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Space-Borne Compressed Sensing Based Receiver for Accurate Localization of Ground-Based Radars

By Esmaeil Ramezani, Mohammad Farzan Sabahi, and Seyyed Mohammad Saberali
Progress In Electromagnetics Research C, Vol. 99, 251-267, 2020


Space borne accurate emitter localization has become an important and indispensable part of electronic warfare (EW) systems. In this paper, a system-level approach to design space borne receiver for accurate localization of long range co-channel radars (e.g. a network of similar surveillance radars) is presented. Due to the wide frequency range of modern radar signals, the receiver should have wide instantaneous bandwidth and requires high sampling rate analog-to-digital converters (ADCs). To address this issue, we propose a receiver structure with an appropriate sub-Nyquist sampling scheme and fast sparse recovery algorithm. The proposed sub-Nyquist sampler employs a three dimensional uniform linear array (ULA), followed by a modulated wideband converter (MWC). To accurately estimate the location of the co-channel radars from sub-Nyquist samples, a novel quad-tree variational Bayesian expectation maximization (QVBEM) algorithm is proposed. The QVBEM algorithm minimizes the computational load and grid mismatch error by iteratively narrowing the search area. This is done by smart grid refinement around radars' locations. To evaluate the performance of the proposed receiver, location finding of pulsed radars is studied through numerical simulations in various scenarios. The results show that the proposed QVBEM method has a significantly lower estimation error than conventional deterministic and Bayesian approaches, with a reasonably computational complexity.


Esmaeil Ramezani, Mohammad Farzan Sabahi, and Seyyed Mohammad Saberali, "Space-Borne Compressed Sensing Based Receiver for Accurate Localization of Ground-Based Radars," Progress In Electromagnetics Research C, Vol. 99, 251-267, 2020.


    1. Bond, P. R., "Space defense," Jane’s Space Systems and Industry 2011-2012, 27th Edition, 97-109, MPG Books Group, Surrey, UK, 2011.

    2., Handbook of Space Technology, 1st Ed., 236-268, John Wiley & Sons Ltd., West Susex, UK, 2009.

    3. Curtis, H. D., "Satellite attitude dynamics," Orbital Mechanics for Engineering Students, 3rd Edition, 543-617, Butterworth-Heinemann, 2013.

    4. Donoho, D., "Compressed sensing," IEEE Trans. Inform. Theory, Vol. 52, No. 4, 1289-1306, Apr. 2006.

    5. Zhang, Z., Y. Xu, J. Yang, X. Li, and D. Zhang, "A survey of sparse representation: Algorithms and applications," IEEE Access, Vol. 3, 490-530, May 2015.

    6. Mishra, A. K. and R. S. Verster, "Compressive sensing: Acquisition and recovery," Compressive Sensing Based Algorithms for Electronic Defense, 1st Edition, 33-60, Springer, 2017.

    7. Ciuonzo, D., G. Romano, and R. Solimene, "Performance analysis of time-reversal MUSIC," IEEE Trans. Signal Process., Vol. 63, No. 10, 2650-2662, 2015.

    8. Devaney, A. J., "Time reversal imaging of obscured targets from multistatic data," IEEE. Trans. Antennas. Propag., Vol. 53, 1600-1610, May 2005.

    9. Ciuonzo, D. and P. Salvo Rossi, "Noncolocated time reversal MUSIC: High-SNR distribution of null spectrum," IEEE Signal Proc. Lett., Vol. 24, No. 4, 397-401, 2017.

    10. Ciuonzo, D., "On time-reversal imaging by statistical testing," IEEE Signal Proc. Lett., Vol. 24, No. 7, 1024-1028, Jul. 2017.

    11. Eldar, Y. C. and G. Kutyniok, "Xampling: Compressed sensing of analog signals," Compressed Sensing: Theory and Applications, 88-147, Cambridge University Press, UK, 2012.

    12. Sharma, S. K., E. Lagunas, S. Chatzinotas, and B. Ottersten, "Application of compressive sensing in cognitive radio communications: A survey," IEEE Commun. Survey & Tutorials, Vol. 18, No. 3, 1838-1860, Feb. 2016.

    13. Salari, S., I. M. Kim, F. Chan, and S. Rajan, "Blind compressive-sensing-based electronic warfare receiver," IEEE Trans. Aerospace and Electronic Systems, Vol. 53, No. 4, 2014-2030, Aug. 2017.

    14. Ramezani, E., M. F. Sabahi, and S. M. Saberali, "Joint frequency and two-dimensional direction of arrival estimation for electronic support systems based on sub-Nyquist sampling," IET Radar, Sonar & Navigation, Vol. 12, No. 8, 889-899, Apr. 2018.

    15. Yaghobi, M., M. Lexa, F. Millioz, and E. Davies, "A low complexity sub-Nyquist sampling system for wideband radar ESM receivers," Proc. IEEE Int. Conf. Acoust., Speech, Sig. Proc. (ICASSP), Florence, Italy, 2014.

    16. Rajan, S. and C. Wu, "An overview of compressive sensing-based receivers,", Technical Report, TR2013-149, Defense Research and Development Canada, Ottawa, Canada, Nov. 2013.

    17. Lin, E., "Compressed sensing for electronic radio frequency receiver: Detection, sensitivity, and implementation,", Ph.D. Dissertation, Dep. Elect. Eng., Wright State Univ., Dayton, OH, USA, 2016.

    18. Streetly, M., Jane’s Radar and Electronic Warfare Systems 2011-2012, 23rd Ed., lhs Jane’s, Coulsdon, UK, 2011.

    19. Sabahi, M. F., M. Masoumzadeh, and A. R. Forouzan, "Frequency-domain wideband compressive spectrum sensing," IET Communications, Vol. 10, No. 13, 1655-1664, 2016.

    20. Mishali, M. and Y. C. Eldar, "From theory to practice: Sub-Nyquist sampling of sparse wideband analog signals," IEEE J. Sel. Top. Signal Process., Vol. 4, No. 2, 375-391, 2010.

    21. Yang, E., X. Yan, and K. Qin, "Modulated wideband converter with run length limited sequences," IEICE Electron. Exp., Vol. 13, No. 17, 20160670, Sep. 2016.

    22. Stein, S., O. Yair, D. Cohen, and Y. C. Eldar, "CaSCADE: Compressed carrier and DOA estimation," IEEE Trans. Process., Vol. 65, No. 10, 2645-2658, 2017.

    23. Liu, L. and P. Wei, "A simplified sub-Nyquist receiver architecture for joint DOA and frequency estimation,", arXiv preprint arXiv:1604.05037v2, Feb. 2017.

    24. Kumar, A. A., S. G. Razul, and C. M. S. See, "Carrier frequency and direction of arrival estimation with nested sub-Nyquist sensor array receiver," Proc. 23rd Eur. Signal Process. Conf. (EUSIPCO), 1167-1171, Aug. 2015.

    25. Anal Kumar, A., S. G. Razul, and C. M. S. See, "Spectrum blind reconstruction and direction of arrival estimation of multi-band signals at sub-Nyquist sampling rates," Multidim. Syst. Sig. Proc., Vol. 29, No. 2, 643-669, Apr. 2018.

    26. Chen, T., L. Liu, and D. Pan, "A ULA-based MWC discrete compressed sampling structure for carrier frequency and AOA Estimation," IEEE Access, Vol. 5, 14154-14164, 2017.

    27. Liu, L. and P. Wei, "Joint DOA and frequency estimation with sub-Nyquist sampling for more sources than sensors," IET Radar Sonar Navig., Vol. 11, No. 12, 1798-1801, 2017.

    28. Foucart, F. and H. Rauhut, "Sparse solutions of underdetermined systems," A Mathematical Introduction to Compressive Sensing, 1st Edition, 41-59, Springer, 2013.

    29. Muthukrishnan, S., "Data streams: Algorithms and applications, foundations and trends," Theoretical Computer Science, Now Publishers, Boston, MA, 2005.

    30. Ji, S., Y. Xue, and L. Carin, "Bayesian compressive sensing," IEEE Trans. Signal Process., Vol. 56, No. 6, 2346-2356, Jun. 2008.

    31. Tipping, M. E., "Sparse Bayesian learning and relevance vector machine," J. Mach. Learn. Res., Vol. 1, 211-244, 2001.

    32. Tzikas, D. G., A. C. Likas, and N. P. Galatsanos, "The variational approximation for Bayesian inference," IEEE Signal Process. Mag., Vol. 25, No. 6, 131-146, Nov. 2008.

    33. Lundgren, M., L. Svensson, and L. Hammarstrand, "Variational Bayesian expectation maximization for radar map estimation," IEEE Trans. Signal Process., Vol. 64, No. 6, 1391-1404, Mar. 2016.

    34. Byeon, M., M. Lee, K. Kim, and J. Y. Choi, "Variational inference for 3-D localization and tracking of multiple cameras," IEEE Trans. Neural Netw. and Learn. Syst., Vol. 99, 1-15, Jan. 2019.

    35. Arjoune, Y., N. Kaabouch, H. El Ghazi, and A. Tamtaoui, "Compressive sensing: Performance comparison of sparse recovery algorithms," 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, Jan. 2017.

    36. Chi, Y., A. Pezeshki, L. Scharf, and R. Caderbank, "Sensitivity to basis mismatch in compressed sensing," IEEE Trans. Signal Process., Vol. 59, No. 5, 2182-2195, May 2011.

    37. Tang, G., B. N. Bhaskar, P. Shah, and B. Recht, "Compressed sensing off the grid," IEEE Trans. Inform. Theory, Vol. 59, No. 11, 7465-7490, Nov. 2013.

    38. Lu, Z., R. Ying, S. Jiang, P. Liu, and W. Yu, "Distributed compressed sensing off the grid," IEEE Signal Proc. Lett., Vol. 22, No. 1, 105-109, Jan. 2015.

    39. Yang, Z., L. Xie, and C. Zhang, "Off-grid direction of arrival estimation using sparse Bayesian inference," IEEE Trans. Signal Process., Vol. 61, 38-43, 2013.

    40. Das, A. and T. J. Sejnowski, "Narrowband and wideband off-grid direction-of-arrival estimation via sparse Bayesian learning," IEEE J. Ocean. Eng., Vol. 3, No. 1, 108-118, Jan. 2018.

    41. Samet, H. and R. A. Earnshaw, "An overview of quadtrees octrees and related hierarchical data structures," Theoretical Foundations of Computer Graphics and CAD, Vol. 40, Springer, Berlin, Germany, 1988.

    42. Donoho, D. L., Y. Tsaig, I. Drori, and J. L. Starck, "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit," IEEE Trans. Inform. Theory, Vol. 58, No. 2, 1094-1121, 2012.