Target-Oriented High-Resolution and Wide-Swath Imaging with an Adaptive Receiving–Processing–Decision Feedback Framework

Autor: Xu Zhan, Xiaoling Zhang, Wensi Zhang, Yuetonghui Xu, Jun Shi, Shunjun Wei, Tianjiao Zeng
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Applied Sciences, Vol 12, Iss 17, p 8922 (2022)
Druh dokumentu: article
ISSN: 2076-3417
DOI: 10.3390/app12178922
Popis: High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a promising technique for applications such as maritime surveillance. In the maritime environment, normally only a few targets such as ships are interested. However, before detecting them, considerable receiving resources and computation time are required to receive the echoes of the whole scene and process them to obtain imaging results. This is a heavy burden for online monitoring on platforms with limited resources. To address these issues, different from the concept of whole-scene-oriented imaging, we propose a target-oriented imaging concept, which is implemented by an adaptive receiving–processing–decision feedback framework with feedback connection. (1) To reduce receiving resource consumption, we propose a two-dimensional adaptive receiving module. It receives sub-band echoes of targets only through dechirping and subaperture decomposition in the range and azimuth directions, respectively. (2) To reduce computation time, we propose a target-oriented processing module. It processes subarea echoes of targets only through parallelly conducting inverse fast Fourier transform (IFFT) and back projection (BP) in the range and azimuth directions, respectively. (3) To allocate resources reasonably, we propose a decision module. It decides the necessary receiving window, bandwidth, and image resolution through constant false alarm rate (CFAR) detection. (4) To allocate resources adaptively, we connect three modules with a closed loop to enable feedback. This enables progressive target imaging and detection from rough to fine. Experimental results verify the feasibility of the proposed framework. Compared with the current one, for a typical scenario, at least 30% of the system’s resources and 99% of computation time are saved.
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