volume | PIER Journals

2018-11-27

Rapidly Adaptive CFAR Detection in Antenna Arrays

Anatolii A. Kononov

Dr. Anatolii A. Kononov

Research Center

STX Engine

Korea

Homepage

Progress In Electromagnetics Research M, Vol. 76, 75-89, 2018

doi:10.2528/PIERM18092401

Abstract

This paper addresses the problem of target detection in adaptive arrays in situations where only a small number of training samples is available. Within the framework of two-stage adaptive detection paradigm, the paper proposes a class of rapidly adaptive CFAR (Constant False Alarm Rate) detection algorithms, which are referred to as joint loaded persymmetric-Toeplitz adaptive matched filter (JLPT-AMF) detectors. A JLPT-AMF detector combines, using a joint detection rule, individual scalar CFAR decisions from two rapidly adaptive two-stage (TS) detectors: a TS TAMF detector and a TS LPAMF detector. The former is based on a TMI filter, which is an adaptive array filter employing a Toeplitz covariance matrix (CM) estimate inversion. The latter is based on an adaptive LPMI filter that uses diagonally loaded persymmetric CM estimate inversion. The proposed class of adaptive detectors may incorporate any rapidly adaptive TS TAMF and TS LPAMF detectors, which, in turn, may employ any scalar CFAR detection algorithms that satisfy an earlier derived linearity condition. The two-stage adaptive processing structure of the JLPT-AMF detectors ensures the CFAR property independently of the antenna array dimension M, the interference CM, and the number of training samples N_CME to be used for estimating this CM. Moreover, the rapidly adaptive JLPT-AMF detectors exhibit highly reliable detection performances, which are robust to the angular separation between the sources, even when N_CME is about m/2 ~ m, m is the number of interference sources. The robustness is analytically proven and verified with statistical simulations. For several representative scenarios when the interference CM has m dominant eigenvalues, comparative performance analysis for the proposed rapidly adaptive detectors is provided using Monte-Carlo simulations.

Citation

Anatolii A. Kononov , "Rapidly Adaptive CFAR Detection in Antenna Arrays," Progress In Electromagnetics Research M, Vol. 76, 75-89, 2018.
doi:10.2528/PIERM18092401
http://www.jpier.org/PIERM/pier.php?paper=18092401

References

1. Reed, I. S., J. D. Mallett, and L. E. Brennan, "Rapid convergence rate in adaptive arrays," IEEE Transactions on Aerospace and Electronic Systems, Vol. 10, No. 6, 853-863, November 1974.
doi:10.1109/TAES.1974.307893

2. Maio, A. D. and M. S. Greco, Modern Radar Detection Theory, Chapter 6, 239–257, Y. I. Abramovich and B. A. Johnson, SciTech Publishing, Edison, NJ, 2016.

3. Steiner, M. and K. Gerlach, "Fast converging adaptive processor for a structured covariance matrix," IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, No. 4, 1115-1126, October 2000.

4. Peckham, C. D., et al., "Reduced-rank STAP performance analysis," IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, No. 2, 664-676, April 2000.
doi:10.1109/7.845257

5. Cheremisin, O., "Efficiency of adaptive algorithms with regularized sample covariance matrix," Radio Eng. Electron. Phys., Vol. 27, No. 10, 69-77, 1982.

6. Gierull, C. H., "Statistical analysis of the eigenvector projection method for adaptive spatial filtering of interference," IEE Proc. - Radar, Sonar Navig., Vol. 144, No. 2, 57-63, April 1997.
doi:10.1049/ip-rsn:19971075

7. Ginolhac, G., P. Forster, F. Pascal, and J. P. Ovarlez, "Exploiting persymmetry for low-rank space time adaptive processing," Signal Processing, Vol. 97, No. 4, 242-251, Elsevier, 2014.

8. Goldstein, M. J., "Reduction of the eigenproblem for Hermitian persymmetric matrices," Math. Computation, Vol. 28, No. 125, 237-238, January 1974.
doi:10.1090/S0025-5718-1974-0329226-5

9. Wilkes, D. M., S. D. Morgera, F. Noor, M. H. Hayes, and III, "A Hermitian Toeplitz matrix is unitarily similar to a real Toeplitz-plus-Hankel matrix," IEEE Trans. SP, Vol. 39, No. 9, 2146-2148, September 1991.
doi:10.1109/78.134459

10. Kononov, A. A., C. H. Choi, and D. H. Kim, "Superfast convergence rate in adaptive arrays," Proc. Int. Conf. Radar, Brisbane, Australia, August 27–30, 2018.

11. Smith, S. T., "Statistical resolution limits and the complexified Cramér-Rao bound," IEEE Trans. SP, Vol. 53, No. 5, 1602, May 2005.

12. Barabell, A. J., "Improving the resolution performance of eigenstructure-based direction-finding algorithms," Proc. ICASSP, 336-339, Boston, MA, 1983.

13. Roy, R. and T. Kailath, "ESPRIT-estimation of signal parameters via rotational invariance techniques," IEEE Trans. on ASSP, Vol. 37, No. 7, 984-995, July 1989.
doi:10.1109/29.32276

14. Abramovich, Y. I., B. A. Johnson, and N. K. Spencer, "Sample-deficient adaptive detection: Adaptive scalar thresholding versus CFAR detector performance," IEEE Transactions on Aerospace and Electronic Systems, Vol. 46, No. 1, 32-46, January 2010.
doi:10.1109/TAES.2010.5417146

15. Kelly, E., "An adaptive detection algorithm," IEEE Transactions on Aerospace and Electronic Systems, Vol. 22, No. 1, 115-127, March 1986.
doi:10.1109/TAES.1986.310745

16. Kalson, S., "Adaptive array CFAR detection," IEEE Transactions on Aerospace and Electronic Systems, Vol. 31, No. 2, 534-542, April 1995.
doi:10.1109/7.381904

17. Pailloux, G., P. Forster, J. P. Ovarlez, and F. Pascal, "Persymmetric adaptive radar detectors," IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, No. 4, 2376-2390, October 2011.
doi:10.1109/TAES.2011.6034639

18. Kononov, A. A., J.-H. Kim, J.-K. Kim, and G. Kim, "A new class of adaptive CFAR methods for nonhomogeneous environments," Progress In Electromagnetics Research B, Vol. 64, 145-170, 2015.
doi:10.2528/PIERB15091603

19. Kononov, A. A. and J. Kim, "Efficient elimination of multiple-time-around detections in pulse-Doppler radar systems," Progress In Electromagnetics Research B, Vol. 71, 55-76, 2016.
doi:10.2528/PIERB16083003

20. Richards, M. A., J. A. Scheer, and W. A. Holm, Principles of Modern Radar, Vol. I, Basic Principles, SciTech Publishing, Raleigh, NC, 2010.
doi:10.1049/SBRA021E

21. Srinivasan, R. and M. Rangaswamy, "Importance sampling for characterizing STAP detectors," IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, No. 1, 273-285, January 2007.
doi:10.1109/TAES.2007.357133