Terrain referenced navigation of an aircraft using particle filter

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Havacılık ve Uzay Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2017

Öğrenci: BURAK TURAN

Danışman: ALİ TÜRKER KUTAY

Özet:

The need for Terrain Referenced Navigation (TRN) arises when Global Navigation Satellite System (GNSS) signals are unavailable. In recent years, research on the application of TRN to aerial and underwater vehicles has been increased rapidly with the developments in the accuracy of digital terrain elevation database (DTED). Since the land and sea floor profiles are inherently nonlinear, TRN becomes a nonlinear estimation problem. Because of the highly nonlinear and non-Gaussian problem, linear or linearized estimation techniques such as Kalman or Extended Kalman Filter (EKF) do not work properly for many terrain profiles. Hence, this thesis focuses on the particle filter (PF) for dealing with nonlinearities and different types of probability distributions even multi modal. Two different particle filter (PF) implementations are studied, Sequential Importance Sampling with effective resampling (SIS-R) and Sampling Importance Resampling (SIR). Both algorithms are tested for an aircraft sample scenario over a DTED map. Simulations with different number of particles, inertial measurement units (IMUs) having various error specifications are performed and investigated.