## 1 Introduction
These include the matrix inversion method [6; 7; 8; 9], the orthogonal projection method [9], the maximum signal method [9], the maximum signal-to-ambiguity-plus-noise ratio (SANR) method [9], the minimum mean-square error (MMSE) method [30], and the improved digital beamforming (IDBF) method [10]. In addition, in [17], the GMTI function is implemented by extending the MCRA to the case of imaging moving targets. A method for simultaneously imaging moving and stationary targets is presented in [31]. This paper focuses on the imaging of stationary targets. When ViSAR is operated on a circular path, the azimuth and the elevation beamwidth are selected to be of similar magnitudes for efficient observation of targets [23]. In this case, the beamwidths can be widened, depending on the requirements of the specific scenario. In the case of a wide beam, it is difficult to apply the PFA, due to the scene size limitation associated with this algorithm. The BPA is advantageous for generating SAR image frames at high frame rates in an overlapping fashion due to its parallel nature. Therefore, the BPA is often applied to ViSAR. However, this algorithm is not suitable for synthesizing SAR images in real time, on small platforms such as UAV, due to its huge computational burden and large memory usage. Conversely, the PFA can be applied when using a narrow beam. In this case, a SAR video can be generated by continuously forming the SAR images from a sub-aperture that is a part of the full circular aperture that has been selected to satisfy the scene size limitation of the PFA. The PFA, which was developed by [PERSON] [32], is the classical algorithm proposed for raw data processing of spotlight SAR [2]. Although the PFA is significantly faster than the BPA, it approximates a matched filter response, thereby causing image errors in large scenes. Due to these errors, the PFA has a scene size limitation. Since the PFA can be implemented easily for a circular and a linear path, it is possible to operate the SAR system more flexibly. Therefore, video from the proposed MIMO ViSAR system is formed using the PFA. In this paper, we derive a signal model based on the MIMO radar signal model, and propose a novel signal processing method for ViSAR systems suitable for UAV environment operation, which improves aspects of the system performance, such as frame rate, and image size of the SARvideo. The proposed ViSAR system focuses on a sub-aperture synthesis method. The new ViSAR system is based on a FMCW SAR structure that is combined with MIMO and multi-channel azimuth processing techniques, to make use of the characteristics listed above. MIMO technology is utilized for increasing the equivalent number of receiving channels in the azimuthal direction, and reducing the aperture size using a co-array concept with a BFD FMCW. A multi-channel azimuth processing technique is utilized for improving the frame rate and image size of the SAR video, by suppressing the azimuth ambiguities in the receiving channels. This paper also provides analyses of the frame rate and image size of SAR video. The performance of the proposed system is evaluated using an exemplary system comprised of two Tx and two receiving (Rx) channels. The results of analyses are presented, and their validity is verified using numerical simulations. This paper consists of five sections and is organized as follows. We start with an overview of ViSAR systems and analysis of the frame rate and image size in Section 2. In Section 3, we describe the MIMO signal model and MIMO video SAR processing steps. In Section 4, an exemplary system is designed and the numerical simulation results and performance estimation are given. Finally, conclusions and future opportunities are presented in Section 5.