Skip to main content
Log in

Performance Predominance of a New Strategy for CFAR Processors over the N-P Model in Detecting Four Degrees of Freedom χ2 Fluctuating Targets

  • Published:
Radioelectronics and Communications Systems Aims and scope Submit manuscript

Abstract

Modern radars have adopted adaptive processing techniques to mitigate the deleterious effects of unwanted clutter and jammer. In this situation, the CFAR algorithms play a vital role in achieving the heterogeneous detection of fluctuating targets. In this regard, while the CA-CFAR processor has the top homogeneous performance, the OS and TM techniques have been suggested to provide robust estimates of the threshold in heterogeneous situations. In order to simultaneously exploit the merits of CA and OS or TM processors, some their hybrid versions have been recently introduced. They are termed as CAOS and CATM models. Practically, the frequency diversity between noncoherent sweeps is widespread in actual radar systems. Additionally, the pulse integration strategy is often used in radar systems to improve the target signal-to-noise ratio and correspondingly the system detection performance. For this reason, this paper is focusing on analyzing these new models in the case where the radar receiver noncoherently integrates M-pulses to handle its detection. Closed-form expression is derived for their nonhomogeneous performance. The tested as well as the spurious targets are assumed to follow χ2-distribution with four degrees of freedom in their fluctuations. Our simulation results reveal that the new version CATM exhibits a homogeneous performance that outweighs that of the classical Neyman-Pearson (N-P) procedure, which is employed as a baseline comparison for other strategies in the field of adaptive detectors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. M. A. Weiner, “Detection probability for partially correlated chi-square targets,” IEEE Trans. Aerospace Electronic Systems 24, No. 4, 411 (1988). DOI: 10.1109/7.7183.

    Article  Google Scholar 

  2. M. Barkat, S. D. Himonas, P. K. Varshney, “CFAR detection for multiple target situations,” IEE Proc. F - Radar Signal Processing 136, No. 5, 193 (1989). DOI: 10.1049/ip-f-2.1989.0033.

    Article  Google Scholar 

  3. M. B. El Mashade, “Detection performance of the trimmed-mean CFAR processor with noncoherent integration,” IEE Proc. Radar, Sonar Navig. 142, No. 1, 18 (1995). DOI: 10.1049/ip-rsn:19951626.

    Article  Google Scholar 

  4. Daniel T. Nagle, Jafar Saniie, “Performance analysis of linearly combined order statistic CFAR detectors,” IEEE Trans. Aerospace Electronic Systems 31, No. 2, 522 (1995). DOI: 10.1109/7.381903.

    Article  Google Scholar 

  5. D.-S. Han, “Detection performance of CFAR detectors based on order statistics for partially correlated chi-square targets,” IEEE Trans. Aerospace Electronic Systems 36, No. 4, 1423 (2000). DOI: 10.1109/7.892694.

    Article  Google Scholar 

  6. A. Farrouki, M. Barkat, “Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments,” IEE Proc. - Radar Sonar Navig. 152, No. 1, 43 (2005). DOI: 10.1049/ip-rsn:20045006.

    Article  Google Scholar 

  7. M. B. El Mashade, “Analysis of cell-averaging based detectors for - 2 fluctuating targets in multitarget environments,” J. Electron. (China) 23, No. 6, 853 (2006). DOI: 10.1007/s11767-005-0067-0.

    Article  Google Scholar 

  8. T. Laroussi, M. Barkat, “A performance comparison of two time diversity systems using CMLD-CFAR detection for partially-correlated chi-square targets and multiple target situations,” Proc. of 14th European Signal Processing Conf., 4–8 Sept. 2006, Florence, Italy (IEEE, 2006), pp. 4–8, URI: https://ieeexplore.ieee.org/document/7071138.

    Google Scholar 

  9. M. B. El Mashade, “Performance analysis of OS structure of CFAR detectors in fluctuating target environments,” PIER C 2, 127 (2008). DOI: 10.2528/PIERC08022807.

    Article  Google Scholar 

  10. B. Magaz, A. Belouchrani, M. Hamadouche, “A new adaptive linear combined CFAR detector in presence of interfering targets,” PIER B 34, 367 (2011). DOI: 10.2528/PIERB11012603.

    Article  Google Scholar 

  11. Long Cai, Xiaochuan Ma, Qi Xu, Bin Li, Shiwei Ren, “Performance analysis of some new CFAR detectors under clutter,” J. Computers 6, No. 6, 1278 (2011). DOI: 10.4304/jcp.6.6.1278-1285.

    Article  Google Scholar 

  12. W. Q. Wang, Radar Systems: Technology, Principles and Applications (Nova Science Publishers, Inc, 2013).

    Google Scholar 

  13. Dejan Ivkoviã, Milenko Andriã, Bojan Zrniã, “A new model of CFAR detector,” Frequenz 68, No. 3–4, 125 (2014). DOI: 10.1515/freq-2013-0087.

    Google Scholar 

  14. Dejan Ivkoviã, Milenko Andriã, Bojan Zrniã, “False alarm analysis of the CATM-CFAR in presence of clutter edge,” Radioengineering 23, No. 1, 66 (2014). URI: http://www.radioeng.cz/fulltexts/2014/14_01_0066_ 0072.pdf.

    Google Scholar 

  15. M. B. El Mashade, “Partially-correlated2 targets detection analysis of GTM-adaptive processor in the presence of outliers,” Int. J. Image, Graphics Signal Processing 7, No. 12, 70 (2014). DOI: 10.5815/ijigsp.2014.12.10.

    Article  Google Scholar 

  16. Dejan Ivkoviã, Milenko Andriã, Bojan Zrniã, Predrag Okiljeviã, Nadica Koziã, “CATM-CFAR detector in the receiver of the software defined radar,” Sci. Tech. Rev. 54, No. 4, 27 (2014). URI: http://www.vti.mod.gov.rs/ntp/rad2014/4-2014/3/e3.htm.

    Google Scholar 

  17. S. Ahmed, “Novel noncoherent radar pulse integration to combat noise jamming,” IEEE Trans. Aerospace Electronic Systems 51, No. 3, 2350 (2015). DOI: 10.1109/TAES.2015.140315.

    Article  Google Scholar 

  18. Dejan Ivkoviã, Milenko Andriã, Bojan Zrniã, “Detection of very close targets by fusion CFAR detectors,” Sci. Tech. Rev. 66, No. 3, 50 (2016). DOI: 10.5937/STR1603050I.

    Article  Google Scholar 

  19. J. R. Machado-Fernandez, N. Mojena-Hernandez, J. C. Bacallao-Vidal, “Evaluation of CFAR detectors performance,” Iteckne 14, No. 2, 170 (2017). DOI: 10.15332/iteckne.v14i2.1772.

    Article  Google Scholar 

  20. M. M. Islam, M. Hossam-E-Haider, “Detection capability and CFAR loss under fluctuating targets of different Swerling model for various gamma parameters in RADAR,” Int. J. Advanced Computer Science Applications 9, No. 2, 90 (2018). DOI: 10.14569/IJACSA.2018.090214.

    Google Scholar 

  21. M. B. El Mashade, “Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated - 2 -targets,” J. Systems Engineering Electronics 29, No. 1, 1 (2018). DOI: 10.21629/JSEE.2018.01.01.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed B. El Mashade.

Additional information

Original Russian Text © M.B. El Mashade, 2018, published in Izvestiya Vysshikh Uchebnykh Zavedenii, Radioelektronika, 2018, Vol. 61, No. 9, pp. 487–507.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El Mashade, M.B. Performance Predominance of a New Strategy for CFAR Processors over the N-P Model in Detecting Four Degrees of Freedom χ2 Fluctuating Targets. Radioelectron.Commun.Syst. 61, 377–393 (2018). https://doi.org/10.3103/S0735272718090017

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0735272718090017

Navigation