Volume 4, Issue 4 (October 2008)                   IJEEE 2008, 4(4): 140-140 | Back to browse issues page

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M. R. Moniri, M. M. Nayebi, A. Sheikhi. Adaptive Signal Detection in Auto-Regressive Interference with Gaussian Spectrum. IJEEE 2008; 4 (4) :140-140
URL: http://ijeee.iust.ac.ir/article-1-81-en.html
Adaptive Signal Detection in Auto-Regressive Interference with Gaussian Spectrum
M. R. Moniri , M. M. Nayebi , A. Sheikhi
Abstract:   (11134 Views)
A detector for the case of a radar target with known Doppler and unknown complex amplitude in complex Gaussian noise with unknown parameters has been derived. The detector assumes that the noise is an Auto-Regressive (AR) process with Gaussian autocorrelation function which is a suitable model for ground clutter in most scenarios involving airborne radars. The detector estimates the unknown parameters by Maximum Likelihood (ML) estimation for the use in the Generalized Likelihood Ratio Test (GLRT). By computer simulations, it has been shown that for large data records, this detector is Constant False Alarm Rate (CFAR) with respect to AR model driving noise variance. Also, measurements show the detector excellent performance in a practical setting. The detector’s performance in various simulated and actual conditions and the result of comparison with Kelly’s GLR and AR-GLR detectors are also presented.
Keywords: CFAR , GLR Test , Radar Detection
Full-Text [PDF 622 kb]   (3224 Downloads)    
Type of Study: Research Paper |
Received: 2008/12/22 | Revised: 2011/07/09 | Accepted: 2011/07/09
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.