Browsing by Subject "Doppler frequency"
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Item Open Access Clutter detection algorithms for airborne pulse-Doppler radar(IEEE, 2010) Güngör, Ahmet; Gezici, SinanClutter detection is an important stage of target detection. Clutter may not always appear around zero Doppler frequency when realistic terrain models and moving platforms are considered. Two algorithms developed for clutter detection using range-Doppler matrix elements and their performance analysis are presented in this paper. The first algorithm has higher error rates but lower computational complexity whereas the second one has lower error rates but higher computational complexity. The algorithms detect clutter position by filtering range-Doppler matrix elements via non-linear filters. ©2010 IEEE.Item Open Access Particle swarm optimization for SAGE maximization step in channel parameter estimation(IET, 2007-11) Bodur, Harun; Tunç, Celal Alp; Aktaş, Defne; Ertürk, Vakur .B.; Altıntaş, AyhanThis paper presents an application of particle swarm optimization (PSO) in space alternating generalized expectation maximization (SAGE) algorithm. SAGE algorithm is a powerful tool for estimating channel parameters like delay, angles (azimuth and elevation) of arrival and departure, Doppler frequency and polarization. To demonstrate the improvement in processing time by utilizing PSO in SAGE algorithm, the channel parameters are estimated from a synthetic data and the computational expense of SAGE algorithm with PSO is discussed. (4 pages).Item Open Access Successive cancelation approach for doppler frequency estimation in pulse doppler radar systems(IEEE, 2010) Soğancı, Hamza; Gezici, SinanIn this paper, a successive cancelation approach is proposed to estimate Doppler frequencies of targets in pulse Doppler radar systems. This technique utilizes the Doppler domain waveform structure of the received signal coming from a point target after matched filtering and pulse Doppler processing steps. The proposed technique is an iterative algorithm. In each iteration, a target that minimizes a cost function is found, and the signal coming from that target is subtracted from the total received signal. These steps are repeated until there are no more targets. The global minimum value of the cost function in each iteration is found via particle swarm optimization (PSO). Performance of this technique is compared with the optimal maximum likelihood solution for various signal-to-noise ratio (SNR) values based on Monte Carlo simulations.