## 1 Introduction  
High-resolution and wide-swath synthetic aperture radar (HRWS SAR) has drawn a lot of attention over the years [1; 2; 3; 4; 5]. The main reason is that it can overcome the minimum antenna area constraint and provide high-resolution images of more extensive areas compared with the traditional SAR [6; 7]. For example, the German Aerospace Center (DRL) plans a mission called High-Resolution Wide-Swath to enhance the performance of the current SAR system, TerraSAR-X. The mission designs a 3 m resolution with 80 km swath coverage for the stripmap mode [8]. Compared with the current TerraSAR-X [9], a 30 km increment is obtained. These prominent advantages make it suitable for applications such as maritime transportation surveillance [8], military target indication [10; 11; 12], etc. Compared with the traditional SAR, the system's pulse repetition frequency (PRF) needs to be lower to receive echoes from the wide swath area. This may result in the under-sampling problem in the azimuth direction, causing ghost images from Doppler ambiguity in the final imaging result [13]. To solve this problem, the HRWS SAR system is designed to deploy multiple receiving channels in the azimuth direction [14]. By receiving echo from multiple channels at the same time, using spatial sampling to replace it, equivalent sampling frequency thus increases multiple times, and undersampling is avoided. Due to these additional receiving channels, new imaging models need to be established, and corresponding imaging algorithms need to be studied. Researchers mainly studied these two aspects over the last decade [3, 4, 15, 16, 17, 18, 19, 20, 21, 22], among which issues related to imaging such as multichannel signal reconstruction [13, 15, 16, 17, 18], amplitude/phase balancing, and motion error compensation [3, 4, 5, 19, 20] were studied the most. In the meantime, along with these developments, multiple new spaceborne and airborne SAR systems have been designed with HRWS imaging capability [2, 8, 21, 22, 23], which again shows its attractive practical value. The previous methods follow the imaging framework in which the imaging result of the whole scene is needed first before detecting targets [24, 25]. For some specific application fields, such as maritime surveillance, the online processing performance is limited due to the following two gaps left by previous approaches. First, when the hardware resources are limited, the design of the imaging framework does not consider it. From the perspective of hardware receiving resources, high resolution means that a high sampling rate is required to sample echo signals with large bandwidth, and wide coverage means a large storage capacity to store echo signals of the whole scene. Although most of them provide little information, the system still needs to receive their echo signals, which increases the consumption of receiving resources. Secondly, when the real-time requirement for software processing is high, the design of the imaging framework does not consider it. From the perspective of computation time, all echoes need to be processed to obtain the whole scene image, increasing computation time consumption. To fill the two gaps, we need to design a new imaging framework related to hardware and software, and the focus should be on the consumption and allocation of resources. Thus, conceptually, we propose a new imaging concept to guide the design of the imaging framework. Unlike the existing whole-scene-oriented imaging framework, the concept is target-oriented: the system's resource allocation is to serve the online target-detection task. Specifically, guided by the concept, we propose an adaptive receiving-processing-decision feedback framework from the aspects of hardware, software, and their combination. To be specific:  
First, to reduce the consumption of receiving hardware resources, we propose a two-dimensional adaptive receiving module, which only receives echoes from targets.