Optimizing a Bank of Kalman Filters for Navigation Integrity using Efficient Software Design

Sepulveda, Luis E.; Leishman, Robert C.; Kauffman, Kyle; Gipson, Jonathon S.

doi:10.33012/2021.18069

Cited by 7 publications

(1 citation statement)

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“…Additionally, as the number of fused navigation sources increases, the need to establish sub-filters also significantly increases. References [23][24][25][26] proposes a multi-sensor integrity-management model that accomplishes integrity management of multiple sensors by dynamically assigning each sensor to one of four modes: monitoring, validation, calibration, and reconstruction. Although the model has a relatively complete logical structure, as the number of sensors and simultaneously faulty sensors increases, the number of parallel sub-filters that need to be constructed significantly increases, resulting in an escalation of computational load.…”

Section: Introductionmentioning

confidence: 99%

Two-Level Integrity-Monitoring Method for Multi-Source Information Fusion Navigation

Chen,

Zhao

2023

Remote Sensing

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To address the issue of integrity monitoring for a multi-source information fusion navigation system, a theoretical framework of two-level integrity monitoring is proposed. Firstly, at the system level, a system-integrity-monitoring method based on the Kalman filter weighted least-squares form is established to detect and isolate faulty navigation sources. Secondly, at the sensor level, considering the redundancy of the faulty navigation sources, this paper presents the design of a multi-mode comprehensive fault-detection method for non-redundant navigation sources. Additionally, an extended-dimension matrix optimized sensor-fault detection and verification method for redundant navigation sources is proposed. Finally, integrity risk allocation criteria are established based on the effectiveness of navigation sources to calculate the system protection level and trigger integrity alarms. The two-level integrity-monitoring method was tested on a multi-source information fusion navigation system integrated with an inertial navigation system (INS), global positioning system (GPS), BeiDou satellite navigation system (BDS), Doppler velocity log (DVL), barometric altimeter (BA), and terrain-aided navigation (TAN). Test results demonstrate that the proposed method can effectively isolate the faulty navigation source within 10 s. Furthermore, it can detect the faulty sensors within the faulty navigation sources, thereby enhancing the reliability and robustness of the multi-source information fusion navigation system.

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Section: Introductionmentioning

confidence: 99%