# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 1 Introduction
In addition, in the case of high-resolution and high-dimensional imaging, the sparse optimization solution process has high computational complexity and a large demand for data sampling and storage, which is difficult to meet the needs of real-time and low-cost imaging. In recent years, deep learning technology based on big data has attracted extensive attention. It can automatically mine hidden structural information and internal laws from data, and has strong nonlinear mapping ability. In practical applications, only one forward propagation of the network is needed, and the processing efficiency is very high. The network parameters are automatically learned through training without manual intervention. In view of the above advantages, in recent years, deep learning technology has also been introduced into the field of radar imaging and has been developed rapidly [18; 19]. A complex valued convolutional neural network (CNN) for sparse ISAR image enhancement is constructed in [20]. In [21], the authors further construct a full convolution neural network to improve the imaging quality of sparse ISAR. In [22], the complex CNN is applied to radar image enhancement, and the ideal point scattering center model is used to build data sets to train the complex CNN, which improves the resolution of ISAR images. For the same purpose, a deep residual network for ISAR low-resolution image enhancement is further constructed in [23]. The authors in [24] introduce the generation model into ISAR imaging, and enhance the recovery effect of weak scattering points with the help of the generation of a countermeasure network. In addition to the above image enhancement methods, some researchers use the traditional sparse reconstruction algorithm for deep expansion for radar imaging. In [25], an iterative soft threshold algorithm is used to expand the network for ISAR imaging. The network combines CNN with the sparse reconstruction process to improve the effect of ISAR imaging. In [26], the authors propose a general method for constructing a layered sparse ISAR imaging network, which uses mixed channels to process real and imaginary data, respectively, which is more suitable for complex echo processing. In [27; 28], ISAR self-focusing imaging is realized by adding initial phase compensation operation in the sub-network. In [29], the 2D-ADMM is used to expand the network to realize ISAR self-focusing imaging under two-dimensional sparse conditions. In addition to the research on ISAR imaging, the authors in [30; 31] have studied deep expansion networks suitable for 2D SAR sparse imaging and 3D SAR sparse imaging,respectively. In [32], the authors attempt to combine the learning imaging method based on deep expansion with the image domain enhancement method to improve the adaptability of the imaging network to model errors. We can see that, in recent years, deep learning technology has made great research progress in the field of ISAR and SAR imaging, which can improve the imaging effect of ISAR and SAR to a certain extent. However, there is no public report on non-orthogonal waveform MIMO radar imaging. In this paper, a non-ideal orthogonal waveform MIMO radar imaging method based on deep learning is proposed. With the help of the powerful nonlinear fitting ability of deep learning, the mapping relationship between the non-orthogonal waveform MIMO radar echo and the ideal target image is automatically learned by constructing the MIMO radar deep imaging network and training on a large amount of simulation training data. The learned imaging network can effectively suppress the coupling interference between non-ideal orthogonal transmission waveforms and improve the imaging quality of MIMO radar. The differences between the proposed method and the existing methods of suppressing the mutual coupling among different waveforms are summarized in Table 1. The effectiveness and timeliness of the proposed method are verified by experiments on point scattering model data and electromagnetic scattering calculation data.