Comparison of Random Forest and Gradient Boosting Fingerprints to Enhance an Outdoor Radio-frequency Localization System

Comparison of Random Forest and Gradient Boosting Fingerprints to Enhance an Outdoor Radio-frequency Localization System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Comparison of Random Forest and Gradient Boosting Fingerprints to Enhance an Outdoor Radio-frequency Localization System Marcelo Nogueira de Sousa, Ricardo Sant'Ana, Riegel P. Fernandes, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-108739/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Machine Learning framework adds a new dimension to the localization estimation problem; it tries to find the most likely position using processed features in a radio map. This paper compares the performance of two machine learning tools, Random Forest (RF) and XGBoost, in exploiting the multipath information for outdoor localization problem. The investigation was carried out in a noisy outdoor scenario, where non-line-of-sight between target and sensors may affect the location of a radio-frequency emitter strongly. It is possible to improve the position system performance by using fingerprints techniques that employ multipath information in a Machine Learning framework, which operate a dataset generated by ray-tracing simulation. Usually, real measurements produce the fingerprints localization features, and there is mismatching with the simulated data. Another drawback of NLOS features extraction is the noise level that occurs in position processing. Random Forest algorithm uses fully grown decision trees to classify possible emitter position, trying to achieve error mitigation by reducing variance. On the other hand, XGBoost approach uses weak learners, defined by high bias and low variance. The results of the simulation performed aims to be used as a design parameter to perform hyperparameter refinements in similar multipath localization problems. Technical Communication Wireless Positioning Hybrid Positioning Machine Learning Ray Tracing Fingerprints Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewer # 3 agreed at journal 25 Dec, 2020 Review # 3 received at journal 25 Dec, 2020 Editorial decision: Major revision 25 Dec, 2020 Review # 2 received at journal 06 Dec, 2020 Reviewer # 2 agreed at journal 02 Dec, 2020 Review # 1 received at journal 30 Nov, 2020 Reviewer # 1 agreed at journal 26 Nov, 2020 Reviewers invited by journal 17 Nov, 2020 Submission checks completed at journal 15 Nov, 2020 Editor assigned by journal 14 Nov, 2020 Editor invited by journal 14 Nov, 2020 First submitted to journal 09 Nov, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-108739","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":4791085,"identity":"11059ad8-74cb-4595-a6d6-5a8690448c01","order_by":0,"name":"Marcelo Nogueira de Sousa","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-4002-3499","institution":"Technische Universitat Ilmenau","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"Nogueira","lastName":"de Sousa","suffix":""},{"id":4791086,"identity":"5597d870-152e-42cc-963c-2a29813fe7fc","order_by":1,"name":"Ricardo Sant'Ana","email":"","orcid":"","institution":"Military Institute of Engineering: Instituto Militar de Engenharia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Sant'Ana","suffix":""},{"id":4791087,"identity":"da2abb08-fae0-4266-8861-c954f633dddf","order_by":2,"name":"Riegel P. 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This paper compares the performance of two machine learning tools, Random Forest (RF) and XGBoost, in exploiting the multipath information for outdoor localization problem. The investigation was carried out in a noisy outdoor scenario, where non-line-of-sight between target and sensors may affect the location of a radio-frequency emitter strongly. It is possible to improve the position system performance by using fingerprints techniques that employ multipath information in a Machine Learning framework, which operate a dataset generated by ray-tracing simulation. Usually, real measurements produce the fingerprints localization features, and there is mismatching with the simulated data. Another drawback of NLOS features extraction is the noise level that occurs in position processing. Random Forest algorithm uses fully grown decision trees to classify possible emitter position, trying to achieve error mitigation by reducing variance. On the other hand, XGBoost approach uses weak learners, defined by high bias and low variance. The results of the simulation performed aims to be used as a design parameter to perform hyperparameter refinements in similar multipath localization problems.\u003c/p\u003e","manuscriptTitle":"Comparison of Random Forest and Gradient Boosting Fingerprints to Enhance an Outdoor Radio-frequency Localization System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-11-19 16:56:09","doi":"10.21203/rs.3.rs-108739/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2020-12-26T00:00:00+00:00","index":3,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-12-26T00:00:00+00:00","index":3,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"decision","content":"Major revision","date":"2020-12-26T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-12-07T00:00:00+00:00","index":2,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2020-12-03T00:00:00+00:00","index":2,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-12-01T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2020-11-27T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-11-18T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-11-15T16:38:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-11-15T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-11-14T23:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-11-10T00:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"eurasip-journal-on-wireless-communications-and-networking","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jwcn","sideBox":"Learn more about [EURASIP Journal on Wireless Communications and Networking](http://jwcn-eurasipjournals.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jwcn/default.aspx","title":"EURASIP Journal on Wireless Communications and Networking","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a73fd333-7754-442c-b06a-eec667a4c0a8","owner":[],"postedDate":"November 19th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":1135721,"name":"Technical Communication"}],"tags":[],"updatedAt":"2021-05-30T14:39:37+00:00","versionOfRecord":[],"versionCreatedAt":"2020-11-19 16:56:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-108739","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-108739","identity":"rs-108739","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}