No abstract

Video SAR can identify changes in the region of interest and the path of targets through continuous observation of the region of interest and targets irrespective of the weather and day or night. Recently, as the complexity of the region of interest, such as urban areas, has increased, research is being conducted to use drones as payloads for video SAR. The satellite, an existing platform, uses information through multiple antennas, and the distance between the target and the platform is sufficiently far to approximate the speed of the target to detect the moving target. However, the drones cannot approximate the target speed owing to the close distance to the target, and there is a limitation in the payload weight; hence, it is difficult to use multiple antennas. Therefore, this study investigates the process of detecting a moving target using a compressive sensing technique on a short-range platform equipped with a single antenna. Finally, through simulation and real data, it was verified that the video SAR image processing of a moving target based on compression sensing was possible.

Compressive sensing uses the sparsity of signals and the incoherence of sensing matrices. The use of random sensing matrices ensures an easy configuration and a high probability of reconstruction, but there is no optimum algorithm that can avoid the lengthy computation time and high memory consumption burden. Deterministic sensing matrix equations are known to mitigate these problems, and among others, chirp sensing matrices can help to achieve fast data recovery. However, most deterministic sensing matrices suffer from increased internal interference compared with that of random sensing matrix groups, and consequently result in degraded performance. In this paper, we propose a novel compressive sensing reconstruction method that enables the acquisition of excellent sparse signal reconstruction performance of existing random sensing matrices and signal processing acceleration performance through deterministic sensing matrices. Accordingly, we propose a method that contributes to the increase in the vast amount of data that has been a chronic problem with SAR(Synthetic Aperture Radar) images and the acceleration of the processing speed.