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ISAR Imaging Algorithm Based on Fourier Dictionary Signal Decomposition and L0 Norm Minimization

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Telecommunications and Remote Sensing (ICTRS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1990))

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Abstract

In In the present work an Inverse Synthetic Aperture Radar (ISAR) image reconstruction algorithm based on Fourier Dictionary signal decomposition and l0 norm minimization of the image matrix is suggested. Three-dimensional (3-D) ISAR geometry and kinematics are analytically described. An ISAR signal model based on Stepped Frequency Modulation (SFM) waveform is derived. An original steepest ascent algorithm is created with a Gaussian function increasing maximal pixel intensities and decreasing pixel intensities of low level. Simulation experiments prove the effectiveness of the developed algorithm.

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References

  1. Wang, B., et al.: An iterative phase autofocus approach for ISAR imaging of maneuvering targets. Electronics 10(17), 2100 (2021). https://doi.org/10.3390/electronics10172100

    Article  Google Scholar 

  2. Wei, X., Yang, J., Lv, M., Chen, W., Ma, X.: Translational mtion compensation for ISAR imaging based on range joint fast orthogonal matching pursuit algorithm. IEEE Access 10(2022), 37382–37395 (2022)

    Article  Google Scholar 

  3. Wei, J., Shao, S., Ma, H., Wang, P., Zhang, L., Liu, H.: High-resolution ISAR imaging with modified joint range spatial-variant autofocus and azimuth scaling, 2020. Sensors 20(18), 5047 (2020). https://doi.org/10.3390/s20185047

    Article  Google Scholar 

  4. Zhang, L., Duan, J., Qiao, Z.-J., Xing, M.-D., Bao, Z.: Phase adjustment and ISAR imaging of maneuvering targets with sparse apertures. China IEEE Trans. AES 50(3) (2014)

    Google Scholar 

  5. Wei, X., Yang, J., Lv, M., Chen, W., Ma, X.: A reweighted alternating direction method of multipliers for joint phase adjustment and ISAR imaging. Remote Sens. Lett. 13, 1020–1028 (2022)

    Article  Google Scholar 

  6. Liu, F., Huang, D., Guo, X., Feng, C.: Translational Motion compensation for maneuvering target echoes with sparse aperture based on dimension compressed optimization. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2022). https://doi.org/10.1109/TGRS.2022.3169275

    Article  Google Scholar 

  7. Bae, J.-H., Kim, K., Yang, E.: A study on the formulation of high resolution range profile and ISAR image using sparse recovery algorithm. J. Korean Inst. Electromagn. Eng. Sci. 25(4), 467–475 (2014). https://doi.org/10.5515/KJKIEES.2014.25.4.467

  8. Bae, J.-H., Kang, B.-S., Kim, K.-T., Yang, E.: Performance of sparse recovery algorithms for the reconstruction of radar images from incomplete RCS data. IEEE Geosci. Remote Sens. Lett. 12(4), 860–864 (2015). https://doi.org/10.1109/LGRS.2014.2364601

  9. Lee, S.-J., Bae, J.-H., Kang, B.-S., Kim, K.-T., Yang, E.-J.: Classification of ISAR images using sparse recovery algorithms. In: 2014 IEEE Conference on Antenna Measurements and Applications (CAMA), Antibes Juan-les-Pins, France, pp. 1–4 (2014). https://doi.org/10.1109/CAMA.2014.7003316

  10. Kang, M.-S., Kim, K.-T.: Compressive sensing approach for high-resolution ISAR image reconstruction and autofocus, Wiley, October 2019. J. Eng. 6 (2019). https://doi.org/10.1049/joe.2019.0572

  11. Rosebrock, F., Rosebrock, J., Cerutti-Maori, D., Ender, J.: ISAR imaging by integrated compressed sensing, range alignment and autofocus. In: EUSAR 2021; 13th European Conference on Synthetic Aperture Radar, Online, pp. 1–5 (2021)

    Google Scholar 

  12. Xing, S., Song, S., Quan, S., Sun, D., Wang, J., Li, Y.: Near-field 3D sparse SAR direct imaging with irregular samples. Remote Sens. (14), 246321 (2022). https://doi.org/10.3390/rs14246321

  13. Liu, F., Huang, D., Cunqian Feng, C.: Translational motion compensation for maneuvering target echoes with sparse aperture based on dimension compressed optimization, Published 2022. IEEE Trans. Geosci. Remote Sens. Eng. (2022). https://doi.org/10.1109/TGRS.2022.3169275

    Article  Google Scholar 

  14. Zhang, L., Wang, H., Qiao, Z.-J.: Resolution enhancement for ISAR imaging via improved statistical compressive sensing. EURASIP J. Adv. Sig. Process. 2016, 80 (2016). https://doi.org/10.1186/s13634-016-0379-2

    Article  Google Scholar 

  15. Zhang, L., Wang, H., Qiao, Z.-J.: Compensation of phase errors for compressed sensing based ISAR imagery using inadequate pulses. Progr. Electromagn. Res. M 41, 125–138 (2015). https://doi.org/10.2528/PIERM14120402, http://www.jpier.org/PIERM/pier.php?paper=14120402

  16. Jiang, Y., Li, Y.: Algorithm-oriented SIMD computer mathematical model and its application. Int. J. Inf. Commun. Technol. Educ. Off. Publication Inf. Resourc. Manag. Assoc. IGI Global 18(3), 1–18 (2022). https://doi.org/10.4018/IJICTE.315743

    Article  Google Scholar 

  17. Ghaffari, A., Babaie-Zadeh, M., Jutten, C.: Sparse decomposition of two dimensional signals. In: ICASSP 2009 - IEEE International Conference on Acoustics, Speech and Signal Processing, April 2009, Taipei, Taiwan. pp. 3157–3160 (2009). ffhal-00400464f

    Google Scholar 

  18. Qiu, W., et al.: Compressive sensing–based algorithm for passive bistatic ISAR with DVB-T signals. IEEE Trans. Aerosp. Electron. Syst. 51(3), 2166–2180 (2015). https://doi.org/10.1109/TAES.2015.130761

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Acknowledgment

This research was funded by Bulgarian National Science Fund (BNSF), under project grant number KP-06-N57/6, entitled “Theoretical and experimental research of models and algorithms for formation and control of specific relief textures on different types of functional surfaces”. This research is also supported by National Scientific Program – Security and Defence adopted by Decision of the Council of Ministers No. 731 of October 21, 2021.

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Correspondence to Andon Lazarov .

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Lazarov, A., Minchev, D. (2023). ISAR Imaging Algorithm Based on Fourier Dictionary Signal Decomposition and L0 Norm Minimization. In: Shishkov, B., Lazarov, A. (eds) Telecommunications and Remote Sensing. ICTRS 2023. Communications in Computer and Information Science, vol 1990. Springer, Cham. https://doi.org/10.1007/978-3-031-49263-1_1

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  • DOI: https://doi.org/10.1007/978-3-031-49263-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49262-4

  • Online ISBN: 978-3-031-49263-1

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