observation data are inaccurate or unavailable for a short period. The experiments of the prototype verify the effectiveness of the proposed method.

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