A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection

Basit Öğe Kaydı
dc.contributor.authorAlpdemir, Mahmut Nedim
dc.contributor.authorSezgin, Mehmet
dc.date.accessioned2024-03-18T07:32:40Z
dc.date.available2024-03-18T07:32:40Z
dc.date.issued2024en_US
dc.departmentFakülteler, Mühendislik ve Tasarım Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.description.abstractGround penetrating radar (GPR) systems are effective sensors for discovering various types of objects buried underground, such as military mines, metal objects, and pieces of underground infrastructures. A GPR system can be manually operated by a human or can be an integral part of a host platform. The host platform may be semi- or fully autonomous and may operate in different environments such as land vehicles or more recently air-borne drones. One challenge for the fully or semi-autonomous host platforms in particular is to find an efficient search procedure that would reduce the operation time and optimize resource utilization. Most of the current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper, we introduce a method that combines a coarse and therefore relatively low cost initial search pattern with a Reinforcement Learning (RL) driven efficient navigation path for eventual target detection, by exploiting the signal processing pipeline of the onboard GPR. We illustrate the applicability of the method using a well-known, high fidelity GPR simulation environment and a novel RL framework. Our results suggest that combination of a coarse navigation scheme and an RL-based training procedure based on GPR scan returns can lead to a more efficient target discovery procedure for host platforms.en_US
dc.identifier.doi10.1007/s00521-024-09466-8en_US
dc.identifier.scopus2-s2.0-85186869606en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/7172
dc.identifier.urihttps://doi.org/10.1007/s00521-024-09466-8
dc.identifier.wosWOS:001176073100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCoverage path planning; Ground penetrating radar; Machine learning; Reinforcement learningen_US
dc.titleA reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detectionen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
A-reinforcement-learning-RLbased-hybrid-method-for-ground-penetrating-radar-GPRdriven-buried-object-detectionNeural-Computing-and-Applications.pdf
Boyut:
4.86 MB
Biçim:
Adobe Portable Document Format
Açıklama:
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.56 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu