Parametric estimation of clutter autocorrelation matrix for ground moving target indication

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2013

Öğrenci: EMRE KALENDER

Danışman: YALÇIN TANIK

Özet:

In airborne radar systems with Ground Moving Target Indication (GMTI) mode, it is desired to detect the presence of targets in the interference consisting of noise, ground clutter, and jamming signals. These interference components usually mask the target return signal, such that the detection requires suppression of the interference signals. Space-time adaptive processing is a widely used interference suppression technique which uses temporal and spatial information to eliminate the effects of clutter and jamming and enables the detection of moving targets with small radial velocity. However, adaptive estimation of the interference requires high computation capacity as well as large secondary sample data support. The available secondary range cells may be fewer than required due to non-homogeneity problems and computational capacity of the radar system may not be sufficient for the computations required. In order to reduce the computational load and the required number of secondary data for estimation, parametric methods use a priori information on the structure of the clutter covariance matrix. Space Time Auto-regressive (STAR) filtering, which is a parametric adaptive method, and full parametric model-based approaches for interference suppression are proposed as alternatives to STAP in the literature. In this work, space time auto-regressive filtering and model-based GMTI approaches are investigated. Performance of these approaches are evaluated by both simulated and flight test data and compared with the performance of sample matrix inversion space time adaptive processing.