Abstract
Circular Synthetic Aperture Radar (CSAR) has attracted much attention in the field of high-resolution SAR imaging. In order to shorten the computation time and improve the imaging effect, in this paper, we propose a fast CSAR imaging strategy that searches the target and automatically selects the area of interest for imaging. The first step is to find the target and select the imaging center and interest imaging area based on the target search algorithm, the second step is to divide the full-aperture data into sub-apertures according to the angle, the third step is to approximate the sub-apertures as linear arrays and imaging them separately, and the last step is to perform sub-image fusion to obtain the final CSAR image. This method can greatly reduce the imaging time and obtain well-focused CSAR images. The proposed algorithm is verified by both simulation and processing real data collected with our mmWave imager prototype utilizing commercially available 77-GHz MIMO radar sensors. Through the experimental results we verified the performance and the superiority of the our algorithm.
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References
Knaell, K.K., Cardillo, G.P.: Radar tomography for the generation of three-dimensional images. IEE Proc. Radar Sonar Navig. 142(2), 54–60 (1995)
Soumekh, M.: Reconnaissance with slant plane circular SAR imaging. IEEE Trans. Image Process. 5(8), 1252–1265 (1996)
Chen, L., An, D., Huang, X.: A backprojection-based imaging for circular synthetic aperture radar. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(8), 3547–3555 (2017)
Ponce, O., Prats-Iraola, P., Pinheiro, M., et al.: Fully polarimetric high-resolution 3-D imaging with circular SAR at L-band. IEEE Trans. Geosci. Remote Sens. 52(6), 3074–3090 (2013)
Ponce, O., Prats-Iraola, P., Scheiber, R., et al.: First airborne demonstration of holographic SAR tomography with fully polarimetric multicircular acquisitions at L-band. IEEE Trans. Geosci. Remote Sens. 54(10), 6170–6196 (2016)
Gianelli, C.D., Xu, L.: Focusing, imaging, and ATR for the Gotcha 2008 wide angle SAR collection. In: Algorithms for Synthetic Aperture Radar Imagery XX, vol. 8746, pp. 174–181. SPIE (2013). https://doi.org/10.1117/12.2015773
Saville, M.A., Jackson, J.A., Fuller, D.F.: Rethinking vehicle classification with wide-angle polarimetric SAR. IEEE Aerosp. Electron. Syst. Mag. 29(1), 41–49 (2014)
Frolind, P.O., Gustavsson, A., Lundberg, M., et al.: Circular-aperture VHF-band synthetic aperture radar for detection of vehicles in forest concealment. IEEE Trans. Geosci. Remote Sens. 50(4), 1329–1339 (2011)
Cantalloube, H.M.J., Colin-Koeniguer, E., Oriot H.: High resolution SAR imaging along circular trajectories. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 850–853. IEEE (2007). https://doi.org/10.1109/IGARSS.2007.4422930
Dupuis, X., Martineau, P.: Very high resolution circular SAR imaging at X band. In: 2014 IEEE Geoscience and Remote Sensing Symposium, pp. 930–933. IEEE (2014). https://doi.org/10.1109/IGARSS.2014.6946578
Lin, Y., Hong, W., Tan, W., et al.: Extension of range migration algorithm to squint circular SAR imaging. IEEE Geosci. Remote Sens. Lett. 8(4), 651–655 (2011)
Chen, L., An, D., Huang, X., et al.: P-band ultra wideband circular synthetic aperture radar experiment and imaging. In: 2016 CIE International Conference on Radar (RADAR), pp. 1–3. IEEE (2016). https://doi.org/10.1109/RADAR.2016.8059352
Hao, J., Li, J., Pi, Y.: Three-dimensional imaging of terahertz circular SAR with sparse linear array. Sensors 18(8), 2477 (2018)
Ao, D., Wang, R., Hu, C., et al.: A sparse SAR imaging method based on multiple measurement vectors model. Remote Sens. 9(3), 297 (2017)
Liu, T., Pi, Y., Yang, X.: Wide-angle CSAR imaging based on the adaptive subaperture partition method in the terahertz band. IEEE Trans. Terahertz Sci. Technol. 8(2), 165–173 (2017)
Zheng, Y., Cui, X., Wu, G., et al.: Polarimetric CSAR image quality enhancement using joint sub-aperture processing. In: 2022 7th International Conference on Signal and Image Processing (ICSIP), pp. 458–462. IEEE (2022). https://doi.org/10.1109/ICSIP55141.2022.9886812
Chu, L., Ma, Y., Yang, S., et al.: Imaging algorithm for circular SAR based on geometric constraints. In: 2022 International Conference on Computer Network, Electronic and Automation (ICCNEA), pp. 303–306. IEEE (2022). https://doi.org/10.1109/ICCNEA57056.2022.00073
Lou, Y., Liu, W., Xing, M., et al.: A novel motion compensation method applicable to ground cartesian back-projection algorithm for airborne circular SAR. IEEE Trans. Geosci. Remote Sens. (2023). https://doi.org/10.1109/TGRS.2023.3276051
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This work was supported by the National Natural Science Foundation of China (61873213).
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Deng, Y., Li, C., Shi, Y., Wang, H., Li, H. (2024). An Improved Target Searching and Imaging Method for CSAR. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_8
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