Design & Efficient Implementation of STAP with the Multi-Vector Method for the Target Detection in Airborne Radars

Document Type : Original Article

Authors

1 دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، گروه مهندسی برق الکترونیک، تهران، ایران

2 دانشگاه گیلان

3 دانشگاه آزاد اسلامی، واحد یادگار امام خمینی )ره( شهر ری، دانشکده برق، تهران، ایران

Abstract

Space-Time Adaptive Processing (STAP) is a fundamental technique in improving the performance of radars which are acted in the presence of severe dynamic disturbances such as clatter and jamming. STAP operation is based on very high sampling rates from signals received simultaneously from several antenna arrays and a number of pulses. Therefore, the volume of computation is very high and its implementation is difficult. In this paper, multi-vector method is proposed to reduce the amount of STAP calculations. Implementation results show that the proposed method, in addition to reducing the computational volume, leads to reduced hardware resources, reduced power consumption and increased maximum operating frequency. For example, the calculation volume for 6 antenna arrays, 10 receiving pulses and 200 range samples with a vector size of 17 is obtained 28.1 GFLOPS with maximum frequency of 278 MHz, and the computation time of 262 μs on the Arria 10 chip. Therefore, the multi-vectoring method can meet the real-time requirements of STAP weights. Angle-doppler adaptive pattern and the static test for the target detection indicate that using of the above method is very practical for calculating weights.

Keywords

References

[1] W. L. Melvin, “A stap Overview: IEEE Aerospace and Electronic Systems Magazine,” vol. 19, no. 1, pp. 19-35, 2004.
[2] R. Klemm, “Principles of Space-Time Adaptive Processing,” IEE Radar, Sonar, Navigation and Avionics, London: IEE Press, 2002.
[3] J. Ward, “Space-Time Adaptive Processing for Airborne Radar,” (Technical Report no. 1015) Lexington, MA: Lincoln Laboratory, MIT, 1994.
[4] W. L. Melvin, “Space-Time Adaptive Radar Performance in Heterogeneous Clutter,” IEEE Transactions on Aerospace and Electronic Systems, vol. 36, no. 2, pp. 621-633, 2000.
[5] Z. Hao and M. Luo, “Adaptive Mainlobe Jamming Suppression Method for STAP Airborne Radar,” IET International Radar conf., 2013.
[6] L. E. Brennan and L. S. Reed, “Theory of Adaptive Radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 9, no. 2, pp. 237–252, 1973.
[7] A. S. Paulus, W. L. Melvin, and B. Himed, “Performance and Computational Trades for RD-STAP Algorithms in Challenging Detection Environments,” IEEE Radar conf., 2016.
[8] N. A. Gawande, J. B. Manzano, A. Tumeo, N. R. Tallent, D. J. Kerbyson, and A. Hoisie, “Power and Performance trade-offs for Space Time Adaptive Processing,” IEEE 26th International conf. on Application-Specific Systems, Architectures and Processors, 2015.
[9] L. B. Fertig, “Analytical Expressions for Space-time Adaptive Processing (STAP) Performance,” IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 1, pp. 42–53, 2015.
[10] J. M. Lebak and A.W. Bojanczyk, “Design and Performance Evaluation of A Portable Parallel Library for Space-Time Adaptive Processing,” IEEE Transactions on Parallel and Distributed Systems, vol. 11, no. 3, pp. 287–298, 2000.
[11] W. K. Liao, A. Choudhary, D. Weiner, and P. Varshney, “Performance evaluation of a parallel pipeline computational model for space-time adaptive processing,” The Journal of Supercomputing, vol. 31, no. 2, pp. 137-160, 2005.
[12] K. Rajan and L. M. Patnik, “Implementation of STAP Algorithms on IBM SP2 and on ADSP 21062 Dual Digital Signal Processor Systems,” Microprocessors and Microsystems, vol. 27, no. 4, pp. 221-227, 2003.
[13] F. Xikun and W. Yongliang, “Real-time implementation of Airborne Radar Space-Time Adaptive Processing on Multi-
DSP System,” International conf. on Radar (CIE '06), 2006.
[14] S. Bin, L. Shaohong, R. Yi, and L. Jingsheng, “Realization and Comparison of QRD Algorithms for STAP,” 2nd Asian-pacific conf. on Synthetic Aperture Radar (APSAR), Proc. of IEEE, pp. 306-309, 2009.
[15] Q. Li, J. S. Cao, and X. Pei, “Real-time and extensible calculation of STAP weights on FPGA,” 12th International conf. on Signal Processing,” pp. 425-428, 2014.
[16] A. Jarrah and M. Jamali, “Software tool for Efficient FPGA Design of Direct Data Domain Approach for Space-Time Adaptive Processing” Electronics Letters, vol. 49, no. 13, pp. 789–791, 2013.
[17] Z. U. Mahmood, M. Alam, K. Jamil, and Z. O. Al-Hekail, “FPGA Implementation of Space-Time Adaptive Processing (stap) Algorithm for Target Detection in Passive Radars,” Progress in Electromagnetics Research C, vol. 35, pp. 35–48, 2013.
[18] T. M. Benson, R. K. Hersey, and E. Culpepper, “GPU-based Space-Time Adaptive Processing (STAP) for Radar,” IEEE High Performance Extreme Computing conf. (HPEC), 2013.
[19] M. A. Richards, “Fundamentals of Radar Signal Processing,” Tata McGraw-Hill Education, 2005.
[20] R. Woods, J. McAllister, G. Lightbody, and Y. Yi, “FPGA-based Implementation of Signal Processing Systems,” John Wiley & Sons Ltd. U.K, 2008.
[21] G. H. Golub and C. F. Van Loan, “Matrix Computations,” Johns Hopkins University Press, Baltimore, Third edition, 1996.
[22] M. Jervis, “Advances in DSP design tool flows for FPGAs,” Military Communications conf., pp. 2041-2046, 2010.
[23] E. Rezagholizadeh, M. A. Sebt, “Clutter Mitigation in Airborne Radar Using DeterministicSubspace Clutter and Direct Data Domain”, Journal of Radar, vol. 4, no. 2, pp. 1–10, 2016 (In Persian).
[24] S. Sadeghi, A. Sheikhi, “Jamming Noise Suppression Using Discrete Polynomial-Phase Transform in Pulsed Radars”, Journal of Radar, vol. 5, no. 1, pp. 53–66, 2017 (In Persian).
[25] M. R. Taban, A. Sheikh Mozaffari, “Adaptive Radar Signal Detection in Gaussian Clutter with Autoregressive Model Using Kalman Fillter”, Journal of Radar, vol. 2, no. 3, pp. 1–12, 2014 (In Persian).

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History

  • Receive Date: 25 October 2017
  • Revise Date: 04 March 2018
  • Accept Date: 12 September 2018
  • Publish Date: 21 March 2018

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