Radar-based Object Classification Using An Artificial Neural Network

Radar-based object detection becomes a more important problem as such sensor technology is broadly adopted in many applications including military, robotics, space exploring, and autonomous vehicles. However, the existing solution for classification of radar echo signal is limited because its deterministic analysis is too complicated to describe various object features. It needs a more sophisticated approach that is capable of identifying them. In this paper, we intend to solve this problem using a state-of-art machine learning approach to read object features or patterns in their micro-Doppler signatures in radar reflection data. In spectrogram analysis, we observe unique patterns of objects, which should be recognizable by a well-trained machine learning algorithm. We train our AlexNet-inspired convolutional neural network model to see these patterns over their radar signal spectrogram and design an intelligent waveform detection system. We demonstrate our proposed system on an Intel^® Xeon CPU and an Intel^® Arria 10 FPGA using Intel^® Open VINO toolkit, a unified framework to import deep learning algorithms in different platforms and achieve a real-time system for automated object classification with over 90% of accuracy on a given radar dataset.