Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 62-64.Among various types of radar systems, the pulse-Doppler radar is the most widely
used one, especially in military applications. Pulse Doppler radars have a primary
objective to detect and estimate the range and the radial velocity of the
targets. In order to have a basis for the detection, first reflected echo signals are
matched filtered and then the time-alligned pulse returns are transformed to the
Fourier domain to obtain the range-Doppler matrix. The resulting range-Doppler
matrix is input to target detection algorithms. For this purpose, constant false
alarm rate (CFAR) algorithms are run on the range-Doppler matrix. It is useful
to run different CFAR algorithms inside the clutter region and outside the clutter
region because the statistics are different inside and outside of the clutter. In order
to achieve this discrimination, the position of the clutter has to be detected in
the range-Doppler matrix. Moreover, the clutter may not always appear around
zero Doppler frequency when realistic terrain models and moving platforms are
considered. Two algorithms for clutter detection using range-Doppler matrix elements
are investigated and their performance analysis is presented in this thesis.
The first algorithm has higher error rates but lower computational complexity,whereas, the second one has lower error rates but higher computational complexity.
Both algorithms detect clutter position by filtering the range-Doppler
matrix elements via non-linear filters. In addition to the probabilistic error rate
analysis, simulation results on some realistic cases are presented. It is concluded
that the first algorithm is a good choice for low clutter-to-noise ratio values when
a low-complexity algorithm is required. On the other hand, the second algorithm
has better performance in all clutter-to-noise ratio values but it requires more
computational power.Güngör, AhmetM.S