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Cognitive Design of Radar Waveform and the Receive Filter for Multitarget Parameter Estimation

  • Yu YaoEmail author
  • Junhui Zhao
  • Lenan Wu
Article

Abstract

This research work considers waveform design for an adaptive radar system. The aim is to achieve enhanced feature extraction performance for multiple extended targets. There are two scenarios to consider: multiple extended targets separated in range and multiple extended targets close in range. We propose a waveform optimization scheme based on Kalman filtering by minimizing the mean square error of separated target scattering coefficient estimation and a waveform optimization approach by minimizing the mean square error of closed power spectrum density estimation. A convex cost function is established, and the optimal solution can be obtained using the existing convex programming algorithm. With subsequent iterations of the algorithm, the simulation results demonstrate an improvement in the estimation of target parameters from the dynamic scene, such as target scattering coefficient and power spectrum density, while maintaining relatively lower computational complexity.

Keywords

Kalman filtering Target scattering coefficient estimation Power spectrum density estimation Waveform optimization Multiple extended targets 

Mathematics Subject Classification

15A69 81P40 90C3 

Notes

Acknowledgements

This work was supported by the national Natural Science Foundation of China (61761019, 61861017, 61861018, 61862024) and the Natural Science Foundation of Jiangxi Province (Jiangxi Province natural Science Fund) (20181BAB211014, 20181BAB211013), and Foundation of Jiangxi Educational Committee of China (GJJ170414).

References

  1. 1.
    Haykin, S.: Cognitive radar: “a way of the future”. IEEE Signal Process. Mag. 23(1), 30–40 (2006)CrossRefGoogle Scholar
  2. 2.
    Haykin, S.: Cognitive Dynamic Systems: Perception-Action Cycle, Radar and Radio. Cambridge University Press, Cambridge (2012)CrossRefzbMATHGoogle Scholar
  3. 3.
    Farina, A., De Maio, A., Haykin, S.: The Impact of Cognition on Radar Technology. Scitech Publishing, IET (2017)CrossRefGoogle Scholar
  4. 4.
    Aubry, A., Demaio, A., Farina, A., Wicks, M.: Knowledge-aided (potentially cognitive) transmit signal and receive filter design in signal-dependent clutter. IEEE Trans. Aerosp. Electron. Syst. 49(1), 93–117 (2013)CrossRefGoogle Scholar
  5. 5.
    Zhang, J.D., Zhu, D.Y., Zhang, G.: Adaptive compressed sensing radar oriented toward cognitive detection in dynamic sparse target scene. IEEE Trans. Signal Process. 60(4), 1718–1729 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Patton, L.K., Frost, S.W., Rigling, B.D.: Efficient design of radar waveforms for optimized detection in colored noise. IET Radar Sonar Navig. 6(1), 21–29 (2012)CrossRefGoogle Scholar
  7. 7.
    Romero, R.A., Goodman, N.A.: Waveform design in signal-dependent interference and application to target recognition with multiple transmissions. IET Radar Sonar Navig. 3(4), 328–340 (2009)CrossRefGoogle Scholar
  8. 8.
    Gong, X.H., Meng, H.D., Wei, Y.M., Wang, X.Q.: Phase-modulated waveform design for extended target detection in the presence of clutter. Sensors 11(7), 7162–7177 (2011)CrossRefGoogle Scholar
  9. 9.
    Aubry, A., Carotenuto, V., Maio, A.D.: Optimization theory-based radar waveform design for spectrally dense environments. IEEE Aerosp. Electron. Syst. Mag. 31(12), 14–25 (2017)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Karbasi, S.M., Aubry, A., Carotenuto, V., Naghsh, M.M., Bastani, M.H.: Knowledge-based design of space-time transmit code and receive filter for a multiple-input-multiple-output radar in signal-dependent interference. IET Radar Sonar Navig. 9(8), 1124–1135 (2015)CrossRefGoogle Scholar
  12. 12.
    Aubry, A., De Maio, A., Piezzo, M., Farina, A.: Radar waveform design in a spectrally crowded environment via nonconvex quadratic optimization. IEEE Trans. Aerosp. Electron. Syst. 50(2), 1138–1152 (2014)CrossRefGoogle Scholar
  13. 13.
    Chen, C.Y., Vaidyanathan, P.: MIMO radar waveform optimization with prior information of the extended target and clutter. IEEE Trans. Signal Process. 57(9), 3533–3544 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Chen, P., Wu, L.: System optimization for temporal correlated cognitive radar with EBPSK-based MCPC signal. Math. Probl. Eng. 2015(1), 302083 (2015)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Kerahroodi, M.A., Aubry, A., De Maio, A., Naghsh, M.M.: A coordinate-descent framework to design low PSL/ISL sequences. IEEE Trans. Signal Process. 65(22), 5942–5956 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Bell, M.R.: Information theory and radar waveform design. IEEE Trans. Inf. Theory 39(12), 1578–1597 (1993)CrossRefzbMATHGoogle Scholar
  17. 17.
    Garren, D.A., Odom, A.C., Osborn, M.K., Goldstein, J.S.: Full-polarization matched-illumination for target detection and identification. IEEE Trans. Aerosp. Electron. Syst. 38(3), 824–837 (2002)CrossRefGoogle Scholar
  18. 18.
    Piezzo, M., Aubry, A., Buzzi, S., De Maio, A., Farina, A.: Non-cooperative code design in radar networks: a game-theoretic approach. EURASIP J. Adv. Signal Process. 63(1), 2013 (2013)Google Scholar
  19. 19.
    Deng, X., Qiu, C., Cao, Z., Morelande, M., Moran, B.: Waveform design for enhanced detection of extended target in signal-dependent interference. IET Radar Sonar Navig. 6(1), 30–38 (2012)CrossRefGoogle Scholar
  20. 20.
    Goodman, N.A., Venkata, P.R., Neifeld, M.A.: Adaptive waveform design and sequential hypothesis testing for target recognition with active sensors. IEEE J. Sel. Top. Signal Process. 1(1), 105–213 (2007)CrossRefGoogle Scholar
  21. 21.
    Calderbank, R., Howard, S., Moran, B.: Waveform diversity in radar signal processing. IEEE Signal Process. Mag. 26(1), 32–41 (2009)CrossRefGoogle Scholar
  22. 22.
    Aubry, A., Maio, A.D., Jiang, B., Zhang, S.Z.: Ambiguity function shaping for cognitive radar via complex quartic optimization. IEEE Trans. Signal Process. 61(22), 5603–5619 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Sen, S., Glover, C.W.: Optimal multicarrier phase-coded waveform design for detection of extended targets. In: Proceedings of the IEEE Radar Conference 2013, Ottawa, Canada, pp. 1–2 (2013)Google Scholar
  24. 24.
    Haimovich, A.M., Blum, R.S., Cinimi, L.J.: MIMO radar with widely separated antennas. IEEE Signal Process. Mag. 25(1), 116–129 (2008)CrossRefGoogle Scholar
  25. 25.
    Sen, S., Nehorai, A.: OFDM-MIMO radar with mutual-information waveform design for low-grazing angle tracking. IEEE Trans. Signal Process. 58(6), 3152–3162 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Maio, A.D., Lops, M.: Design principles of MIMO radar detectors. IEEE Trans. Aerosp. Electron. Syst. 43(1), 886–898 (2007)CrossRefGoogle Scholar
  27. 27.
    Karbasi, S.M., Aubry, A., Maio, A.D.: Robust transmit code and receive filter design for extended targets in clutter. IEEE Trans. Signal Process. 63(8), 1965–1976 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Pillai, U., Youla, D.C., Oh, H.S., Guerci, J.R.: Optimum transmit–receiver design in the presence of signal-dependent interference and channel noise. IEEE Trans. Inf. Theory 46(2), 577–584 (2000)CrossRefzbMATHGoogle Scholar
  29. 29.
    Yu, Y., Junhui, Z., Lenan, W.: Adaptive waveform design for MIMO radar-communication transceiver. Sensors 18(6), 1957–1968 (2018)CrossRefGoogle Scholar
  30. 30.
    Aubry, A., Maio, A.D., Piezzo, M., Farina, A., Wicks, M.: Cognitive design of the receive filter and transmitted phase code in reverberating environment. IET Radar Sonar Navig. 6(9), 822–833 (2012)CrossRefGoogle Scholar
  31. 31.
    Sen, S.: PAPR-constrained pareto-optimal waveform design for OFDM-STAP radar. IEEE Trans. Geosci. Remote Sens. 52(6), 3658–3669 (2014)CrossRefGoogle Scholar
  32. 32.
    Luo, Z.Q., Ma, W.K., Anthony, M.C.S., Ye, Y.Y., Zhang, S.Z.: Semidefinite relaxation of quadratic optimization problems. IEEE Signal Process. Mag. 27(3), 20–34 (2010)CrossRefGoogle Scholar
  33. 33.
    Dai, F.Z., Liu, H.W., Wang, P.H., Xia, S.Z.: Adaptive waveform design for range-spread target tracking. Electron. Lett. 46(11), 793–796 (2010)CrossRefGoogle Scholar
  34. 34.
    Yang, Y., Rick, S.B.: MIMO radar waveform design based on mutual information and minimum mean-square error estimation. IEEE Trans. Aerosp. Electron. Syst. 43(1), 330–343 (2007)CrossRefGoogle Scholar
  35. 35.
    Chen, P., Wu, L.: Waveform design for multiple extended targets in temporally correlated cognitive radar system. IET Radar Sonar Navig. 10(1), 398–410 (2015)Google Scholar
  36. 36.
    Cover, T.M., Thomas, J.: Elements of Information Theory. John Wiley & Sons, New York (2006)zbMATHGoogle Scholar
  37. 37.
    Naghibi, T., Behnia, F.: MIMO radar waveform design in the presence of clutter. IEEE Trans. Aerosp. Electron. Syst. 47(2), 770–781 (2011)CrossRefGoogle Scholar
  38. 38.
    Jiu, B., Liu, H., Zhang, L., Wang, Y., Luo, T.: Wideband cognitive radar waveform optimization for joint target radar signature estimation and target detection. IEEE Trans. Aerosp. Electron. Syst. 51(2), 1530–1546 (2015)CrossRefGoogle Scholar
  39. 39.
    Leshem, A., Naparstek, O., Nehorai, A.: Information theoretic adaptive radar waveform design for multiple extended targets. IEEE J. Sel. Top. Signal Process. 1(1), 42–55 (2007)CrossRefGoogle Scholar
  40. 40.
    Boyd, S.P., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  41. 41.
    Aubry, A., Maio, A.D., Foglia, G.: Diffuse multipath exploitation for adaptive radar detection. IEEE Trans. Signal Process. 63(5), 1268–1281 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Aditya, S., Molisch, A.F., Behairy, H.M.: A survey on the impact of multipath on wideband time-of-arrival-based localization. Proc. IEEE 106(7), 1183–1203 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.East China Jiaotong UniversityNanchangChina
  2. 2.Southeast UniversityNanjingChina

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