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Performance enhancement of low-cost ins/gps navigation system operating in urban environments

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  1. Tez No: 839063
  2. Yazar: YUNUS EMRE ÖZDEMİR
  3. Danışmanlar: DR. IVAN PETRUNİN
  4. Tez Türü: Yüksek Lisans
  5. Konular: Astronomi ve Uzay Bilimleri, Havacılık ve Uzay Mühendisliği, Astronomy and Space Sciences, Aeronautical Engineering
  6. Anahtar Kelimeler: Air Mobility, Navigation, Urban Environment, GNS, GPS, INS, Multipath, Non Line of Sight, Machne Learning
  7. Yıl: 2022
  8. Dil: İngilizce
  9. Üniversite: Cranfıeld Unıversıty
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: Belirtilmemiş.

Özet

As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for urban air mobility applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance to the safety and mission success. Currently, GNSS (Global Navigation Satellite System) is the primary PNT source for any kind of navigation system with its high accuracy and global coverage. However, GNSS is highly vulnerable to lineof-sight (LoS) blockages and multipath reflections, which are quite common, especially in urban areas. Inertial Navigation System (INS)/Global Positioning System (GPS) fusion is the most common multi-sensor navigation approach and is widely used even in low-cost GNSS receivers for UAV applications. Since urban environments have a lot of flat surfaces and obstacles that reflect GPS signals, two major signal type issues are experienced: non-line of sight (NLoS) reception and multipath (MP) interference. In this study, we aim to classify received GPS signals as NLoS, LoS, or multipath to enhance PVT estimation by eliminating reflected signals in urban environments. Because of the difficulty of predicting and modelling multipath and NLoS errors, utilizing machine learning was considered a suitable approach. The main concept of the proposed method is that evaluate received signals with GPS receiver observables. By investigating the GNSS receiver outputs, it has been determined which values are related and how they are related to the signal types. After detecting and excluding faulty GPS signals, the enhanced PVT estimations are provided to INS/GPS fusion block. GPS and INS position estimates are fused with a loosely coupled Extended Kalman Filter (EKF). Therefore, improved GPS data quality provides enhancement on overall INS/GPS accuracy in urban air mobility applications.

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