# Application of Machine Learning in Electromagnetics: Mini-Review
## 3 ML and EM
### Remote Object Detection and Recognition  
ML has been applied in the analysis and prediction of information from radar signals. Synthetic Aperture Radar (SAR) is a microwave tool for the detection and recognition of targets as well as for the analysis of natural and man-made scenes. It has the benefits of all-weather, day-and-night operation with high-resolution imaging capability. Recently, CNNs have been used for SAR target recognition (TR) and could achieve high precision in image recognition and classification. However, the shortage of data still makes SAR-based target recognition difficult. An earlier report [106] utilized CNN with three types of data augmentation for issues in SAR-TR, such as the translation of a target, the randomness of speckle noise in different observations, and the lack of pose images in training data. To overcome the over-fitting problem of CNN for SAR-TR, [PERSON] et al. [107] proposed a new all-convolutional network (A-ConvNets) based on sparsely connected layers. The experi  
Figure 6: Detection network diagram and DoA (direction of arrival) estimation algorithm using DNN (Deep Neural Network) model [93].  
mental results showed that, using data-driven features the model learned automatically from SAR image pixels, A-ConvNets achieves excellent classification accuracy.  
[PERSON] et al. [108] used a transfer learning approach where a pre-trained CNN, namely AlexNet, was used as a feature extractor. Moreover, [PERSON] et al. [109] proposed a Multiview deep convolutional neural network (DCNN), which is an implementation of deep learning architecture, to recognize targets using multiple viewing angles. The framework was seen to effectively learn and extract classification information from the Multiview and requires only a small number of SAR images to train the network model. Experimental results have shown the superiority of the proposed framework based on the moving and stationary target acquisition and recognition datasets. By analyzing multiple images from different viewpoints (Figure 7a,b), the proposed algorithm showed an improved recognition rate using the targets' four input views compared to when using two or three input views, in the case of an extended operating condition (Figure 7c). CNNs require a massive amount of parameters and operations to generate a single inference, making them unsuitable for latency- and energy-constrained applications such as SAR-TR. To reduce the cost of implementing these networks, [PERSON] et al. [110] developed a set of compact network architectures, which achieves an overall 984 times reduction in terms of storage requirements and 71 times reduction in terms of computational complexity compared to state-of-the-art CNNs for automatic target recognition. To achieve good performance with a small number of parameters, [PERSON] et al. [111] proposed a lightweight two-stream CNN to extract multilevel features. Experimental results demonstrate that the proposed method achieved higher recognition accuracy compared to other CNN-based methods. Another method for determining the characteristics of the unknown reflective object is analyzing the signals from inverse scattering problems of EM.  
Figure 7: \(Cont.\)  
Figure 7: Illustrations of Multiview SAR (Synthetic Aperture Radar) ATR (Automatic Target Recognition) and Multiview SAR image combinations’ generation for training. (**a**) Geometric model of Multiview SAR ATR for a ground target, (**b**) confusion matrix under extended operating condition with recognition rate for each Multiview, and (**c**) example of nine three-view SAR image combinations’ generation from only six raw SAR image [109].