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3-D Target Localization Based on Bi-static Range Measurements in Widely Separated MIMO Radars

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Abstract

In this paper, a three-dimensional target localization problem in widely separated multiple-input multiple-output radars is solved using two specific techniques based on time difference of arrival measurements. These techniques are provided in terms of transmitter and receiver antennas, which are named as technique_t and technique_r, respectively. The localization problem is rewritten as a non-convex optimization problem which is based on a least-squares method without any initial estimation. Therefore, a convex semidefinite programming problem is obtained by utilizing the semidefinite relaxation method for the problem which can be performed via the CVX toolbox. Several simulations are provided to evaluate the positioning accuracy in terms of bi-static range error for 3 and 4 transmitter/receiver antennas, different antenna arrangements, and near/far target. In other simulations, the localization accuracy is evaluated in terms of the empirical cumulative density function of positioning error. The results show that the proposed techniques have better accuracy and performance in different scenarios in comparison with other compared methods. The last simulation also demonstrates that the computational time of the mentioned techniques is 0.69 s which is suitable for real-time processing.

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References

  1. Aubry, A., Maio, A. D., & Huang, Y. (2016). MIMO radar beampattern design via PSL/ISL optimization. IEEE Transactions on Signal Processing, 64(15), 3955–3967.

    Article  MathSciNet  Google Scholar 

  2. Feraidooni, M. M., Gharavian, D., Alaee-Kerahroodi, M., & Imani, S. (2020). A coordinate descent framework for probing signal design in cognitive MIMO radars. IEEE Communications Letters, 24, 1115–1118.

    Article  Google Scholar 

  3. Abtahi, A., Azghani, M., & Marvasti, F. (2019). An adaptive iterative thresholding algorithm for distributed MIMO radars. IEEE Transactions on Aerospace and Electronic Systems, 55(2), 523–533.

    Article  Google Scholar 

  4. Imani, S., Feraidooni, M. M., Gharavian, D., & Nayebi, M. M. (2020). SINR improvement based on joint design of transmit covariance matrix and receive filter design for colocated MIMO radar. IET Communications, pp. 1–10.

  5. Janatian, N., Modarres-Hashemi, M., & Sheikhi, A. (2013). CFAR detectors for MIMO radars. CSSP, 32(3), 1389–1418.

    Google Scholar 

  6. Feraidooni, M. M., Gharavian, D., & Imani, S. (2019). Signal-dependent interference reduction based on a new transmit covariance matrix and receive filter design for co-located MIMO radar. Signal, Image and Video Processing, 13, 1275–1282.

    Article  Google Scholar 

  7. Gong, J., Lou, S., & Guo, Y. (2019). A robust angle estimation method for bistatic MIMO radar about non-stationary random noise. Wireless Personal Communications, 106(2), 439–450.

    Article  Google Scholar 

  8. Feraidooni, M.M., Alaee-Kerahroodi, M., Imani, S., & Gharavian, D. (2019). Designing set of binary sequences and space-time receive filter for moving targets in colocated MIMO radar systems. In 2019 20th International Radar Symposium (IRS).

  9. Haghnegahdar, M., Imani, S., Ghorashi, S. A., & Mehrshahi, E. (2017). A new iterative approach in SINR improvement of MIMO radars by using combination of orthogonal waveforms. Wireless Personal Communications, 97(2), 2069–2085.

    Article  Google Scholar 

  10. Feraidooni, M. M., Gharavian, D., Imani, S., & Kerahroodi, M. A. (2020). Designing m-ary sequences and space-time receive filter for moving target in cognitive MIMO radar systems. Signal Processing, 174, 107620.

    Article  Google Scholar 

  11. Li, J., & Stoica, P. (2009). MIMO radar signal processing. New York, NY: Wiley.

    Google Scholar 

  12. 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 

  13. Imani, S., Nayebi, M. M., & Ghorashi, S. A. (2017). Transmit signal design in colocated MIMO radars without covariance matrix optimization. IEEE Transactions on Aerospace and Electronic Systems, 99, 2178–2186.

    Article  Google Scholar 

  14. Amiri, R., Behnia, F., & Sadr, M. A. M. (2017). Positioning in MIMO radars based on constrained least squares estimation. IEEE Communications Letters, 21, 2222–2225.

    Article  Google Scholar 

  15. Amiri, R., Behnia, F., & Noroozi, A. (2019). Efficient algebraic solution for elliptic target localisation and antenna position refinement in multiple-input-multiple-output radars. IET Radar, Sonar Navigation, 13(11), 2046–2054.

    Article  Google Scholar 

  16. Wang, Y., Wu, Y., & Shen, Y. (2019). On the resolution limits for MIMO localization. IEEE Communications Letters, 23, 462–465.

    Article  Google Scholar 

  17. Amiri, R., & Behnia, F. (2017). An efficient weighted least squares estimator for elliptic localization in distributed MIMO radars. IEEE Signal Processing Letters, 24, 902–906.

    Article  Google Scholar 

  18. Noroozi, A., & Sebt, M. A. (2015). Target localization from bistatic range measurements in multi-transmitter multi-receiver passive radar. IEEE Signal Processing Letters, 22, 2445–2449.

    Article  Google Scholar 

  19. Du, Y., & Wei, P. (2014). An explicit solution for target localization in noncoherent distributed MIMO radar systems. IEEE Signal Processing Letters, 21, 1093–1097.

    Article  Google Scholar 

  20. Noroozi, A., Oveis, A. H., & Sebt, M. A. (2017). Iterative target localization in distributed MIMO radar from bistatic range measurements. IEEE Signal Processing Letters, 24, 1709–1713.

    Article  Google Scholar 

  21. Amiri, R., Behnia, F., & Zamani, H. (2017). Asymptotically efficient target localization from bistatic range measurements in distributed MIMO radars. IEEE Signal Processing Letters, 24, 299–303.

    Article  Google Scholar 

  22. Noroozi, A., Sebt, M. A., Hosseini, S. M., Amiri, R., & Nayebi, M. M. (2020). Closed-form solution for elliptic localization in distributed MIMO radar systems with minimum number of sensors. IEEE Transactions on Aerospace and Electronic Systems, 56(4), 3123–3133.

    Article  Google Scholar 

  23. Wang, G., So, A. M. C., & Li, Y. (2016). Robust convex approximation methods for TDOA-based localization under NLOS conditions. IEEE Transactions on Signal Processing, 64, 3281–3296.

    Article  MathSciNet  Google Scholar 

  24. Shi, X., Anderson, B. D. O., Mao, G., Yang, Z., Chen, J., & Lin, Z. (2016). Robust localization using time difference of arrivals. IEEE Signal Processing Letters, 23, 1320–1324.

    Article  Google Scholar 

  25. Yang, H., Chun, J., & Chae, D. (2015). Hyperbolic localization in MIMO radar systems. IEEE Antennas and Wireless Propagation Letters, 14, 618–621.

    Article  Google Scholar 

  26. Yang, K., Wang, G., & Luo, Z. Q. (2009). Efficient convex relaxation methods for robust target localization by a sensor network using time differences of arrivals. IEEE Transactions on Signal Processing, 57, 2775–2784.

    Article  MathSciNet  Google Scholar 

  27. Wang, G., Li, Y., & Ansari, N. (2013). A semidefinite relaxation method for source localization using TDOA and FDOA measurements. IEEE Transactions on Vehicular Technology, 62, 853–862.

    Article  Google Scholar 

  28. Wang, Y., & Wu, Y. (2017). An efficient semidefinite relaxation algorithm for moving source localization using TDOA and FDOA measurements. IEEE Communications Letters, 21, 80–83.

    Article  Google Scholar 

  29. Amiri, R., Behnia, F., & Noroozi, A. (2019). Efficient joint moving target and antenna localization in distributed MIMO radars. IEEE Transactions on Wireless Communications, 18(9), 4425–4435.

    Article  Google Scholar 

  30. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. New York, NY: Cambridge University Press.

    Book  Google Scholar 

  31. Grant, M., & Boyd, S. (2014). CVX: Matlab software for disciplined convex programming, version 2.1. http://cvxr.com/cvx.

  32. Ho, K., & Xu, W. (2004). An accurate algebraic solution for moving source location using TDOA and FDOA measurements. IEEE Transactions on Signal Processing, 52, 2453–2463.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Telecommunications, Faculty of Electrical Engineering, Shahid Beheshti University, G. C., Tehran, Iran

    Mohammad Mahdi Feraidooni, Davood Gharavian & Sadjad Imani

  2. Department of Telecommunications, Faculty of Electrical Engineering, Shahed University, G. C., Tehran, Iran

    Mohammad Peimany

Authors
  1. Mohammad Mahdi Feraidooni
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  2. Davood Gharavian
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  3. Mohammad Peimany
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  4. Sadjad Imani
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Correspondence to Davood Gharavian.

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Feraidooni, M.M., Gharavian, D., Peimany, M. et al. 3-D Target Localization Based on Bi-static Range Measurements in Widely Separated MIMO Radars. Wireless Pers Commun 118, 3565–3584 (2021). https://doi.org/10.1007/s11277-021-08197-6

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