# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 1 Introduction  
Compared with optical imaging, radar imaging has the ability of all-weather detection, and can effectively detect the morphological characteristics of targets. It has a wide range of application needs in the military and civil fields. Multiple-input multiple-output (MIMO) radar imaging is a new radar imaging technology. By transmitting mutually orthogonal signals and sorting the signals in the receivers, observation channels and degrees of freedom far greater than the actual number of receiving and transmitting array elements can be obtained. Compared with traditional synthetic aperture radar (SAR) imaging and inverse synthetic aperture radar (ISAR) imaging, MIMO radar imaging has significant advantages, such as a high data sampling rate, no complex motion compensation, and easy-to-realize three-dimensional imaging. In recent years, it has attracted extensive attention. Many studies have been performed on imaging models, imaging methods, error correction, and other aspects [1, 2, 3]. Transmitting orthogonal waveforms are the basis for giving full play to the advantages of MIMO radar imaging technology. Through time diversity or frequency diversity, it is easy to achieve the ideal orthogonality between different transmission waveforms, but it will reduce the data rate and spectral efficiency of MIMO radar imaging. At present, transmitting orthogonal waveforms simultaneously with the same frequency can give full play to the advantages of MIMO radar system, which is a research hotspot in the MIMO radar field. However, studies have shown that the ideal orthogonal waveform requires that the spectra of different transmitted signals do not overlap [4], and the commonly used waveforms with the same time and frequency do not meet the above requirements. This will lead to mutual coupling noise among different waveform components and highintegral sidelobe in the result after the matched filter, which will seriously affect the imaging quality of MIMO radar. In order to solve this problem, researchers have successively designed multiple MIMO waveforms from the perspective of waveform design, such as the multi-phase coded signal [5], discrete frequency coded signal [6], zero-correlation area phase coded signal [7], positive and negative linear frequency modulation signal [8], OFDM chirp signal [9], and space-time coded waveform [10]. Although the influence of non-orthogonal waveform coupling can be suppressed to a certain extent through waveform optimization, a completely orthogonal waveform with the same frequency does not exist. Thus, the waveform design cannot completely solve this problem. In addition, from the perspective of signal processing, some scholars have designed receiving filters [11], introduced waveform polarization information [12], and adopted clean technology [13] to improve the image forming effect of non-orthogonal waves, but the actual effect is limited. Sparse recovery or compressed sensing imaging is a new imaging method proposed in recent years, which is different from the matching filter imaging theory. By constructing the linear mapping relationship between the MIMO radar echo signal and the target's one-dimensional range profile [14], two-dimensional profile [15], and three-dimensional profile [16; 17], based on the sparsity of the target image, the sparse optimization algorithm is used to directly reconstruct the high-resolution, low-sidelobe target image, which can avoid the mutual coupling noise generated by the non-orthogonal waveform through matching filtering. However, this type of non-orthogonal waveform MIMO radar imaging, based on sparse recovery, faces problems such as sensitivity to model errors, difficulty in selecting the super-parameters of the optimization algorithm, sparse basis mismatch, and so on, resulting in a poor sparse imaging effect in practice.