In this paper the problem of detection of coherent radar signals with slow fluctuating amplitude and unknown Doppler shift in non-Gaussian clutter is considered. Coherent radar signal detection with unknown Doppler shift is rarely considered in the literature. It has be More
In this paper the problem of detection of coherent radar signals with slow fluctuating amplitude and unknown Doppler shift in non-Gaussian clutter is considered. Coherent radar signal detection with unknown Doppler shift is rarely considered in the literature. It has been demonstrated that in high resolution radars or in small grazing angles, the pseudo-Gaussian models are more accurate than Gaussian for clutter modeling. Optimum detection of signals with unknown Doppler shift in pseudo-Gaussian clutter contains a complicated multiple integral. Therefore, in this paper, generalized forms of the suboptimum GLR and CGLR detectors are proposed. Also, by estimating the random variable related to the clutter power (τ) in the test cell, GLRTLQ detector for unknown Doppler shift case is introduced and generalized. It is demonstrated that the proposed GLRTLQ detector has a simple structure and does not depend on the clutter distribution. The performances of the proposed detectors are evaluated by computer simulation.
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In many of detection problems the received signals models under two hypotheses, H0 and H1, are the same except that some model parameters have fixed value under H0. These models are so called Nested Models. One of the most important examples is detection of a target wit More
In many of detection problems the received signals models under two hypotheses, H0 and H1, are the same except that some model parameters have fixed value under H0. These models are so called Nested Models. One of the most important examples is detection of a target with unknown amplitude in the clutter. In this problem, one can assume similar models for received signals under H0 and H1 unless the target amplitude is assumed to be zero under H0. If the Bayesian approach used for treating unknown parameters, it can be shown that the likelihood ratio can be calculated as the ratio of the posterior and the prior probability of unknown parameters. Using this method a new detector for detection in Gaussian clutter is presented in this paper. Simulation results show that the proposed detector has much better performance compared with conventional GLRT detectors. It is also shown that a CFAR property is achieved provided that a small modifications in decision rule.
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