OAL-HMT: Optimized AAV Localization Using Hybrid Metaheuristic Techniques
Achieving an exact localization is a complex and essential issue for autonomous aerial vehicles (AAVs) due to their three-directional high-speed mobility. Identifying the accurate flying position of AAVs for resource management and task reallocation is still challenging. In these scenarios, the posi...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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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/10989237/ |
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| author | Awadhesh Dixit Naga Nandini Devi Meka Firoj Gazi Md Muzakkir Hussain |
| author_facet | Awadhesh Dixit Naga Nandini Devi Meka Firoj Gazi Md Muzakkir Hussain |
| author_sort | Awadhesh Dixit |
| collection | DOAJ |
| description | Achieving an exact localization is a complex and essential issue for autonomous aerial vehicles (AAVs) due to their three-directional high-speed mobility. Identifying the accurate flying position of AAVs for resource management and task reallocation is still challenging. In these scenarios, the position of the AAVs must be identifiable in a timely and precise manner. A bioinspired metaheuristic hybrid model was proposed to overcome the shortcomings of inaccurate altitude and improve the AAVs' flying positional coordinates. The proposed model incorporates the particle swarm optimization (PSO) with a fuzzy logic technique. PSO is used to find the optimal or near-optimal positions for the AAVs by minimizing localization error across a wide search space. Once the PSO has determined a feasible solution, fuzzy logic is applied for fine tuning the position based on real-time environmental factors (e.g., signal strength, sensor data, or global positioning system errors). This combination achieved both global efficiency (via PSO) and local precision (via fuzzy logic), ensuring robust localization even in noisy or dynamic conditions during AAVs flight operations. The model, compared to the state-of-the-art model, shows more accuracy in AAV localization with real-time operational data. |
| format | Article |
| id | doaj-art-3ea2ac7d7da844b6840fb33d532522e1 |
| institution | DOAJ |
| issn | 2832-7322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Indoor and Seamless Positioning and Navigation |
| spelling | doaj-art-3ea2ac7d7da844b6840fb33d532522e12025-08-20T03:19:34ZengIEEEIEEE Journal of Indoor and Seamless Positioning and Navigation2832-73222025-01-01314215110.1109/JISPIN.2025.356737510989237OAL-HMT: Optimized AAV Localization Using Hybrid Metaheuristic TechniquesAwadhesh Dixit0https://orcid.org/0000-0002-5978-4087Naga Nandini Devi Meka1https://orcid.org/0009-0007-3229-4751Firoj Gazi2Md Muzakkir Hussain3https://orcid.org/0000-0002-6371-2545SRM University AP, Guntur, IndiaSRM University AP, Guntur, IndiaSRM University AP, Guntur, IndiaSRM University AP, Guntur, IndiaAchieving an exact localization is a complex and essential issue for autonomous aerial vehicles (AAVs) due to their three-directional high-speed mobility. Identifying the accurate flying position of AAVs for resource management and task reallocation is still challenging. In these scenarios, the position of the AAVs must be identifiable in a timely and precise manner. A bioinspired metaheuristic hybrid model was proposed to overcome the shortcomings of inaccurate altitude and improve the AAVs' flying positional coordinates. The proposed model incorporates the particle swarm optimization (PSO) with a fuzzy logic technique. PSO is used to find the optimal or near-optimal positions for the AAVs by minimizing localization error across a wide search space. Once the PSO has determined a feasible solution, fuzzy logic is applied for fine tuning the position based on real-time environmental factors (e.g., signal strength, sensor data, or global positioning system errors). This combination achieved both global efficiency (via PSO) and local precision (via fuzzy logic), ensuring robust localization even in noisy or dynamic conditions during AAVs flight operations. The model, compared to the state-of-the-art model, shows more accuracy in AAV localization with real-time operational data.https://ieeexplore.ieee.org/document/10989237/Energy efficiencyflying ad hoc networks (FANET)fuzzy logiclocalizationparticle swarm optimization (PSO)performance |
| spellingShingle | Awadhesh Dixit Naga Nandini Devi Meka Firoj Gazi Md Muzakkir Hussain OAL-HMT: Optimized AAV Localization Using Hybrid Metaheuristic Techniques IEEE Journal of Indoor and Seamless Positioning and Navigation Energy efficiency flying ad hoc networks (FANET) fuzzy logic localization particle swarm optimization (PSO) performance |
| title | OAL-HMT: Optimized AAV Localization Using Hybrid Metaheuristic Techniques |
| title_full | OAL-HMT: Optimized AAV Localization Using Hybrid Metaheuristic Techniques |
| title_fullStr | OAL-HMT: Optimized AAV Localization Using Hybrid Metaheuristic Techniques |
| title_full_unstemmed | OAL-HMT: Optimized AAV Localization Using Hybrid Metaheuristic Techniques |
| title_short | OAL-HMT: Optimized AAV Localization Using Hybrid Metaheuristic Techniques |
| title_sort | oal hmt optimized aav localization using hybrid metaheuristic techniques |
| topic | Energy efficiency flying ad hoc networks (FANET) fuzzy logic localization particle swarm optimization (PSO) performance |
| url | https://ieeexplore.ieee.org/document/10989237/ |
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