# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 4 Experimental Results
### Experiment of Point Target Simulation Data  
Firstly, an aircraft target model composed of ideal scattering points is considered. The imaging parameters are consistent with the training data. We use the method in Section 3.2 to generate the MIMO radar echo and the ideal target image, which is shown in Figure 3. Four existing MIMO radar imaging methods are selected for comparison. (1) The matched filtering method--range compression is carried out based on Equation (4), and FFT is carried out to realize azimuth compression. (2) The 1D-SR method--a one-dimensional sparse recovery (1D-SR) model is constructed by using the method proposed in [16] to directly solve the two-dimensional image of the target. It should be noted that, due to the high dimension of the one-dimensional vector model (in this experiment, the dimension of the perception matrix is up to 11,000 \(\times\) 8000), the efficiency of the sparse solving algorithm in [16], which involves matrix inversion, is too low. Therefore, this paper uses the more efficient orthogonal matching pursuit (OMP) algorithm to solve the sparse model, and the sparsity for OMP is set to 400. (3) The MB-SL0 method--the MB-SL0 algorithm proposed in [14] is used for the range compression, and then the azimuth compression is realized through FFT. (4) The CNN method--we train a CNN network for MIMO radar imaging. The network input is the imaging result of the matched filtering method. The network structure is similar to MIMO-LI-Net, but does not include skip connections between network layers. The network training method is consistent with the training method of the proposed imaging network. The imaging results of the four comparison methods are shown in Figure 4. As can be seen from Figure 4a, due to the non-ideal orthogonality of the transmitted waveform, there is serious mutual coupling noise in the imaging result of the matched filtering method. Figure 4b shows the imaging results of the 1D-SR method. It can be seen that the 1D-SR method can well suppress the mutual coupling noise, but, due to the basis mismatch of the sparse reconstruction model, there is a certain position shift in the scattering points in the sparse reconstructed image compared with the ideal image. Moreover, the sparsity parameter of the OMP algorithm needs to be set manually. When the sparsity is small, the scattering points will be lost, and when the sparsity is large, the computational complexity will increase sharply. As can be seen from Figure 4c, the MB-SL0 method can effectively suppress the range coupling interference caused by the non-orthogonality of the waveform, but the sidelobe is high in the azimuth direction. Moreover, due to the decoupling processing of distance and azimuth, when the scattering points far away in the azimuth undergo migration over distance units, the imaging results will be defocused. Figure 4d shows the imaging results of the CNN method. It can be seen that the CNN method can better suppress the image coupling interference caused by non-orthogonal waveforms. However, due to the lack of targeted design of the network structure, the network has a poor effect on the recovery of weak scattering points in the image. The imaging results of MIMO-LI-Net, trained in this paper, are shown in Figure 5. From the  
Figure 3: Ideal target image. figure, we can see that most scattering points on the target have been well recovered, and the coupling interference between non-orthogonal waveforms has been effectively suppressed. In order to quantitatively analyze the above imaging results, normalized mean square error (NMSE) and target background ratio (TBR) [32] are used as evaluation indicators. In TBR, the threshold is set to 0.0001. The index calculation results of each method are shown in Table 2. It can be seen that the imaging index of the matched filtering method is the worst, and the method in this paper has achieved the best results in NMSE and TBR.