access icon openaccess Fast estimation of high-order motion parameters for real-time ISAR imaging

This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Received 01/06/2019, Accepted 15/07/2019, Published 23/10/2019

The inverse synthetic aperture radar (ISAR) technique is an important tool for target recognition and classification; thus, the high-quality and real-time performance are two essential indicators for ISAR imaging. Based on the classical range–Doppler principle, the motion compensation is the prerequisite step for the subsequent imaging processing. Due to the non-cooperative characteristic of the target, the unknown moving parameters are required to be well estimated. Generally, the translational motion of the target can be accurately described by two-order dynamic parameters. However, the most parameter estimation methods can only estimate the one-order parameter, while the common high-order estimation methods require priori knowledge and are complex to implement. Aiming at this issue, the authors propose a high-order symmetric accumulated cross-correlation method to realise the rapid and accurate estimation of the motion parameters with no requirement of priori knowledge. It takes the advantage of the symmetric accumulation manner to offset the phase errors and optimise the computational complexity simultaneously, and then formulates the estimation to solve the least-square problem. Experimental results verify that the proposed method shows distinct advantages on achieving the high-accuracy and low-complexity parameter estimation, which is highly conductive to realise the high-quality motion compensation for real-time ISAR imaging.

Inspec keywords: correlation methods; radar imaging; least squares approximations; motion compensation; computational complexity; parameter estimation; synthetic aperture radar

Other keywords: high-order motion parameters; rapid estimation; high-order symmetric accumulated cross-correlation method; priori knowledge; essential indicators; parameter estimation methods; target recognition; real-time performance; low-complexity parameter estimation; symmetric accumulation manner; classical range–Doppler principle; common high-order estimation methods; one-order parameter; prerequisite step; classification; real-time ISAR imaging; noncooperative characteristic; unknown moving parameters; two-order dynamic parameters; subsequent imaging processing; translational motion; inverse synthetic aperture radar technique; high-quality motion compensation

Subjects: Optical, image and video signal processing; Computer vision and image processing techniques; Radar equipment, systems and applications

References

    1. 1)
      • 5. Musman, S., Kerr, D., Bachmann, C.: ‘Automatic recognition of ISAR ship images’, IEEE Trans. Aerosp. Electron. Syst., 1996, 32, (4), pp. 1392–1404.
    2. 2)
      • 8. Walker, J.L.: ‘Range-Doppler imaging of rotating objects’, IEEE Trans. Aerosp. Electron. Syst., 1980, 16, (1), pp. 23–52.
    3. 3)
      • 26. Wang, G.Y., Bao, Z.: ‘The minimum entropy criterion of range alignment in ISAR motion compensation’. Proc. Radar Conf., Edinburgh, UK, October 1997, pp. 236–239.
    4. 4)
      • 13. Zhu, D, Wang, L., Yu, Y., et al: ‘Robust ISAR range alignment via minimizing the entropy of the average range profile’, IEEE Geosci. Remote Sens. Lett., 2009, 6, (2), pp. 204–208.
    5. 5)
      • 27. Peters, G., Wilkinson, J.H.: ‘The least squares problem and pseudo-inverses’, Comput. J., 1970, 13, (3), pp. 309–316.
    6. 6)
      • 18. Cho, J.S., Kim, D.J., Park, D.J.: ‘Robust centroid target tracker based on new distance features in cluttered image sequences’, IEICT Trans. Inf. Syst., 2000, E83-D, (12), pp. 2141–2151.
    7. 7)
      • 7. Lee, S.-J., Lee, M.-J., Kim, K.-T., et al: ‘Classification of ISAR images using variable cross-range resolutions’, IEEE Trans. Aerosp. Electron. Syst., 2018, 54, (5), pp. 2291–2303.
    8. 8)
      • 11. Wang, Y., Ling, H., Chen, V.C.: ‘ISAR motion compensation via adaptive joint time-frequency technique’, IEEE Trans. Aerosp. Electron. Syst., 1998, 34, (2), pp. 670–677.
    9. 9)
      • 12. Zhang, W., Fu, Y., Nie, L., et al: ‘Parameter estimation of micro-motion targets for high-range-resolution radar using high-order difference sequence’, IET Signal Process., 2018, 12, (1), pp. 1–11.
    10. 10)
      • 10. Wu, H., Grenier, D., Deisle, G.Y., et al: ‘Translational motion compensation in ISAR image processing’, IEEE Trans. Aerosp. Electron. Syst., 1995, 4, (11), pp. 1561–1571.
    11. 11)
      • 14. Liu, L., Zhou, F., Tao, M., et al: ‘Adaptive translational motion compensation method for ISAR imaging under low SNR based on particle swarm optimization’, IEEE J. Select. Topics Appl. Earth Obs. Remote Sens., 2015, 8, (11), pp. 5146–5157.
    12. 12)
      • 4. Yu, X., Zhu, D.Y., Zhang, J.D., et al: ‘Motion compensation algorithm based on the designing structured gram matrices method’, IET Radar Sonar Navig.., 2014, 8, (3), pp. 209–219.
    13. 13)
      • 9. Xue, J., Zhang, Q., Wang, Y., et al: ‘PWFT-RD: a high-quality and low-complexity ISAR imaging approach for maneuvering target’, IEEE. Access., 2018, 6, pp. 71168–71180.
    14. 14)
      • 22. Xue, J., Han, X., Zhang, Q.: ‘A symmetric accumulated cross-correlation method of parameter estimation based on fractional Fourier transform for ISAR motion compensation’. Proc. Int. Conf. Wireless Communication and Signal Processing (WCSP), Nanjing, China, October 2017, pp. 1–6.
    15. 15)
      • 17. Itoh, T., Sueda, H., Watanabe, Y.: ‘Motion compensation for ISAR via centroid tracking’, IEEE Trans. Aerosp. Electron. Syst., 1996, 32, (3), pp. 1191–1197.
    16. 16)
      • 20. Chen, C.C., Andrews, H.C.: ‘Target-motion-induced radar imaging’, IEEE Trans. Aerosp. Electron. Syst., 1980, 16, (3), pp. 2–14.
    17. 17)
      • 2. Ausherman, D.A., Kozma, A., Walker, J.L.: ‘Developments in radar imaging’, IEEE Trans. Aerosp. Electron. Syst., 1984, 20, (4), pp. 363–399.
    18. 18)
      • 28. Qian, C., Huang, L., So, H.C., et al: ‘Unitary PUMA algorithm for estimating the frequency of a complex sinusoid’, IEEE Trans. Signal Process., 2015, 63, (20), pp. 5358–5368.
    19. 19)
      • 19. Wang, J.F., Liu, X.Z.: ‘Improved global range alignment for ISAR’, IEEE Trans. Aerosp. Electron. Syst., 2007, 43, (3), pp. 1072–1075.
    20. 20)
      • 25. Wang, R., Zeng, T., Hu, C., et al: ‘Accurate range profile alignment method based on minimum entropy for inverse synthetic aperture radar image formation’, IET Radar Sonar Navig., 2016, 10, (4), pp. 663–671.
    21. 21)
      • 30. Liu, Y.T.: ‘ISAR signal processing’, in ‘Radar imaging technology’ (Technology Press, Harbin, China, 2014, 1st edn.), pp. 207–208.
    22. 22)
      • 21. Delisle, G.Y., Wu, H.Q.: ‘Moving target imaging and trajectory computation using ISAR’, IEEE Trans. Aerosp. Electron. Syst., 1994, 30, (3), pp. 887–899.
    23. 23)
      • 6. Kim, K.-T., Kim, H.-T.: ‘Efficient classification of ISAR images’, IEEE Trans. Antenn. Propag., 2005, 53, (5), pp. 1611–1621.
    24. 24)
      • 1. Prickett, J., Chen, C.C.: ‘Principles of inverse synthetic aperture radar (ISAR) imaging’. Proc. IEEE EASCON Record, Arlington, VA, USA, 29 September–1 October1980, pp. 340–345.
    25. 25)
      • 29. Ozdemir, C.: ‘Motion compensation for inverse synthetic aperture radar’, in ‘Inverse synthetic aperture radar imaging with MATLAB algorithms’ (John Wiley and Sons, Inc. Publication, New Jersey, USA, 2012), pp. 306–310.
    26. 26)
      • 31. Cleveland, W.S.: ‘Robust locally weighted regression and smoothing scatterplots’, J. Am. Statist. Assoc., 1979, 74, pp. 829–836.
    27. 27)
      • 23. Shin, S.Y., Myung, N.H.: ‘The application of motion compensation of ISAR image for a moving target in radar target recognition’, Microw. Opt. Tech. Lett., 2008, 50, (6), pp. 1673–1678.
    28. 28)
      • 15. Fan, H., Ren, L., Mao, E., et al: ‘A high-precision method of phase-derived velocity measurement and its application in motion compensation of ISAR imaging’, IEEE Trans. Geosci. Remote Sens., 2018, 56, (1), pp. 60–77.
    29. 29)
      • 24. Zhang, L., Sheng, J., Duan, J., et al: ‘Translational motion compensation for ISAR imaging under low SNR by minimum entropy’, EURASIP J. Adv. Signal Process., 2013, 2013, (33), pp. 1–19.
    30. 30)
      • 16. Wang, Y., Bao, Z.: ‘Inverse synthetic aperture radar imaging of nonuniformly rotating target based on the parameters estimation of multicomponent quadratic frequency-modulated signals’, IEEE Sens. J., 2015, 15, (7), pp. 4053–4061.
    31. 31)
      • 3. Wiley, C.A., Kozma, A., Walker, J.L.: ‘Synthetic aperture radars – a paradigm for technology evolution’, IEEE Trans. Aerosp. Electron. Syst., 1985, 21, pp. 300–309.
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