Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (1): 1-17.doi: 10.21629/JSEE.2018.01.01
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MohamedBakry EL_MASHADE*(
)
Received:2016-09-17
Online:2018-02-26
Published:2018-02-23
Contact:
MohamedBakry EL_MASHADE
E-mail:MohamedElMashade@gmail.com
About author:EL_MASHADE Mohamed Bakry received his B.S. degree in electrical engineering from Al Azhar University, Cairo, in 1978, M.S. degree in the theory of communications from Cairo University, in 1982, Le D.E.A. d'Electronique (Spécialité: Traitment du Signal) from USTL, L'Academie de Montpellier, Montpellier, France, in 1985, and Le Diplôme de Doctorat (Spécialité: Composants, Signaux et Systems) in optical communications, from USTL de Montpellier, France, in 1987. His current research activities include radar signal processing, estimation and detection, optical communications, fiber Bragg grating, digital signal processing, and optoelectronic devices. He serves on the Editorial Board of International Journal of Communications, Networks, and System Sciences (IJCNS). He has also served as a reviewer for many international journals. He was the author of more than 45 peer-reviewed journal articles and the coauthor of more than 50 journal technical papers as well as one international book chapter. He received the best research paper award from International Journal of Semiconductor Science & Technology in 2014 for his work on "Noise Modeling Circuit of Quantum Structure Type of Infrared Photodetectors". He has organized a special issue on "Recent Trends of Wireless Communication Networks" for IJCNS. Dr. EL_MASHADE won the Egyptian Encouraging Award, in Engineering Science, two times (in 1998 & 2004). In 2004, he was included in the American society "Marquis Who's Who" as a "Distinguishable Scientist". Additionally, his name was included in the International Biographical Centre, Cambridge, England as an "Outstanding Scientist" in 2005. E-mail: MohamedBakry EL_MASHADE. Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated χ2-targets[J]. Journal of Systems Engineering and Electronics, 2018, 29(1): 1-17.
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Fig 1
Architecture of CA GTM adaptive threshold scheme"
Fig 2
Thresholding constant versus false alarm rate of CA-, OS-, and CAOS-CFAR schemes processing$ \bm M $-sweeps for a reference window of 24 cells"
Fig 3
Constant scaling multiplier versus rate of false alarm of CA-, TM-, and CATM-CFAR schemes processing $ \bm M $-sweeps for an estimation channel of 24 samples"
Fig 4
Partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target detection performance of CA-GTM(9, 2, 0) scheme for $ \mathit{\boldsymbol{N = 24, M = 2, R_1 = R_2 = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 5
Partially-correlated $ \mathit{\boldsymbol{\chi ^{2}}} $ target detection performance of CA- GTM(9, 2, 0) scheme for $ \mathit{\boldsymbol{N = 24, M = 4, R_{1} = R_{2} = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 6
Partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target detection performance of CA-GTM(2, 2, 0) scheme for $ \mathit{\boldsymbol{N = 24, M = 2, R_1 = R_2 = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 7
Partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target detection performance of CA-GTM(2, 2, 0) scheme for $ \mathit{\boldsymbol{N = 24, M = 4, R_1 = R_2 = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 8
Partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target detection performance of ML-GTM(2, 2, 0) scheme for $ \mathit{\boldsymbol{N = 24, M = 2, R_1 = R_2 = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 9
Partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target detection performance of ML-GTM(2, 2, 0) scheme for $ \mathit{\boldsymbol{N = 24, M = 4, R_1 = R_2 = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 10
Partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target detection performance of ML-GTM(9, 2, 0) scheme for $ \mathit{\boldsymbol{N = 24, M = 2, R_1 = R_2 = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 11
Partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target detection performance of ML-GTM(9, 2, 0) scheme for $ \mathit{\boldsymbol{N = 24, M = 4, R_1 = R_2 = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 12
Partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target detection performance of ML-GTM(0, 0, 0) scheme for $ \mathit{\boldsymbol{N = 24, M = 2, R_1 = R_2 = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 13
Partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target detection performance of ML-GTM(0, 0, 0) scheme for $ \mathit{\boldsymbol{N = 24, M = 4, R_1 = R_2 = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 14
Ideal multi-pulse signal strength requested to achieve a detection level of 90% of CFAR schemes for partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target when $ \mathit{\boldsymbol{N = 24, M = 2, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 15
Ideal multi-pulse signal strength requested to achieve a given detection level of CFAR schemes for partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target when $ \mathit{\boldsymbol{N = 24, M = 4, \rho = 0, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 16
Ideal multi-pulse signal strength requested to achieve a given detection level of CFAR schemes for partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target when $ \mathit{\boldsymbol{N = 24, M = 4, \rho = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 17
Multi-target multi-pulse signal strength requested to achieve a given detection level of CFAR schemes for partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target when $ \mathit{\boldsymbol{N = 24, M = 3, R_{1} = R_{2} = 1 , \rho = 0, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 18
Multi-target multi-pulse signal strength requested to achieve a given detection level of CFAR schemes for partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target when $ \mathit{\boldsymbol{N = 24, M = 3, R_1 = R_2 = 1 , \rho = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 19
Partially-correlated $ \mathit{\boldsymbol{\chi ^2}} $ target false alarm rate performance of CFAR detectors for $ \mathit{\boldsymbol{N = 24, M = 2, R_1 = R_2 = 1, \kappa = 2}} $, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
Fig 20
Multi-pulse actual probability of false alarm of CFAR processors for $ \mathit{\boldsymbol{\chi ^2}} $ target fluctuation model when $ \mathit{\boldsymbol{N = 24, M = 3, \kappa = 2, R_1 = R_2 = 1, INR = -5}} $ and 25 dB, and $ \mathit{\boldsymbol{P_{fa} = 10^{-6}}} $"
| 1 | EL_MASHADE M B. M-sweeps detection analysis of cell-averaging CFAR processors in multiple target situations. IEE Radar, Sonar Navigation, 1994, 141 (2): 103- 108. |
| 2 |
EL_MASHADE M B. Analysis of the censored mean-level CFAR processor in multiple target and nonuniform clutter. IEE Radar, Sonar Navigation, 1995, 142 (5): 259- 266.
doi: 10.1049/ip-rsn:19951985 |
| 3 | EL_MASHADE M B. Multipulse analysis of the generalized trimmed mean CFAR detector in nonhomogeneous background environments. International Journal of Electronics and Communications AEÜ, 1998, 52 (4): 249- 260. |
| 4 | EL_MASHADE M B. Detection analysis of CA family of adaptive radar schemes processing M-correlated sweeps in homogeneous and multiple target environments. Signal Processing, 2000, 80 (5): 787- 801. |
| 5 | HAN D S. Detection performance of CFAR detectors based on order statistics for partially correlated chi-square targets. IEEE Trans. on Aerospace and Electronic Systems, 2000, 36 (4): 1423- 1429. |
| 6 | EL_MASHADE M B. Exact analysis of OS modified versions with noncoherent integration. Journal of Electronics, 2004, 21 (4): 265- 277. |
| 7 | RICKARD M A. Fundamental of radar signal processing. McGraw-Hill Education, 2005. |
| 8 | EL_MASHADE M B. Analysis of cell-averaging based detectors for χ2 fluctuating targets in multitarget environments. Journal of Electronics, 2006, 23 (6): 853- 863. |
| 9 | EL_MASHADE M B. Performance analysis of OS structure of CFAR detectors in fluctuating target environments. Progress in Electromagnetics Research C, 2008, 2, 127- 158. |
| 10 | EL_MASHADE M B. Performance analysis of CFAR detection scheme processing M-sweeps in the presence of outliers. Advances in Microwave and Wireless Technologies, 2013, 1 (2): 16- 33. |
| 11 | EL_MASHADE M B. Performance of the developed versions of CFAR schemes processing non-coherently integrated Mpulses in the presence of outliers. Measuring Behaviour, 2014, 3 (1): 7- 21. |
| 12 | EL_MASHADE M B. Analysis of adaptive detection of partially-correlated χ2 targets in multitarget environments. Parallel and Cloud Computing Research, 2013, 1 (2): 17- 39. |
| 13 | IVKOVIC D, ANDRIC M, ZRNIC B. False alarm analysis of the CATM-CFAR in presence of clutter edge. Radioengineering, 2014, 23 (1): 66- 72. |
| 14 | EL_MASHADE M B. Partially-correlated χ2 targets detection analysis of GTM-adaptive processor in the presence of outliers. International Journal of Image Graphics & Signal Processing, 2014, 6 (12): 70- 90. |
| 15 | IVKOVI'C D, ANDRI'C M, ZRNI'C B. A new model of CFAR detector. Frequenz, 2014, 68 (3/4): 125- 136. |
| 16 | EL_MASHADE M B. Heterogeneous performance evaluation of sophisticated versions of CFAR detection schemes. Radioelectronics and Communications Systems, 2016, 59 (12): 536- 551. |
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