access icon free Parameter estimation of micro-motion targets for high-range-resolution radar using high-order difference sequence

Micro-range (m-R) signatures which are induced by micro-motion dynamics can be observed from range profiles, providing that the range resolution of radar is high enough. For real scenarios, micro-motion is often mixed with macro-motion (translation). To extract the micro-motion signatures, it is required to remove the macro-motion component. The widely employed range alignment technique fails for rigid-body targets with micro-motion, since the relative distances between different scattering centres on a rigid-body target are varying and it is unable to obtain a stable reference range profile. Thus, the extracted m-R signatures will be accompanied with residual macro-motion, which may lead to the degradation. However, this issue is often ignored in the research of m-R signatures extraction. In this work, by modelling the motions of scattering centres as the superimposition of a polynomial signal (represents macro-motion) and a sinusoidal signal (represents micro-motion), a micro-motion period estimation method based on high-order difference sequence is proposed. The property that the difference operation can decrease the order of polynomial signals while preserve sinusoidal signals with the same frequency enables the proposed method to extract m-R signatures in the presence of macro-motion. The effectiveness of the proposed method is validated by synthetic and measured radar data.

Inspec keywords: radar resolution; radar signal processing

Other keywords: m-R signatures extraction; micro-motion period estimation method; parameter estimation; high-range-resolution radar; range alignment technique; sinusoidal signal; polynomial signal; high-order difference sequence; rigid-body target

Subjects: Radar equipment, systems and applications; Signal processing and detection

References

    1. 1)
      • 8. Bai, X., Xing, M., Zhou, F., et al: ‘High resolution ISAR imaging of targets with rotating parts’, IEEE Trans. Aerosp. Electron. Syst., 2011, 47, (4), pp. 25302543.
    2. 2)
      • 14. Zhang, W., Li, K., Jiang, W.: ‘Micro-motion frequency estimation of radar targets with complicated translations’, Int. J. Electron. Commun., 2015, 69, pp. 903914.
    3. 3)
      • 1. Dudczyk, J.: ‘Radar emission sources identification based on hierarchical agglomerative clustering for large data sets’, J. Sens., 2016, 2016, pp. 19, Article ID 1879327, doi:10.1155/2016/1879327.
    4. 4)
      • 13. Zhang, W., Li, K., Jiang, W.: ‘Parameter estimation of radar targets with macro-motion and micro-motion based on circular correlation coefficients’, IEEE Signal Process. Lett., 2015, 22, (5), pp. 633637.
    5. 5)
      • 16. Djurovic, I.: ‘Viterbi algorithm for chirp-rate and instantaneous frequency estimation’, Elsevier Signal Process., 2011, 91, pp. 13081314.
    6. 6)
      • 4. Fogle, O.R., Rigling, B.D.: ‘Micro-Range/micro-Doppler decomposition of human radar signatures’, IEEE Trans. Aerosp. Electron. Syst., 2012, 48, (4), pp. 30583072.
    7. 7)
      • 19. Chen, V.C., Lin, C., Pala, W.P.: ‘Time-varying Doppler analysis of electromagnetic backscattering from rotating object’. IEEE Radar Conf., Verona, NY, USA, April 2006, pp. 807812.
    8. 8)
      • 9. Zhou, F., Bai, X., Xing, M., et al: ‘Analysis of wide-angle radar imaging’, IET Radar Sonar Navig., 2011, 5, (4), pp. 449457.
    9. 9)
      • 20. Zhu, D, Li, Y, Zhu, Z.: ‘A keystone transform without interpolation for SAR ground moving-target imaging’, IEEE Geosci. Remote Sens. Lett., 2007, 4, (1), pp. 1822.
    10. 10)
      • 6. Liu, L., Mclernon, D., Ghogho, M., et al: ‘Ballistic missile detection via micro-Doppler frequency estimation from radar return’, Digital Signal Process, 2012, 22, pp. 8795.
    11. 11)
      • 18. Wang, G., Bao, Z.: ‘The minimum entropy criterion of range alignment in ISAR motion compensation’. Proceeding Conf. Radar 97, Edinburgh, UK, October 1997, pp. 1416.
    12. 12)
      • 2. Dudczyk, J, Kawalec, A.: ‘Adaptive forming of the beam pattern of microstrip antenna with the use of an artificial neural network’, Int. J. Antennas Propag., 2012, 2012, pp. 113, Article ID 935073, doi:10.1155/2012/935073.
    13. 13)
      • 15. Peleg, S., Porat, B.: ‘Estimation and classification of polynomial-phase signals’, IEEE Trans. Inf. Theory, 1991, 37, (2), pp. 422430.
    14. 14)
      • 11. Luo, Y., Zhang, Q., Qiu, C., et al: ‘Micro-Doppler effect analysis and feature extraction in ISAR imaging with stepped-frequency chirp signals’, IEEE Trans. Geosci. Rem. Sens., 2010, 48, (4), pp. 20872098.
    15. 15)
      • 3. Chen, V.C., Li, F., Ho, S-S., et al: ‘Micro-Doppler effect in radar: phenomenon, model, and simulation study’, IEEE Trans. Aerosp. Electron. Syst., 2006, 42, (1), pp. 221.
    16. 16)
      • 10. Huo, K., Liu, Y., Hu, J., et al: ‘A novel imaging method for fast rotating targets based on the segmental Pseudo keystone transform’, IEEE Trans. Geosci. Rem. Sens., 2011, 49, (4), pp. 14641472.
    17. 17)
      • 5. Molchanov, P, Totsky, A.: ‘On micro-Doppler period estimation’. 2013 19th Int. Conf. on Control Systems and Computer Science, Bucharest, May 2013, pp. 325330.
    18. 18)
      • 17. Rife, D.C., Boorstyn, R.R.: ‘Single tone parameter estimation from discrete-time observations’, IEEE Trans. Inf. Theory., 1974, IT-20, (5), pp. 591598.
    19. 19)
      • 7. Zhang, Q., Yeo, T.S., Tan, H.S., et al: ‘Imaging of a moving target with rotating parts based on the Hough transform’, IEEE Trans. Geosci. Rem. Sens., 2008, 46, (1), pp. 291299.
    20. 20)
      • 12. Wang, J., Kasilingam, D.: ‘Global range alignment for ISAR’, IEEE Trans. Aerosp. Electron., 2003, 39, (1), pp. 351357.
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