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Kapalı alan konum belirlemede RFID ve UWB teknolojilerinin performans karşılaştırılması

The performance comparison of RFID and UWB technologies in indoor positioning

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  1. Tez No: 904272
  2. Yazar: ÖMER FARUK BAĞDATLI
  3. Danışmanlar: PROF. DR. ÖZGÜR ÖZDEMİR
  4. Tez Türü: Yüksek Lisans
  5. Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2024
  8. Dil: Türkçe
  9. Üniversite: İstanbul Teknik Üniversitesi
  10. Enstitü: Lisansüstü Eğitim Enstitüsü
  11. Ana Bilim Dalı: Elektronik ve Haberleşme Mühendisliği Ana Bilim Dalı
  12. Bilim Dalı: Telekomünikasyon Bilim Dalı
  13. Sayfa Sayısı: 65

Özet

Kapalı alan konum belirleme, bir iç mekan ortamında bir cihazın veya kullanıcının konumunun tahmin edilmesidir. Son yirmi yılda internet ile insanların bilgisayarlar ve akıllı telefonlar aracılığıyla birbirleriyle haberleştiği zamandan git gide nesnelerin insanlarla haberleştiği ve nesnelerin kendi aralarında haberleştiği bir zamana dönüşmesi, kapalı alan konum belirlemeye ihtiyacı arttırmaktadır. Açık alanlardan farklı olarak elektromanyetik dalgalar kapalı alanlarda çok daha fazla yansımaya, kırılmaya ve saçılmaya uğrarlar. Bu da kapalı alan konum belirlemenin zorluğunu arttırır. Ayrıca alınan sinyaller direk vericiden gelen sinyalin yanında yansımalardan gelen sinyalleri de içerebilir (çoklu yol etkisi). Kapalı alan konumlandırmasında bu sorunları aşmak için RFID (Radyo Frekansı ile Tanımlama), UWB (Ultra Geniş Bant), Wi-Fi (Kablosuz yerel ağ), Bluetooth gibi çeşitli teknolojiler kullanılmaktadır. Bu çalışmada RFID ve UWB teknolojileri kullanılarak kapalı bir odada aktif bir etiketin konum kestirimi iki okuyucu ile yapılmış olup, teknolojilerin konum doğruluğu performansları karşılaştırılmıştır. Bunu yaparken RFID, konum belirleme yöntemlerinden parmak izi yöntemi kullanılmıştır. Parmak izi yöntemini kullanırken oda içerisinde aktif etiketten belirli noktalarda (çevrim dışı) sinyal gücü seviyeleri alınarak bir veri tabanı oluşturulmuştur. Burada oluşturulan veri ile aktif etiketin gerçek konumu arasındaki karşılaştırma; k en yakın komşu algoritması, maksimum olabilirlik kestirimi ve olasılık dağılımı yaklaşımlarıyla yapılmıştır. Ultra geniş bant teknolojisinde kapalı alan konum belirlemede bilaterasyon konum belirleme yöntemi kullanılmıştır. Okuyucuların bilinen sabit konumları ve iki yönlü mesafe ölçümünün (TWR) verdiği üçüncü nokta ile mobil etiketin konumunu tahmin etmemizi sağlar. Bilaterasyon yönteminde ortaya çıkan iki kesişim noktasından sadece biri oda içerisinde bulunduğundan, konum kestirimi sadece bu nokta için yapılmıştır. Deney sonucunda UWB teknolojisi bilaterasyon yöntemi RFID parmak izi yöntemine göre konum doğruluğu daha yüksek sonuçlar vermiştir. RFID parmak izi yönteminde alının sinyal seviyelerinin aralığının geniş olması konum doğruluğunu olumsuz etkilemiştir. Sinyal seviyesindeki dalgalanmalarının üstesinden gelebilmek için istatistiksel yaklaşımlardan yararlanılarak konum doğruluklarının arttığı deneylenmiştir. Bunun yanında, iki teknolojinin de oda içerisindeki saçıcı cisimlerden etkilendiği ve bunun sonucunda da ölçüm sonuçlarındaki hataların arttığı tespit edilmiştir.

Özet (Çeviri)

With Industry 4.0 and later Industry 5.0, production methods have started to digitally transform like never before. In this period, the digital industry has merged with objects. In this regard, the internet of things (IoT) has attracted a lot of attention with its vision of combining the physical world with the digital world. The internet of things should not be considered simply as an interaction but as an advanced integration of all services, systems and protocols in terms of infrastructure. It is estimated that 50 billion objects will be connected to each other in 2025 thanks to the internet of things. In the last twenty years, from the time when people communicated with each other through the internet and computers and smartphones, there is gradually a time when objects communicate with people and objects communicate with each other. These systems will provide the opportunity to quickly access information about many objects with high efficiency and productivity in business and daily life. Therefore, the importance of indoor positioning technologies for these systems is increasing day by day. Indoor location determination is used in healthcare, supply chain, smart home applications, tracking systems, etc. It is used in many applications. For example, remote monitoring of the heart sensor attached to humans, identification and tracking of the herd with bio-chips attached to the herd, authorized computer controlling the entire city electricity, real-time tracking of visually impaired or elderly people, real-time tracking of forklift vehicles operating in closed environments. preventing possible accidents, providing interactive maps in environments such as libraries or airports, etc. GPS (Global Positioning System), which attracts a lot of attention in open areas, cannot be used in closed environments because the electromagnetic wave cannot enter the building. Unlike determining location in open areas, determining location in closed areas has much more challenging aspects. The main reasons for this are: multipath fading as a result of the reflection of radio waves from the walls and objects inside the closed area, the receiver and transmitter systems not being in the line of sight, the presence of people moving in the closed area, and the obstacles in the closed area causing signal scattering and attenuation. Various radio technologies are used to overcome these challenges in indoor positioning. Current radio frequency communication technologies can be summarized as: Wi-Fi, Bluetooth, Bluetooth Low Energy, Zigbee, IEEE 802.15.4 standard, LTE, near field communication (NFC), ultra wide band (UWB), and radio frequency identification (RFID). Determination of the best technological system for indoor location systems; It is possible by taking into account many factors such as cost, accuracy, durability, scaling and coverage. The places where each technological system stands out in these elements are determined according to the user's needs, the performance characteristics of the system and environmental conditions. Although RFID, radio frequency identification, was first used to identify objects, it is now also used for location determination. In doing so, it utilizes readers, tags, antennas, and middleware. Unlike a wireless local network, it establishes its own infrastructure. Active RFID systems were used in this study. Due to the large volume of active tags, they can store more information with an extra memory module. They can even be used as a multi-sensor with a microprocessor placed on the chip. Thus, this active tag will not only be used for identification purposes but will now be able to collect information from the object it is attached to as a smart tag, process this information and pass it under its own control. Thus, it will become a system that can be used for the internet of things. The wide bandwidths of ultra-wideband signals provide many advantages for positioning, communications and radar applications. The high time resolution of ultra-wideband signals facilitates very precise position estimation which enables performing a variety of applications. It offers high accuracy, especially in indoor real-time positioning systems. There are various positioning techniques used in closed areas. The most common techniques are trilateration, bilateration, triangulation, statistical techniques, radio map matching (fingerprinting), etc. The techniques are selected according to the most suitable conditions for its applications. For instance, there are minimum two nodes for bilateration and triangulation techniques. In this study, we aim to compare the performance of indoor position technologies of RFID and UWB. In RFID, the RSSI (Received Signal Strength Indication) signal parameters that correspond to wireless communications between the tag and the readers are measured and then the physical position of the target (mobile active tag) is calculated based on fingerprinting method with k-NN algorithm, maximum likelihood estimation and statistical probability distribution. UWB is a RF communication technology for short-range with its larger channel band width (500 Mhz) with short pulses. It is based on the combination of very short pulses and used in indoor location technology for accuracy and reliability. In UWB, to find the distance between readers and the tag, TWR (Two way ranging) positioning method is used. Then bilateration technique determines the location of the tag by using two readers. In our experiment, an active mobile tag's position is estimated by using two readers in a closed room. These readers and the tag have two separate microprocessors' antenna working on 2.45 GHz and 5GHz frequency, which enable us the comparison of RFID and UWB technologies. To implement fingerprint technique with RFID, a database of RSSI values (radio map) measured on 12 specific points separated by 1 meter intervals in the room is generated. In order to determine the position of the mobile active tag, we compare the received signals with the RSSI values stored in the database. Then, UWB combined with bilateration technique is applied. Two readers provide us two intersection points in 2 dimensional space which one of these stays inside room. Two known reference points (readers fixed positions) and the third point determined by TWR method enable us to estimate the mobile tag position. As a result of the study, it was observed that UWB technology based on the bilateration method is a more suitable technology for indoor location accuracy, giving more accurate results in indoor location determination than RFID technology based on the fingerprint method. In the RFID technology-based fingerprinting method for indoor positioning, the RSSI values obtained from active tags by readers within a room have been compared using k-nearest neighbors algorithm, maximum likelihood estimation, and probability distribution comparisons. In the fingerprinting method, it has been observed that the range of received signal strength indicator (RSSI) values is quite broad. The lower positioning accuracy achieved using the k-nearest neighbors algorithm based solely on the average RSSI is expected due to this reason. For fluctuating RSSI values, a statistical approach has yielded results that are closer to the actual location compared to a deterministic approach. Additionally, it has been noted that positioning accuracy increases when measurements are taken at points far from large objects in the room. Furthermore, it has been revealed that the RFID fingerprinting method is more affected by scattering objects (such as TV units) inside the room, resulting in greater measurement errors. In both technologies, it has been observed that positioning accuracy decreases when measurements are taken right next to scattering objects. In future research, the impact of increasing the number of offline measurement points and RSSI readings on positioning accuracy in the RFID fingerprinting method could be examined. Additionally, using other positioning methods for fluctuating RSSI values, which were not addressed in this study, is another potential area for future research.

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