Smoother Integral Sliding Mode Filter for RSSI-Based Localization

This study advances our previous research on radio frequency (RF) source localization using unmanned aerial vehicles (UAVs) through a novel geometrical Received Signal Strength Indicator (RSSI) approach. Our methodology utilizes four UAVs, split into two groups, which adjust their positions until th...

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Main Author: Nurbanu Guzey
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10693456/
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author Nurbanu Guzey
author_facet Nurbanu Guzey
author_sort Nurbanu Guzey
collection DOAJ
description This study advances our previous research on radio frequency (RF) source localization using unmanned aerial vehicles (UAVs) through a novel geometrical Received Signal Strength Indicator (RSSI) approach. Our methodology utilizes four UAVs, split into two groups, which adjust their positions until the power received by each UAV matches that of its counterpart, allowing the calculation of intersecting lines that pinpoint the source location. Recognizing the challenges posed by environmental noise which can alter signal values leading to inaccurate estimations, we introduce a smoother integral sliding mode filter. This enhanced filter incorporates the integral of the error into the sliding mode control framework to address the high-frequency chattering typically associated with sliding mode filters in noisy environments. This adaptation not only reduces the chattering effect but also stabilizes the UAV’s response to fluctuating signal strengths, resulting in more accurate and reliable localization. Simulation results demonstrate a marked improvement in localization accuracy and robustness against noise.
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spelling doaj-art-2cfb5e8799f04d6d9f4a009bfda441ff2025-08-20T02:01:57ZengIEEEIEEE Access2169-35362024-01-011217241017241810.1109/ACCESS.2024.346799410693456Smoother Integral Sliding Mode Filter for RSSI-Based LocalizationNurbanu Guzey0https://orcid.org/0000-0002-6587-2489Department of Computer Engineering, Sivas University of Science and Technology, Sivas, TürkiyeThis study advances our previous research on radio frequency (RF) source localization using unmanned aerial vehicles (UAVs) through a novel geometrical Received Signal Strength Indicator (RSSI) approach. Our methodology utilizes four UAVs, split into two groups, which adjust their positions until the power received by each UAV matches that of its counterpart, allowing the calculation of intersecting lines that pinpoint the source location. Recognizing the challenges posed by environmental noise which can alter signal values leading to inaccurate estimations, we introduce a smoother integral sliding mode filter. This enhanced filter incorporates the integral of the error into the sliding mode control framework to address the high-frequency chattering typically associated with sliding mode filters in noisy environments. This adaptation not only reduces the chattering effect but also stabilizes the UAV’s response to fluctuating signal strengths, resulting in more accurate and reliable localization. Simulation results demonstrate a marked improvement in localization accuracy and robustness against noise.https://ieeexplore.ieee.org/document/10693456/Localizationreceived signal strength indicatorsliding mode filterunmanned aerial vehicles
spellingShingle Nurbanu Guzey
Smoother Integral Sliding Mode Filter for RSSI-Based Localization
IEEE Access
Localization
received signal strength indicator
sliding mode filter
unmanned aerial vehicles
title Smoother Integral Sliding Mode Filter for RSSI-Based Localization
title_full Smoother Integral Sliding Mode Filter for RSSI-Based Localization
title_fullStr Smoother Integral Sliding Mode Filter for RSSI-Based Localization
title_full_unstemmed Smoother Integral Sliding Mode Filter for RSSI-Based Localization
title_short Smoother Integral Sliding Mode Filter for RSSI-Based Localization
title_sort smoother integral sliding mode filter for rssi based localization
topic Localization
received signal strength indicator
sliding mode filter
unmanned aerial vehicles
url https://ieeexplore.ieee.org/document/10693456/
work_keys_str_mv AT nurbanuguzey smootherintegralslidingmodefilterforrssibasedlocalization