Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression
We consider the robust localization, via Gaussian process regression (GPR), of multiple transmitters/targets based on received signal strength indicator (RSSI) data collected by fixed sensors distributed in the environment. For such a scenario and approach, we contribute both with a novel noise robu...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Indoor and Seamless Positioning and Navigation |
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| Online Access: | https://ieeexplore.ieee.org/document/10314734/ |
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| author | Niclas Fuhrling Hyeon Seok Rou Giuseppe Thadeu Freitas de Abreu David Gonzalez G. Osvaldo Gonsa |
| author_facet | Niclas Fuhrling Hyeon Seok Rou Giuseppe Thadeu Freitas de Abreu David Gonzalez G. Osvaldo Gonsa |
| author_sort | Niclas Fuhrling |
| collection | DOAJ |
| description | We consider the robust localization, via Gaussian process regression (GPR), of multiple transmitters/targets based on received signal strength indicator (RSSI) data collected by fixed sensors distributed in the environment. For such a scenario and approach, we contribute both with a novel noise robust procedure to train the parameters of the GPR model, which is achieved via a mini-batch stochastic gradient descent (SGD) scheme with gradients given in closed form, and with a pair of corresponding robust marginalization procedures for the estimation of target locations. Simulation results validate the contributions by showing that the proposed methods significantly outperform the best related state-of-the-art (SotA) alternative and approach the performance of a genie-aided (GA) scheme. |
| format | Article |
| id | doaj-art-9daaaea8ae4d4e2e9536879ad701bc5e |
| institution | DOAJ |
| issn | 2832-7322 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Indoor and Seamless Positioning and Navigation |
| spelling | doaj-art-9daaaea8ae4d4e2e9536879ad701bc5e2025-08-20T02:57:17ZengIEEEIEEE Journal of Indoor and Seamless Positioning and Navigation2832-73222023-01-01110411410.1109/JISPIN.2023.333203310314734Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process RegressionNiclas Fuhrling0https://orcid.org/0000-0003-1942-8691Hyeon Seok Rou1https://orcid.org/0000-0003-3483-7629Giuseppe Thadeu Freitas de Abreu2https://orcid.org/0000-0002-5018-8174David Gonzalez G.3https://orcid.org/0000-0003-2090-8481Osvaldo Gonsa4https://orcid.org/0000-0001-5452-8159School of Computer Science and Engineering, Constructor University (previously Jacobs University Bremen), Bremen, GermanySchool of Computer Science and Engineering, Constructor University (previously Jacobs University Bremen), Bremen, GermanySchool of Computer Science and Engineering, Constructor University (previously Jacobs University Bremen), Bremen, GermanyWireless Communications Technologies Group, Continental AG, Hannover, GermanyWireless Communications Technologies Group, Continental AG, Hannover, GermanyWe consider the robust localization, via Gaussian process regression (GPR), of multiple transmitters/targets based on received signal strength indicator (RSSI) data collected by fixed sensors distributed in the environment. For such a scenario and approach, we contribute both with a novel noise robust procedure to train the parameters of the GPR model, which is achieved via a mini-batch stochastic gradient descent (SGD) scheme with gradients given in closed form, and with a pair of corresponding robust marginalization procedures for the estimation of target locations. Simulation results validate the contributions by showing that the proposed methods significantly outperform the best related state-of-the-art (SotA) alternative and approach the performance of a genie-aided (GA) scheme.https://ieeexplore.ieee.org/document/10314734/Gaussian process regression (GPR)machine learningmultitarget localizationnoise robustnessreceived signal strength indicator (RSSI) |
| spellingShingle | Niclas Fuhrling Hyeon Seok Rou Giuseppe Thadeu Freitas de Abreu David Gonzalez G. Osvaldo Gonsa Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression IEEE Journal of Indoor and Seamless Positioning and Navigation Gaussian process regression (GPR) machine learning multitarget localization noise robustness received signal strength indicator (RSSI) |
| title | Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression |
| title_full | Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression |
| title_fullStr | Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression |
| title_full_unstemmed | Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression |
| title_short | Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression |
| title_sort | robust received signal strength indicator rssi based multitarget localization via gaussian process regression |
| topic | Gaussian process regression (GPR) machine learning multitarget localization noise robustness received signal strength indicator (RSSI) |
| url | https://ieeexplore.ieee.org/document/10314734/ |
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