Signal-dependent interference reduction based on a new transmit covariance matrix and receive filter design for colocated MIMO radar

  • Mohammad Mahdi Feraidooni
  • Davood GharavianEmail author
  • Sadjad Imani
Original Paper


Waveform design is an important topic in diverse radars, especially MIMO radars. In this paper, the signal-to-interference plus noise ratio (SINR) has been improved using the proposed covariance matrix and filter coefficients in colocated MIMO radars. In the proposed method, transmit waveform covariance matrix and receive filter coefficients have been jointly optimized to make the desired transmit beam pattern to the target direction, reject a maximum number of signal-dependent interfering sources and prepare suitable SINR and degree of freedom (DOF). Simulation results show that the proposed method has appropriate and significant SINR values, DOF and computational time in comparison with other methods. Moreover, the constant and equal power is transmitted through each antenna element in the proposed method and similar elements can be used to design the transmitters. Therefore, the suitable computational time and complexity and also the simple design of transmitters have made the proposed method more practical than other compared methods.


Colocated MIMO radar Covariance matrix Interference Optimization SINR Waveform design 



  1. 1.
    Haimovich, A.M., Blum, R.S., Cimini, L.J.: MIMO radar with widely separated antennas. IEEE Signal Process. Mag. 25(1), 116–129 (2008)CrossRefGoogle Scholar
  2. 2.
    Li, J., Stoica, P.: MIMO radar with colocated antennas. IEEE Signal Process. Mag. 24(5), 106–114 (2007)CrossRefGoogle Scholar
  3. 3.
    Li, J., Stoica, P., Xu, L., Roberts, W.: On parameter identifiability of MIMO radar. IEEE Signal Process. Lett. 14(12), 968–971 (2007)CrossRefGoogle Scholar
  4. 4.
    Li, L.: Joint parameter estimation and target localization for bistatic MIMO radar system in impulsive noise. Signal Image Video Process. 9(8), 1775–1783 (2015)CrossRefGoogle Scholar
  5. 5.
    Imani, S., Bolhasani, M., Ghorashi, S.A., Rashid, M.: Waveform design in MIMO radar using radial point interpolation method. IEEE Commun. Lett. 22(10), 2076–2079 (2018)CrossRefGoogle Scholar
  6. 6.
    Hadi, M.A., Alshebeili, S., Jamil, K., El-Samie, F.E.A.: Compressive sensing applied to radar systems: an overview. Signal Image Video Process. 9(1), 25–39 (2015)CrossRefGoogle Scholar
  7. 7.
    Karbasi, S.M., Aubry, A., De Maio, A., Bastani, M.H.: Robust transmit code and receive filter design for extended targets in clutter. IEEE Trans. Signal Process. 63(8), 1965–1976 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Imani, S., Ghorashi, S.A., Bolhasani, M.: SINR maximization in colocated MIMO radars using transmit covariance matrix. Signal Process. 119, 128–135 (2016)CrossRefGoogle Scholar
  9. 9.
    Lipor, J., Ahmed, S., Alouini, M.: Fourier-based transmit beampattern design using MIMO radar. IEEE Trans. Signal Process. 62(9), 2226–2235 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Imani, S., Ghorashi, S.A.: Sequential quasi-convex-based algorithm for waveform design in colocated multiple-input multiple-output radars. IET Signal Process. 10(3), 309–317 (2016)CrossRefGoogle Scholar
  11. 11.
    Stoica, P., Li, J., Xie, Y.: On probing signal design for MIMO radar. IEEE Trans. Signal Process. 55(8), 4151–4161 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Imani, S., Ghorashi, S.A.: Transmit signal and receive filter design in co-located MIMO radar using a transmit weighting matrix. IEEE Signal Process. Lett. 22(10), 1521–1524 (2015)CrossRefGoogle Scholar
  13. 13.
    Haghnegahdar, M., Imani, S., Ghorashi, S.A., Mehrshahi, E.: A new iterative approach in SINR improvement of MIMO radars by using combination of orthogonal waveforms. Wirel. Pers. Commun. 97(2), 2069–2085 (2017)CrossRefGoogle Scholar
  14. 14.
    Cheng, X., Aubry, A., Ciuonzo, D., De Maio, A., Wang, X.: Robust waveform and filter bank design of polarimetric radar. IEEE Trans. Aerosp. Electron. Syst. 53(1), 370–384 (2017)CrossRefGoogle Scholar
  15. 15.
    Cui, G., Li, H., Rangaswamy, M.: MIMO radar waveform design with constant modulus and similarity constraints. IEEE Trans. Signal Process. 62(2), 343–353 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Ahmed, S., Alouini, M.: MIMO-radar waveform covariance matrix for high SINR and low side-lobe levels. IEEE Trans. Signal Process, 62(8), 2056–2065 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Haghnegahdar, M., Imani, S., Ghorashi, S.A., Mehrshahi, E.: SINR enhancement in colocated MIMO radar using transmit covariance matrix optimization. IEEE Signal Process. Lett. 24(3), 339–343 (2017)CrossRefGoogle Scholar
  18. 18.
    Ahmed, S., Thompson, J., Petillot, Y., Mulgrew, B.: Finite alphabet constant-envelope waveform design for MIMO radar. IEEE Trans. Signal Process. 59(11), 5326–5337 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Liu, J., Li, H., Himed, B.: Joint optimization of transmit and receive beamforming in active arrays. IEEE Signal Process. Lett. 21(1), 39–42 (2014)CrossRefGoogle Scholar
  20. 20.
    Gao, Y., Li, H., Himed, B.: Joint transmit and receive beamforming for hybrid active–passive radar. IEEE Signal Process. Lett. 24(6), 779–783 (2017)CrossRefGoogle Scholar
  21. 21.
    Imani, S., Nayebi, M.M., Ghorashi, S.A.: Transmit signal design in co-located MIMO radar without covariance matrix optimization. IEEE Trans. Aerosp. Electron. Syst. 53(5), 2178–2186 (2017)CrossRefGoogle Scholar
  22. 22.
    Greco, M., Gini, F., Farina, A.: Radar detection and classification of jamming signals belonging to a cone class. IEEE Trans. Signal Process. 56(5), 1984–1993 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Bandiera, F., Farina, A., Orlando, D., Ricci, G.: Detection algorithms to discriminate between radar targets and ECM signals. IEEE Trans. Signal Process. 58(12), 5984–5993 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Carotenuto, V., De Maio, A., Orlando, D., Pallotta, L.: Adaptive radar detection using two sets of training data. IEEE Trans. Signal Process. 66(7), 1791–1801 (2018)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Aubry, A., De Maio, A., Naghsh, M.M.: Optimizing radar waveform and Doppler filter bank via generalized fractional programming. IEEE J. Sel. Top. Signal Process. 9(8), 1387–1399 (2015)CrossRefGoogle Scholar
  26. 26.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, New York (2004)CrossRefzbMATHGoogle Scholar
  27. 27.
    Stoica, P., Li, J., Zhu, X.: Waveform synthesis for diversity-based transmit beampattern design. IEEE Trans. Signal Process. 56(6), 2593–2598 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Jardak, S., Ahmed, S., Alouini, M.: Generation of correlated finite alphabet waveforms using Gaussian random variables. IEEE Trans. Signal Process. 62(17), 4587–4596 (2014)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Telecommunications, Faculty of Electrical EngineeringShahid Beheshti University, G. C.TehranIran

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