## 5 Conclusions  
In this paper, a new target-oriented imaging concept for HRWS SAR imaging is proposed, including the realization framework. For applications such as maritime surveillance, we utilize the strong sparsity of the imaging scene to design the imaging framework, which aims to reduce the consumption of receiving resources and computation time for online surveillance.  
The proposed adaptive receiving-processing-decision framework with feedback connection is inspired by the recent cognitive radar concept [35]. Thus, besides reducing unnecessary resource costs in the area with little information, we adaptively adjust the allocation of resources according to the perception result through the proposed decision module in the framework. Then, from the perspectives of hardware, we design a 2D receiving module to reduce the consumption from the receiving hardware system in terms of sampling frequency and sampling length; from the perspectives of software, the target-oriented processing module is proposed to reduce the computation time from processing in terms of both effective imaging area and imaging resolution. The concept and the framework are verified through numerical simulations, showing promising performance on both consumption reduction of receiving resources and computation time.  
Currently, this paper conducts preliminary study about this concept and framework. More complex scenes are needed to consider. The resource allocation and dynamic schedule related to the GPU device both need to be studied further. Furthermore, for more efficient decisions to be fed back, target-detection methods utilizing deep learning techniques may be integrated into this framework [25; 36].  
Conceptualization, X.Z. ([PERSON]); methodology, X.Z. ([PERSON]); software, W.Z.; validation, Y.X.; form analysis, J.S.; investigation, S.W.; resources, T.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 17: Detection imaging result of the proposed framework.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **The Current One** & **The One with CFBP** & \
\begin{tabular}{c} **Relative** \\ **Reduced Proportion** \\ \end{tabular} & **The Proposed One** &
\begin{tabular}{c} **Relative Reduced** \\ **Proportion** \\ \end{tabular} \\ \hline Range sampling points & 6000 points & 600 points & **0\%**\(\downarrow\) & 2400 points & **60\%**\(\downarrow\) \\ Range sampling frequency & 180 MHz & 180 MHz & **0\%**\(\downarrow\) & 120 MHz & **33.3\%**\(\downarrow\) \\ Azimuth sampling points 1 & 12,500 points & 12,500 points & **0\%**\(\downarrow\) & 8750 points & **30.0\%**\(\downarrow\) \\ Computation time & 7480 s & 2412.9 s & 67.7\%\(\downarrow\) & 63.64 s & **99.1\%**\(\downarrow\) \\ \hline \hline \end{tabular}  
* As the areas of all ships are within the same minimum receiving window and aperture in the range and azimuth directions that cannot be reduced, the relative reduced proportions of receiving resources are less than the point-like target simulation.  
\end{table}
Table 5: Comparison results for the typical scenario.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 61571099. And the APC was funded by National Natural Science Foundation of China, grant number 61571099.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.