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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
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)
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
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)
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)
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
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
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
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
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
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)
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
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
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
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
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
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
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
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-49263-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49262-4
Online ISBN: 978-3-031-49263-1
eBook Packages: Computer ScienceComputer Science (R0)