## 3 Methodology
### Two-Dimensional Adaptive Receiving Module
In other words, because we only receive echoes from targets by dechirping, the receiving module only needs to bear the resource consumption of the necessary sub-band and partial-time-length echoes rather than the consumption of the full-bandwidth and full-time-length echoes from the whole scene. The next step is to receive in the azimuth direction. In recent years, GPU has widely shown its strong calculation performance through thousands of parallel threads, which makes it suitable for imaging algorithms such as BP [31]. To facilitate the following signal processing, the module adapts parallel streaming subaperture decomposition receiving in the azimuth direction. Since only the targets are to be imaged, instead of the whole aperture for the entire scene to be received, only a group of the targets' subapertures is received individually. For each subaperture received, to avoid massive memory costs, the receiving storage framework is streaming architecture. For CUDA device computation, the parallel computation is executed by thousands of threads running concurrently. For each thread, a pixel in the image is uniquely related to it, along with the pixel's corresponding data in a different azimuth time. In the streaming architecture, instead of relating to the whole aperture of the imaging scene, the thread corresponds to its own aperture. A circular aperture buffer is used during the azimuth  
\[F_{s}\geq B_{r} \tag{5}\]2 Dehirping receiving for a distributed ship target. receiving phase, and each received echo of the target stream into the buffer by chronological order. Threads form into an image buffer, which is after the aperture buffer and stores and accumulates the processing results of the aperture buffer. Further, the massive storage device follows the image buffer to store the final imaging result. This three-level receiving structure is illustrated in \[F_{s}\geq B_{r} \tag{5}\]3  
The sizes of the two buffers are determined by the aperture's length, the size of targets, and the pixel interval. For the aperture buffer, its size is calculated as  
\[F_{s}\geq B_{r} \tag{5}\]4  
\[F_{s}\geq B_{r} \tag{5}\]5  
where \(L_{a}\) is the synthetic aperture length, \(v\) is the flight speed, \(W_{ag}\) is the target size in the azimuth direction, \(N_{interp}\) is the interpolation ratio in the range direction, and \(\cdot\) is the round up operator. For the image buffer, its size is calculated as  
\[F_{s}\geq B_{r} \tag{5}\]6  
\[F_{s}\geq B_{r} \tag{5}\]7  
where \(d_{az}\) is the image pixel size in the azimuth direction, \(d_{rg}\) is the image pixel size in the range direction, and \(W_{rg}\) is the target size in the range direction. The above receiving process is related to the target's position and size. Thus, the receiving window's position and size are adaptively changed according to the environment. Moreover, the azimuth receiving aperture's length is also related to the necessary azimuth image resolution for target detection. It is adaptively changed from short to long according to the decision module's feedback. \[F_{s}\geq B_{r} \tag{5}\]8 Three-level streaming receiving structure. At the end of this subsection, we conclude by summarizing the receiving hardware resource consumption of the proposed receiving module, as presented in \[F_{s}\geq B_{r} \tag{5}\]9 The comparison with the current receiving module is also included. In Table 1, \(c_{1}\) is the oversampling ratio; \(c_{2}=2N_{interp}/c;c_{3}=N/v\). \(W_{rs}\) and \(W_{ss}\) are the range length and the azimuth length of the whole scene, respectively. \(\alpha_{1}\) is the relative ratio of target's length vs. the whole scene's length in the range direction. \(\alpha_{2}\) and \(\beta\) are the relative ratios of receiving windows' or subaperture's length vs. the whole scene's length in the range direction and the azimuth direction, respectively.