Kablosuz teknolojilerle iç mekan konumlandırma: Ölçüm yöntemleri, yapay zeka ve uygulamalar

Abstract

Arka Plan—Küresel navigasyon uydu sistemi sinyalleri iç mekanlarda büyük ölçüde zayıflar. Çok yollu yayılım, doğrudan görüş hattının bulunmaması, cihazlar arasındaki farklılıklar ve ortamda bulunan kişi sayısının değişmesi, dış mekan konumlandırma yöntemlerinin kullanımını sınırlar. İç mekan teknolojilerinin çeşitliliği ve doğruluk ölçütlerindeki tutarsızlıklar da uygulamaya yönelik doğrudan karşılaştırmayı güçleştirir.

Amaç—Bu yapılandırılmış anlatı derlemesi; ölçüm büyüklüklerini, kestirim algoritmalarını, protokol yeteneklerini, doğrulama tasarımını, yapay zekayı, yönetişimi ve yaşam döngüsü gereksinimlerini denetlenebilir bir teknoloji seçimi süreciyle ilişkilendirmektedir.

Yöntem—IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, Web of Science ve Scopus veri tabanları; ISO/IEC, IEEE, Bluetooth SIG, 3GPP, ETSI, CEPT ve ulusal düzenleyici kurumların resmî belgeleriyle birlikte taranmıştır. Tarama 22 Temmuz 2026 tarihinde güncellenmiştir. Nihai kanıt haritası; güncel derlemelere, standartlara ve resmi teknik belgelere, ayrıca temsil edici deneysel çalışmalara öncelik verilerek seçilmiş 103 benzersiz kaynaktan oluşmuştur.

Bulgular—Alınan sinyal gücü, kanal durum bilgisi, uçuş süresi, gidiş-dönüş süresi, varış zamanı farkı ve açı ölçümleri; yakınlık belirleme, geometrik konum kestirimi, parmak izi tabanlı konumlandırma ve algılayıcı füzyonunu desteklemektedir. Başarım; donanıma, referans düğümlerin geometrisine, kalibrasyona, doğrudan görüş hattı oranına ve test tasarımına bağlıdır. Wi-Fi ve Bluetooth, ortama bağlı olarak genellikle metre düzeyinde konumlandırma veya güvenilir oda/bölge belirleme sağlarken ultra geniş bant, elverişli kurulumlarda santimetre ile desimetre düzeyinde doğruluğa ulaşabilir. Görünür ışık, akustik, jeomanyetik, yapay manyetik ve cihazsız sistemler tamamlayıcı yetenekler sunar; ancak görüş koşulları, yankılanma, harita bakımı, mahremiyet veya donanım açısından ek kısıtlar getirir. Öğrenme tabanlı yöntemler doğrusal olmayan eşlemeyi ve doğrudan görüş hattı dışı yayılımın etkilerini iyileştirebilir; buna karşılık paketlerin rastgele bölünmesine veya aynı ölçüm oturumuna dayanan doğrulama, genellenebilirliği olduğundan yüksek gösterebilir.

Sonuç—Her koşulda üstün olan tek bir teknoloji yoktur. Seçim; çıktı türü, hata yüzdelikleri, kapsama alanı, gecikme, güncelleme hızı, cihaz modeli, ortam, bakım, siber güvenlik, mahremiyet ve yaşam döngüsü maliyeti dikkate alınarak yapılmalı ve ilgili uygulama koşullarını yansıtan bir saha pilotuyla doğrulanmalıdır. Süreklilik ve güvenilirliğin tekil laboratuvar doğruluğundan daha önemli olduğu durumlarda hibrit sistemler tercih edilebilir.

Keywords

• Yapay zeka
• Parmak izi tabanlı konumlandırma
• İç mekan konumlandırma
• Ultra geniş bant
• Kablosuz konumlandırma

Supporting Institution

Makale çalışması için hiçbir kurumdan destek alınmamıştır.

Ethical Statement

Makalede etik kurul izni alınmasına gerek yoktur.

Indoor positioning with wireless technologies: Measurement methods, artificial intelligence, and applications

Abstract

Context—Global navigation satellite system signals are strongly attenuated indoors, where multipath, non-line-of-sight propagation, device heterogeneity, and changing occupancy limit outdoor positioning methods. The diversity of indoor technologies and inconsistent accuracy metrics also complicate direct, application-oriented comparison.

Objective—This structured narrative review connects measurement observables, estimation algorithms, protocol capabilities, validation design, artificial intelligence, governance, and life-cycle requirements to an auditable technology-selection decision.

Method—IEEE Xplore, the ACM Digital Library, ScienceDirect, SpringerLink, Web of Science, and Scopus were searched together with official ISO/IEC, IEEE, Bluetooth SIG, 3GPP, ETSI, CEPT, and national regulatory documents. The search was updated on 22 July 2026. The final evidence map comprised 103 unique sources prioritized as recent reviews, standards and official technical documents, and representative experiments.

Results—Received signal strength, channel state information, time-of-flight, round-trip time, time-difference-of-arrival, and angle measurements support proximity, geometric estimation, fingerprinting, and sensor fusion. Performance depends on hardware, anchor geometry, calibration, line-of-sight ratio, and test design. Wi-Fi and Bluetooth commonly provide environment-dependent meter-level positioning or robust room/zone awareness, whereas ultra-wideband can reach centimeter-to-decimeter accuracy in favorable deployments. Visible-light, acoustic, geomagnetic, artificial-magnetic, and device-free systems add complementary capabilities but also introduce visibility, reverberation, map-maintenance, privacy, or hardware constraints. Learning-based methods can improve nonlinear mapping and NLOS mitigation, but packet-random or same-session validation may overstate generalizability.

Conclusion—No technology is universally superior. Selection should use output type, error percentiles, coverage, latency, update rate, device model, environment, maintenance, cybersecurity, privacy, and life-cycle cost, followed by a context-matched site pilot. Hybrid systems are preferable when continuity and reliability are more important than isolated laboratory accuracy.

Keywords

• Artificial intelligence
• Fingerprint-based positioning
• Indoor positioning
• Ultra-wideband
• Wireless positioning

Supporting Institution

No institutional support was received for this study.

Ethical Statement

Ethics committee approval was not required for this study.

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