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Multi-way Compressive Sensing Based 2D DOA Estimation Algorithm for Monostatic MIMO Radar with Arbitrary Arrays

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Abstract

This paper addresses the problem of two-dimensional (2D) direction of arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar equipped with arbitrary arrays manifold. A low-complexity multi-way compressive sensing (MCS) 2D DOA estimation algorithm is derived in this paper. To ease the computational burden, the received data are first arranged into a trilinear model, and then three random compression matrixes are individually applied to reduce each tensor to a much smaller tensor. Finally, the 2D DOA estimation problem then links to the low-dimensional trilinear model. The proposed algorithm has estimation performance close to that of the trilinear decomposition algorithm while it is more appealing form the perspective of storage capacity. Compared with other estimation algorithms, such as multiple signal classification, our MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also doesn’t require array interpolation of the array geometry, therefore the proposed algorithm has much less computational load, which indicates the proposed algorithm will have wide application in the future for Massive MIMO systems.

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References

  1. Fishler, E., Haimovich, A., Blum, R., Chizhik, D., Cimini, L., Valenzuela, R. (2004). MIMO radar: An idea whose time has come. In Proceedings of the IEEE radar conference (pp. 71–78).

  2. Haimovich, A. M., Blum, R. S., & Cimini, L. J. (2008). MIMO radar with widely separated antennas. IEEE Signal Processing Magazine, 25(1), 116–129.

    Article  Google Scholar 

  3. Zhang, X., Huang, Y., Chen, C., Li, J., & Xu, D. (2012). Reduced-complexity Capon for direction of arrival estimation in a monostatic multiple-input multiple-output radar. IET Radar, Sonar and Navigation, 6(8), 796–801.

    Article  Google Scholar 

  4. Yan, H., Li, J., & Liao, G. (2008). Multitarget identification and localization using bistatic MIMO radar systems. EURASIP Journal on Advances in Signal Processing, 2008, 48.

    Google Scholar 

  5. Duofang, C., Baixiao, C., & Guodong, Q. (2008). Angle estimation using ESPRIT in MIMO radar. Electronics Letters, 44(12), 770–771.

    Article  Google Scholar 

  6. Zhang, X., Xu, Z., Xu, L., & Xu, D. (2011). Trilinear decomposition-based transmit angle and receive angle estimation for multiple-input multiple-output radar. IET Radar, Sonar and Navigation, 5(6), 626–631.

    Article  Google Scholar 

  7. Kruskal, J. B. (1977). Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics. Linear Algebra and its Applications, 18(2), 95–138.

    Article  MathSciNet  MATH  Google Scholar 

  8. Jon, A.B. (2002). Genetic algorithms as a tool for opportunistic phased array radar design. California Naval Postgraduate School.

  9. Cao, Y., Zhang, Z., Dai, F., & Xie, R. (2011). Fast DOA estimation for monostatic MIMO radar with arbitrary array configurations. IEEE Radar Conference, 1, 959–962.

    Google Scholar 

  10. Cao, Y., Zhang, Z., Dai, F., & Xie, R. (2012). Direction of arrival estimation for monostatic multiple-input multiple-output radar with arbitrary array structures. IET Radar, Sonar and Navigation, 6(7), 679–686.

    Article  Google Scholar 

  11. Chen, C., & Zhang, X. (2013). A low-complexity joint 2D-DOD and 2D-DOA estimation algorithm for MIMO radar with arbitrary arrays. International Journal of Electronics, 100(10), 1455–1469.

    Article  Google Scholar 

  12. Friedlander, B. (1993). The root-MUSIC algorithm for direction finding with interpolated arrays. IEEE Transaction on Signal Processing, 30(1), 15–19.

    MATH  Google Scholar 

  13. Belloni, F., Richter, A., & Koivunen, V. (2007). DoA estimation via manifold separation for arbitrary array structures. IEEE Transaction on Signal Processing, 55(10), 4800–4810.

    Article  MathSciNet  Google Scholar 

  14. Rusek, F., Persson, D., Lau, B. K., Larsson, E. G., Marzetta, T. L., Edfors, O., & Tufvesson, F. (2013). Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 30(1), 40–60.

    Article  Google Scholar 

  15. Larsson, E., Edfors, O., Tufvesson, F., & Marzetta, T. (2014). Massive MIMO for next generation wireless systems. IEEE Communications Magazine, 52(2), 186–195.

    Article  Google Scholar 

  16. Donoho, D. L. (2006). Compressed sensing. IEEE Transaction Information Theory, 52(4), 1289–1306.

    Article  MathSciNet  MATH  Google Scholar 

  17. Yao, Yu., Petropulu, A. P., & Poor, H. V. (2011). Measurement matrix design for compressive sensing-based MIMO radar. IEEE Transactions on Signal Processing, 59(11), 5338–5352.

    Article  MathSciNet  Google Scholar 

  18. Jindong, Z., Daiyin, Z., & Gong, Z. (2013). Adaptive compressed sensing radar oriented toward cognitive detection in dynamic sparse target scene. IEEE Transactions on Signal Processing, 60(4), 1718–1729.

    Google Scholar 

  19. Sidiropoulos, N. D., & Kyrillidis, A. (2012). Multi-way compressed sensing for sparse low-rank tensors. IEEE Signal Processing Letters, 19(11), 757–760.

    Article  Google Scholar 

  20. Renzheng, C., Xiaofei, Z., & Weiyang, C. (2014). Compressed sensing parallel factor analysis-based joint angle and Doppler frequency estimation for monostatic multiple-input–multiple-output radar. IET Radar, Sonar and Navigation, IET Radar Sonar Navigation, 8(6), 597–606.

    Article  Google Scholar 

  21. Jiang, T., & Sidiropoulos, N. D. (2004). Kruskal’s permutation lemma and the identification of CANDECOMP/PARAFAC and bilinear models with constant modulus constraints. IEEE Transactions on Signal Processing, 52(9), 2625–2636.

    Article  MathSciNet  Google Scholar 

  22. Stoica, P., & Nehorai, A. (1990). Performance study of conditional and unconditional direction-of-arrival estimation. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(10), 1783–1795.

    Article  MATH  Google Scholar 

  23. Arias-Castro, E., & Eldar, Y. C. (2011). Noise folding in compressed sensing. IEEE Signal Processing Letters, 18(8), 478–481.

    Article  Google Scholar 

  24. Rossi, M., Haimovich, A. M., & Eldar, Y. C. (2014). Spatial compressive sensing for MIMO radar. IEEE Transactions on Signal Processing, 62(2), 419–430.

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This work is supported by China NSF Grants (61071163, 61271327 and 61471191), Funding for Outstanding Doctoral Dissertation in NUAA (BCXJ14-08), Funding of Jiangsu Innovation Program for Graduate Education (KYLX_0277), the Fundamental Research Funds for the Central Universities (3082015NP2015504), and partly funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PADA).

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Authors and Affiliations

  1. College of Electronics and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Fang-Qing Wen & Gong Zhang

  2. Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing, 210016, China

    Fang-Qing Wen & Gong Zhang

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  1. Fang-Qing Wen
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  2. Gong Zhang
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Correspondence to Gong Zhang.

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Wen, FQ., Zhang, G. Multi-way Compressive Sensing Based 2D DOA Estimation Algorithm for Monostatic MIMO Radar with Arbitrary Arrays. Wireless Pers Commun 85, 2393–2406 (2015). https://doi.org/10.1007/s11277-015-2911-3

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