Skip to main content
SpringerLink
Log in

Unitary Subspace-Based Method for Angle Estimation in Bistatic MIMO Radar

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

In this paper, the issue of joint angle estimation for bistatic multiple-input multiple-output (MIMO) radar is investigated, and an algorithm for the joint estimation under real-valued computation is proposed. By utilizing the unitary transformation, the direction matrices and the data matrix are transformed to be real-valued ones. The direction of departure (DOD) can be estimated via the real-valued rotational invariance in the subspace, and the direction of arrival (DOA) can be obtained via the real-valued reduced-dimension function of multiple signal classification (MUSIC). The proposed algorithm utilizes both the signal and noise subspaces, requires no peak searching, and can achieve automatically paired estimations of the angles. Furthermore, it has better angle estimation performance than some existing methods. The simulation results verify the algorithmic effectiveness and robustness of the proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. M.L. Bencheikh, Y. Wang, Joint DOD-DOA estimation using combined ESPRIT-MUSIC approach in MIMO radar. Electron. Lett. 46(15), 1081–1083 (2010)

    Article  Google Scholar 

  2. M.L. Bencheikh, Y.D. Wang, H.Y. He, Polynomial root finding technique for joint DOA DOD estimation in bistatic MIMO radar. Signal Process. 90(9), 2723–2730 (2010)

    Article  MATH  Google Scholar 

  3. R. Bro, N.D. Sidiropoulos, G.B. Giannakis, A fast least squares algorithm for separating trilinear mixtures, in Proc. Inter. Workshop ICA and BSS, France, Jan. (1999), pp. 289–294

    Google Scholar 

  4. D.F. Chen, B.X. Chen, G.D. Qin, Angle estimation using ESPRIT in MIMO radar. Electron. Lett. 44(12), 770–771 (2008)

    Article  Google Scholar 

  5. J. Chen, H. Gu, W. Su, Angle estimation using ESPRIT without pairing in MIMO radar. Electron. Lett. 44(24), 1422–1423 (2008)

    Article  Google Scholar 

  6. E. Fishler, A. Haimovich, R.S. Blum, et al., MIMO radar: an idea whose time has come, in Proc. IEEE Radar Conf., Apr. (2004), pp. 71–78

    Google Scholar 

  7. X. Gao, X. Zhang, G. Feng, Z. Wang, D. Xu, On the MUSIC-derived approaches of angle estimation for bistatic MIMO radar, in 2009 International Conf. Wire. Networks and Inf. Syst. (WNIS ’09), Shanghai, China, Dec. 28–29 (2009), pp. 343–346

    Chapter  Google Scholar 

  8. M. Haardt, J.A. Nossek, Unitary ESPRIT: how to obtain increased estimation accuracy with a reduced computational burden. IEEE Trans. Signal Process. 43(5), 1232–1242 (1995)

    Article  Google Scholar 

  9. J. Li, P. Stoica, MIMO radar—diversity means superiority, in Proc. 14th Adaptive Sensor Array Process. Workshop (ASAP ’06), Lincoln Lab, MA, USA, Dec. (2006), pp. 1–6

    Google Scholar 

  10. X. Li, Z. Zhang, W. Mao et al., A study of frequency diversity MIMO radar beamforming, in IEEE 10th International Conference (ICSP2010) (2010), pp. 352–356

    Google Scholar 

  11. I. Marcu, S. Halunga, O. Fratu, D. Vizireanu, Multiuser systems implementations in fading environments, in Applications of MATLAB in Science and Engineering, ed. by T. Michałowski, September 9 (InTech, Rijeka, 2011), pp. 167–180. ISBN 978-953-307-708-6

    Google Scholar 

  12. D. Nion, N.D. Sidiropoulos, A PARAFAC-based technique for detection and localization of multiple targets in a MIMO radar system, in 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Apr. (2009), pp. 2077–2080

    Chapter  Google Scholar 

  13. D. Nion, N.D. Sidiropoulos, Adaptive algorithms to track the PARAFAC decomposition of a third-order tensor. IEEE Trans. Signal Process. 57(6), 2299–2310 (2009)

    Article  MathSciNet  Google Scholar 

  14. B.D. Rao, K.V.S. Hari, Performance analysis of ESPRIT and TAM in determining the direction of arrival of plane waves in noise. IEEE Trans. Acoust. Speech Signal Process. 40(12), 1990–1995 (1989)

    Article  Google Scholar 

  15. P. Stoica, A. Nehorai, Performance study of conditional and unconditional direction-of-arrival estimation. IEEE Trans. Signal Process. 38(10), 1783–1795 (1990)

    MATH  Google Scholar 

  16. P. Stoica, A. Nehorai, Performance comparison of subspace rotation and MUSIC methods for direction estimation. IEEE Trans. Signal Process. 39(2), 446–453 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  17. C. Voicu, S. Halunga, D. Vizireanu, Performances of conventional and MMSE detectors for image transmissions, in Telecommunication in Modern Satellite Cable and Broadcasting Services (TELSIKS). 10th Intern. Conf., vol. 1, 5–8 Oct. (2011), pp. 76–79

    Google Scholar 

  18. H. Yan, J. Li, G. Liao, Multitarget identification and localization using bistatic MIMO radar systems. EURASIP J. Adv. Signal Process., 283483 (2008), pp. 1–8

    Google Scholar 

  19. C. Yunhe, Joint estimation of angle and Doppler frequency for bistatic MIMO radar. Electron. Lett. 46(2), 170–172 (2010)

    Article  Google Scholar 

  20. X. Zhang, D. Xu, Angle estimation in MIMO radar using reduced-dimension capon. Electron. Lett. 46(12), 860–861 (2010)

    Article  Google Scholar 

  21. X. Zhang, L. Xu, L. Xu, D. Xu, Direction of departure (DOD) and direction of arrival (DOA) estimation in MIMO radar with reduced-dimension MUSIC. IEEE Commun. Lett. 14(12), 1161–1163 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. G. Zheng, B. Chen, M. Yang, Unitary ESPRIT algorithm for bistatic MIMO radar. Electron. Lett. 48(3), 179–181 (2012)

    Article  Google Scholar 

  24. Z.D. Zheng, J.Y. Zhang, Fast method for multi-target localization in bistatic MIMO radar. Electron. Lett. 47(2), 138–139 (2011)

    Article  Google Scholar 

  25. M.D. Zoltowski, M. Haardt, C.P. Mathews, Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via unitary ESPRIT. IEEE Trans. Signal Process. 44(2), 316–328 (1996)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by China NSF Grants (61201208, 61271327, 61071164), Jiangsu Planned Projects for Postdoctoral Research Funds (1201039C), the China Postdoctoral Science Foundation (2012M521099), the Hubei Key Laboratory of Intelligent Wire1ess Communications (IWC2012002), the Open project of the Key Laboratory of Nondestructive Testing (Nanchang Hangkong University), the Aeronautical Science Foundation of China (20120152001), the Qing Lan Project, Research Innovation Program for College Graduates of Jiangsu Province (CXZZ13_0165), Funding for Outstanding Doctoral Dissertation in NUAA (BCXJ13-09), PAPD of Jiangsu Higher Education Institutions, and the Fundamental Research Funds for the Central Universities (NS2013024, NZ2012010, kfjj120115, kfjj20110215).

Author information

Authors and Affiliations

  1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 210016, China

    Jianfeng Li & Xiaofei Zhang

Authors
  1. Jianfeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaofei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianfeng Li.

Appendix

Appendix

Define h t=−angle(a r (ϕ k )), which contains the ambiguous phases. Set the initial elements of h as zeros. The unambiguous phases can be identified via the following two steps [19].

  1. 1.

    Set the first element: h(1)=0.

  2. 2.

    h(n+1)=h(n)+δ(n),n=1,…,N−1 where

    $$ \delta ( n ) = \left \{ \begin{array}{l@{\quad}l} \mathbf{h}^{t} ( n + 1 ) - \mathbf{h}^{t} ( n ) & \bigl\vert \mathbf{h}^{t} ( n + 1 ) - \mathbf{h}^{t} ( n ) \bigr\vert \le \pi\\ \mathbf{h}^{t} ( n + 1 ) - \mathbf{h}^{t} ( n ) - 2\pi & \mathbf{h}^{t} ( n + 1 ) - \mathbf{h}^{t} ( n ) > \pi \\ \mathbf{h}^{t} ( n + 1 ) - \mathbf{h}^{t} ( n ) + 2\pi & \mathbf{h}^{t} ( n + 1 ) - \mathbf{h}^{t} ( n ) < \pi \end{array} \right .$$

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, J., Zhang, X. Unitary Subspace-Based Method for Angle Estimation in Bistatic MIMO Radar. Circuits Syst Signal Process 33, 501–513 (2014). https://doi.org/10.1007/s00034-013-9653-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-013-9653-9

Keywords

Search

Navigation