# Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar
###### Abstract  
At present, millimeter wave radar imaging technology has become a recognized human security solution in the field. The millimeter wave radar imaging system can be used to detect a concealed object; multiple-input multiple-output radar antennas and synthetic aperture radar techniques are used to obtain the raw data. The analytical Fourier transform algorithm is used for image reconstruction. When imaging a target at 90 mm from radar, which belongs to the near field imaging scene, the image resolution can reach 1.90 mm in \(X\)-direction and 1.73 mm in \(Y\)-direction. Since the error caused by the distance between radar and target will lead to noise, the original reconstruction image is processed by gamma transform, which eliminates image noise, then the image is enhanced by linearly stretched transform to improve visual recognition, which lays a good foundation for supervised learning. In order to flexibly deploy the machine learning algorithm in various application scenarios, ShuffleNetV2, MobileNetV3 and GhostNet representative of lightweight convolutional neural networks with redefined convolution, branch structure and optimized network layer structure are used to distinguish multi-category SAR images. Through the fusion of squeeze-and-excitation and the selective kernel attention mechanism, more precise features are extracted for classification, the proposed GhostNet_SERResNet56 can realize the best classification accuracy of SAR images within limited resources, which prediction accuracy is 98.18% and the number of parameters is 0.45 M.  
m _Applied_  
23 September 2021  
**Published: 24 September 2021**
customized systems. In 2020, MIMO-ISAR technology was used to reduce scanning time in a near-field millimeter wave imaging system [5]. In 2021, dual-polarization antennas were employed to improve the millimeter wave imaging system [6]. This makes it possible to design low-cost and low-power millimeter wave imagers based on the latest development of frequency modulated continuous wave (FMCW) millimeter wave radar with synthetic aperture radar (SAR) [7] and multiple-input multiple-output (MIMO) [8] radar antennas technology. This paper uses the MIMO-SAR radar to move along a zigzag route: the radar starts at three transmit antennas and four receive antennas and transmits the FMCW signal at each position, receives and stores the radar echo signal at the corresponding transmitting position, which generates an equivalent long antenna aperture--the image's longitudinal resolution and horizontal resolution is guaranteed. However, in the previous near-field millimeter wave imaging system, human intervention is needed to check whether the tested person is carrying dangerous goods, which greatly reduces the detection efficiency. In recent years, convolutional neural networks have been used for SAR images classification [9]. There is a lot of redundancy in mainstream convolutional neural networks, which leads to the process of training the model taking up a lot of time and memory space. Lightweight convolutional neural networks, such as predicting facial expressions by ShuffleNetV2 [10], complete autonomous vehicle target detection by MobileNetV3 [11] and remote sensing image classification by GhostNet [12], reduce the amount of network parameters and calculations through redefining convolution, adopting branch structure and optimizing the network layer structure. Compared with traditional neural networks, lightweight CNN reduces the size of the model and increases the speed while maintaining the same level of accuracy. On the basis of the existing lightweight neural network, this paper innovatively introduces the SE (squeeze-and-excitation) and SK (selective kernel) attention mechanism module, the importance of each feature channel is automatically acquired through learning, and then the useful features are promoted according to this importance and the features that are not useful for the current task are suppressed. The system performance is improved, and better classification results are obtained.