# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 3 Imaging Method of Non-Orthogonal Waveform MIMO Radar Based on Deep Learning
### Deep Network Structure  
According to the specific characteristics of non-orthogonal waveform MIMO radar imaging, this paper constructs a MIMO radar learning imaging network (MIMO-LI-Net), and the network structure is shown in Figure 2. The echo transformation module is constructed according to the range Doppler imaging algorithm, including the matched filter network and the azimuth compression network: the matched filter network completes the range dimension compression of the target echo by inputting the non-orthogonal emission waveform set. According to the configuration of the MIMO array structure, the azimuth compression network completes the two-dimensional compression of the target echo through echo rearrangement, azimuth compression, and other processes. In the specific implementation, this paper designs a uniform equivalent transceiver array and frequency domain matched filter, which can improve the efficiency of echo transformation with the help of fast Fourier transform (FFT). It should be noted that, due to the coupling interference between non-orthogonal waveforms and the possible migration though resolution cells, the above two-dimensional compression results cannot achieve the ideal imaging effect. Nevertheless, the echo transformation process still extracts effective target features from the complex echo of MIMO radar, which can provide a good initial input for the subsequent feature imaging process. The feature imaging module is constructed by a convolutional neural network, which can be divided into two parts, encoding and decoding, as shown in Figure 2. In the figure, Conv and ConvTrans represent the convolution layer and deconvolution layer, respectively. Moreover, (3 \(\times\) 3, 1) indicates that the size of the convolution kernel or deconvolution kernel is 3 \(\times\) 3 and the step length is 1. After each convolution operation or deconvolution operation, a batch normalization (BN) process and a relu activation function are connected. The network consists of five convolution layers, and the number of input/output channels of each layer is 2/32, 32/64, 64/64, 64/64, and 64/128, respectively. It includes five deconvolution layers, and the number of input/output channels of each layer is 128/64, 64/64, 64/64, 64/32, and 32/1, respectively. MaxPool and MaxUnpool represent the maximum pooling layer and the maximum unpooling layer, respectively, and (2 \(\times\) 2, 2) indicates that the size of the pooled or unpooled window is 2 \(\times\) 2 and the window step is 2. The same location index is used for the maximum pooling layer and the maximum unpooling layer. The network includes 4 pooling layers and 4 unpooling layers, respectively. Different from general natural image processing, we send the real part and imaginary part of the echo transformation result to the CNN network as two separate channels. Compared with the input single-channel modulus data, the inputted dual-channel data contain the complete information of the transformed echo, which is conducive to image feature extraction. In addition, the feature imaging module establishes a skip connection between the coding and decoding parts of the network, which helps to retain the details of the image in the process of image reconstruction.  
Figure 1: Non-orthogonal waveform MIMO radar imaging framework based on deep network.