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2008-04-01
Analysis of CFAR Detection of Fluctuating Targets
By
Progress In Electromagnetics Research C, Vol. 2, 65-94, 2008
Abstract
Our scope in this paper is to provide a complete analysis of CFARdetection of fluctuating targets when the radar receiver incoherently integrates M returned pulses from a chi-squared fluctuating targets with two and four degrees of freedom and operates in a multitarget environment. Since the Swerling models of fluctuating targets represent a large number of such type of radar targets, we restrict our attention here to this interesting class of fluctuation models. There are four categories of such representation; namely SWI, SWII, SWIII, and SWIV. SWI and SWIII represent scan-to- scan fluctuating targets, while SWII and SWIV represent fast pulse-to-pulse fluctuation. Exact expressions are derived for the probability of detection of all of these models. A simple and an effective procedure for calculating the detection performance of both fixed-threshold and adaptive-threshold algorithms is obtained. The backbone of this procedure is the ω-domain representation of the cumulative distribution function of the test statistic of the processor under consideration. In the CFARcase, the estimation of the noise power levels from the leading and the trailing reference windows is based on the OS technique. The performance of this detector is analyzed in the case where the operating environment is ideal and where it includes some of extraneous targets along with the target under test. The primary and the secondary outlying targets are assumed to be fluctuating in accordance with the four Swerling's models cited above. The numerical results show that, for large SNR, the processor detection performance is highest in the case of SWIV model while it attains its minimum level of detection in the case of SWI model. Moreover, SWII model has higher performance than the SWIII representation of fluctuating targets. For low SNR, on the other hand, the reverse of this behavior is occurred. This observation is common either for fixed-threshold or for adaptive-threshold algorithm.
Citation
Mohamed El Mashade, "Analysis of CFAR Detection of Fluctuating Targets," Progress In Electromagnetics Research C, Vol. 2, 65-94, 2008.
doi:10.2528/PIERC08020802
References

1. Swerling, P., "Probability of detection for fluctuating targets," IRE Transaction on Information Theory, Vol. IT-6, 269-308, April 1960.

2. Meyer, D. P. and H. A. Mayer, Radar Target Detection, Academic Press, 1973.

3. Skolnik, I. M., Introduction to Radar Systems, 2nd edition, McGraw-Hill, 1980.

4. Helstrom, C. W. and J. A. Ritcey, "Evaluating radar detection probabilities by steepest descent integration," IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-20, No. 5, 624-633, Sept. 1984.
doi:10.1109/TAES.1984.310530

5. Hou, X. Y., N. Morinaga, and T. Namekawa, "Direct evaluation of radar detection probabilities," IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-23, No. 4, 418-423, July 1987.
doi:10.1109/TAES.1987.310875

6. Ritcey, J. A., "Detection analysis of the MX-MLD with noncoherent integration," IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-26, No. 3, 569-576, May 1990.
doi:10.1109/7.106136

7. El Mashade, M. B., "M-sweeps detection analysis of cell-averaging CFARpro cessors in multiple target situations," IEE Radar, Sonar Navig., Vol. 141, No. 2, 103-108, April 1994.
doi:10.1049/ip-rsn:19949887

8. Swerling, P., "Radar probability of detection for some additional fluctuating target cases," IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-33, 698-709, April 1997.

9. El Mashade, M. B., "Performance analysis of the excision CFAR detection techniques with contaminated reference channels," Signal Processing “ELSEVIER”, Vol. 60, 213-234, Aug. 1997.
doi:10.1016/S0165-1684(97)00073-X

10. El Mashade, M. B., "Partially correlated sweeps detection analysis of mean-level detector with and without censoring in nonideal background conditions," AEU, Vol. 53, No. 1, 33-44, Feb. 1999.

11. 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 “ELSEVIER”, Vol. 80, 787-801, Aug. 2000.
doi:10.1016/S0165-1684(99)00166-8

12. El Mashade, M. B., "Target multiplicity performance analysis of radar CFARdetection techniques for partially correlated chisquare targets," AEU, Vol. 56, No. 2, 84-98, April 2002.

13. El Mashade, M. B., "M-sweeps exact performance analysis of OS modified versions in nonhomogeneous environments," IEICE Trans. Commun., Vol. E88-B, No. 7, 2918-2927, July 2005.
doi:10.1093/ietcom/e88-b.7.2918

14. El Mashade, M. B., "Target multiplicity exact performance analysis of ordered-statistic based algorithms for partially correlated chi-square targets," Accepted for publication in IEE Proc. - Radar, Sonar Navig.

15. El Mashade, M. B., "CFARdetection of partially correlated chisquare targets in target multiplicity environments," Accepted for publication in Int. J. Electron. Commun. AEU.

16. El Mashade, M. B., "Performance comparison of a linearly combined ordered-statistic detectors under postdetection integration and nonhomogeneous situations," Accepted for publication in Chinese Journal of Electronics.