Abstract
The motion of target rotation entitles different Doppler frequencies for scatterers located in cross-range domain. Due to this fact, we can produce an un-scaled target image by using the technique of inverse synthetic aperture imaging (ISA). However, the rotation also smears the image of the target since it can easily cause unwanted range migration and Doppler migration. This paper presents a new ISAR imaging algorithm based on sparse Bayesian Learning by using sparse probing frequency signals, which can easily solve the problem of range migration caused by target rotation. The source causing range migration is theoretically modeled in the mathematical signal model under sparse representation. Then sparse Bayesian learning is applied to automatically learn the sparse coefficients from the original radar data to form the focused and high resolution target image.
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Wang, L., Bi, G., Wang, X. (2020). Flexible Sparse Representation Based Inverse Synthetic Aperture Radar Imaging. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_87
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DOI: https://doi.org/10.1007/978-981-13-9409-6_87
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