A Neural Network with Convolutional Module and Residual Structure for Radar Target Recognition Based on HighResolution Range Profile In the conventional neural network, deep depth is required to achieve high accuracy of recognition. Additionally, the problem of saturation may be caused, wherein the recognition accuracy is downregulated with the increase in the number of network layers. To tackle the mentioned problem, a neural network model is proposed incorporating a micro convolutional module and residual structure. Such a model exhibits few hyperparameters, and can extended flexibly. In the meantime, to further enhance the separability of features, a novel loss function is proposed, integrating boundary constraints and center clustering. According to the experimental results with a simulated dataset of HRRP signals obtained from thirteen 3D CAD object models, the presented model is capable of achieving higher recognition accuracy and robustness than other common network structures.

Resolving Elevation Ambiguity in 1D Radar Array Measurements using Deep Learning Motivated by requirements for future automotive radar, we study the problem of resolving target elevation from measurements by a onedimensional horizontal radar antenna array. This is a challenging and illposed problem, since such measurements contain only indirect and highly ambiguous elevation cues. As a consequence, traditional modelbased approaches fail. We instead propose to use a machinelearningbased approach that learns to exploit the subtle elevation cues and prior knowledge of the scene from the data. We design an encoderdecoder structured deep convolutional neural network that takes a radar return intensity image in the rangeazimuth plane as input and produces a depth image in the elevationazimuth plane as output. We train the network with over 200 000 radar frames collected in highway environments. Through experimental evaluations, we demonstrate the feasibility of resolving the highly ambiguous elevation information in such environments.