Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
A General framework for adaptive radar detection based on fast and slow-time preprocessing
Download
index.pdf
Date
2019
Author
Saraç, Uğur Berkay
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
4
views
0
downloads
This thesis is about the design of an adaptive radar detector under heterogeneous clutter environment using a small number of secondary data, which is at the same time robust to Doppler mismatch. To this end, the observations taken from heterogeneous clutter environment are first processed with a specially designed fast-time preprocessing matrix, cleansing the target contamination in the secondary range cells. Using these clean secondary data, the covariance matrix of the clutter is estimated via the parametric spectral estimation method proposed by Burg. Using this clutter space and the target space in which the targets are assumed to be in Generalized Eigenspace operation, a reduced dimension space which includes the target space and escapes the clutter space is found. After the observations which are purified in the fast-time dimension are projected in this newfound space, the detection mechanism is activated using the reduced dimension Kelly detector. In this thesis, all steps described here are explained in detail and the performance of the proposed detector is evaluated and compared with the other detectors in the literature by using the MATLAB simulation results.
Subject Keywords
Radar.
,
Keywords: Adaptive Radar Detectors
,
Kelly’s Detector
,
Burg’s Method
,
Dimension Reduction
,
Generalized Eigenvectors.
URI
http://etd.lib.metu.edu.tr/upload/12623721/index.pdf
https://hdl.handle.net/11511/44159
Collections
Graduate School of Natural and Applied Sciences, Thesis