Combination and selection of machine learning algorithms in GNSS architecture design for concurrent executions with HIL testing

Simple item page
dc.contributor.authorXu, Zhengjia
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorGrech, Raphael
dc.contributor.authorPeltola, Pekka
dc.contributor.authorTiwari, Smita
dc.date.accessioned2024-01-09T14:19:30Z
dc.date.available2024-01-09T14:19:30Z
dc.date.issued2023-11-10
dc.description.abstractAs machine learning (ML) continuing to gain popularity, ML-assisted Global Navigation Satellite System (GNSS) receivers facilitate the performance of Autonomous Systems (AS) navigation solutions. However, selections of ML is often a trade-off in practice where empirical knowledge is taken to alleviate complexities. Therefore, this paper explores decision-making solutions for maximising determined hardware performance under quantitative and qualitative considerations. This work proposes Algorithm Selection and Matching with Fuzzy Analytic Hierarchy Process (ASM-FAHP) that maps multiple trade-off concerns into a Multi-Criteria Decision-Making (MCDM) problem. The ASM-FAHP firstly searches all the possible alternatives to find possible combinations with hardware resource limitations taken into account. Afterwards, ASM-FAHP prioritizes the most significant candidate by constructing a hierarchical structure with several attributes and scoring with fuzzy numbers. Hereby, the most suitable ML combinations are determined by calculating synthesised fuzzy weights per each alternative. The performance of the ML combination is evaluated by concurrently executing it on resource-constrained hardware, specifically the Jetson Nano board. The ML models are trained and tested using high-fidelity synthetic datasets produced from Spirent GSS7000 simulator and SimGen while connected to hardware-in-the-loop (HIL). It has been discovered that when approaching hardware limits, the selected combination of machine learning algorithms makes full use of memory resources but sacrifices processing speed.en_UK
dc.description.sponsorshipThis work is performed under the ESA-funded project VTL4AV (NAVISPEL1-066 bis).en_UK
dc.identifier.citationZhengjia X, Petrunin I, Tsourdos A, et al., (2023) Combination and selection of machine learning algorithms in GNSS architecture design for concurrent executions with HIL testing. 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), 1-5 October 2023, Barcelona, Spainen_UK
dc.identifier.eisbn979-8-3503-3357-2
dc.identifier.eissn2155-7209
dc.identifier.isbn979-8-3503-3358-9
dc.identifier.issn2155-7195
dc.identifier.urihttps://doi.org/10.1109/DASC58513.2023.10311160
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20625
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectGNSS receiver designen_UK
dc.subjectmachine learningen_UK
dc.subjectdeep learningen_UK
dc.subjectalgorithm selectionen_UK
dc.subjectFAHPen_UK
dc.titleCombination and selection of machine learning algorithms in GNSS architecture design for concurrent executions with HIL testingen_UK
dc.typeConference paperen_UK
dcterms.dateAccepted2023-04-22

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Machine_learning_algorithms_in_GNSS-2023.pdf
Size:
3.26 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)