Consensus optimization approach for distributed Kalman filtering: Performance recovery of centralized filtering

Ryu, Kunhee; Back, Juhoon

doi:10.1016/j.automatica.2022.110843

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…The solution process of the discrete standard-type Kalman filtering algorithm can be expressed as shown in the following equations [ 19 , 20 , 21 , 22 ]:

…”

Section: Methodsmentioning

confidence: 99%

A Kalman Filtering Algorithm for Measurement Interruption Based on Polynomial Interpolation and Taylor Expansion

Cheng,

Wang,

et al. 2024

Entropy

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Combined SINS/GPS navigation systems have been widely used. However, when the traditional combined SINS/GPS navigation system travels between tall buildings, in the shade of trees, or through tunnels, the GPS encounters frequent signal blocking, which leads to the interruption of GPS signals, and as a result, the combined SINS/GPS-based navigation method degenerates into a pure inertial guidance system, which will lead to the accumulation of navigation errors. In this paper, an adaptive Kalman filtering algorithm based on polynomial fitting and a Taylor expansion is proposed. Through the navigation information output from the inertial guidance system, the polynomial interpolation method is used to construct the velocity equation and position equation of the carrier, and then the Taylor expansion is used to construct the virtual measurement at the moment of the GPS signal interruption, which can make up for the impact of the lack of measurement information on the combined SINS/GPS navigation system when the GPS signal is interrupted. The results of computer simulation experiments and road measurement tests based on the loosely combined SINS/GPS navigation system show that when the carrier faces a GPS signal interruption situation, compared with a combined SINS/GPS navigation algorithm that does not take any rescue measures, our proposed combined SINS/GPS navigation algorithm possesses a higher accuracy in the attitude angle estimation, a higher accuracy in the velocity estimation, and a higher accuracy in the positional localization, and the system possesses higher stability.

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“…The solution process of the discrete standard-type Kalman filtering algorithm can be expressed as shown in the following equations [ 19 , 20 , 21 , 22 ]:

…”

Section: Methodsmentioning

confidence: 99%

A Kalman Filtering Algorithm for Measurement Interruption Based on Polynomial Interpolation and Taylor Expansion

Cheng,

Wang,

et al. 2024

Entropy

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“…A centralized Kalman filter [11][12][13] can achieve multi-source fusion navigation using the effective information from various sensors for optimal estimation, thus achieving a higher positioning accuracy. However, as the dimension of the state vector increases, the consumption of computational resources increases, and real-time performance deteriorates.…”

Section: Introductionmentioning

confidence: 99%

Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm

et al. 2023

Applied Sciences

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To address the issue of low positioning accuracy in unmanned vehicles navigating in obstructed spaces due to easily contaminated navigation measurement information, an improved adaptive federated Kalman filtering INS/GNSS/VNS integrated navigation algorithm is proposed. In this algorithm, an inertial navigation system (INS) serves as the common reference system, and, together with the global navigation satellite system (GNSS) and visual navigation system (VNS), they form the subsystems that together make up the main system. In the event of faulty measurement values in the subsystems, a combination of the residual chi-square and sliding-window averaging methods are used for fault detection to improve the fault tolerance of the integrated navigation algorithm. Additionally, an adaptive sharing factor is proposed to adjust the accuracy of the integrated navigation algorithm based on the accuracy of the sub-filters. Simulation experiments demonstrated that, compared with classic federated Kalman filtering, the proposed algorithm reduced the root mean square errors (RMSEs) of the three-dimensional position by 56.4%, 54.8%, and 43.4% and the root mean square errors of the three-dimensional velocity by 71.0%, 72.1%, and 28.4% in the event of sub-filter faults, effectively solving the problem of low positioning accuracy for unmanned vehicles in obstructed spaces while ensuring the real-time performance of the system.

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“…Regarding multi-source fusion navigation algorithms, how to further improve the system's accuracy, stability, and real-time performance has attracted many scholars' attention [15]. According to different information processing methods, multi-source fusion navigation algorithms can be roughly divided into centralized and decentralized filter algorithms [16]. For the fusion navigation system with many sensors, the centralized filter algorithm will face the problems of large dimensions of the calculation matrix and the inability to isolate the fault sensors, so there are some problems in practical application.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Source Fusion Navigation System to Overcome GPS Interruption of Unmanned Ground Vehicles

et al. 2023

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The Inertial Navigation System/Global Positioning System (INS/GPS) fusion navigation technology commonly used by unmanned ground vehicles is prone to GPS interruption in complex environments, resulting in the inability of the system to locate accurately. This paper designs an INS/GPS/Odometer/Vision/Magnetometer fusion navigation scheme in response to this problem. Add odometer, vision, and magnetometer to the INS/GPS integrated navigation system, use odometer dead reckoning to suppress the position offset caused by GPS interruption, reduce the cumulative error of the system through visual positioning, and correct the course of INS with the help of magnetometer orientation Angle, improve the positioning ability of the system in the GPS interruption environment. In order to further enhance the positioning accuracy of the system, the Federal Filter algorithm (FKF) was improved, and the Federal Robust Cubature Kalman Filter algorithm (FRCKF) was proposed. A Robust Cubature Kalman Filter algorithm is introduced into the framework of the federated filter algorithm, which improves the processing ability of the FKF algorithm for nonlinear systems and reduces the positioning error of the system. Finally, an experimental platform for unmanned ground vehicle navigation was developed, and the effectiveness of the multi-source fusion navigation system was verified through vehicle navigation experiments. The experiment results show that the multi-source fusion navigation system designed in this paper can effectively reduce the negative impact of GPS interruption on the INS/GPS integrated navigation system and improve the positioning accuracy of unmanned ground vehicles.INDEX TERMS Unmanned ground vehicles, GPS interruption, INS/GPS/odometer/vision/magnetometer fusion navigation system, federal robust cubature Kalman filter algorithm, nonlinear systems.

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Consensus optimization approach for distributed Kalman filtering: Performance recovery of centralized filtering

Cited by 6 publications

References 26 publications

A Kalman Filtering Algorithm for Measurement Interruption Based on Polynomial Interpolation and Taylor Expansion

A Kalman Filtering Algorithm for Measurement Interruption Based on Polynomial Interpolation and Taylor Expansion

Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm

A Multi-Source Fusion Navigation System to Overcome GPS Interruption of Unmanned Ground Vehicles

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