Urban street lighting system plays an important role in urban traffic lighting and road safety. With it, the power consumption of urban street lights is increasing, and the cost is also increasing year by year. The purpose of this paper is to detect and classify targets through road monitoring radar, so as to achieve the purpose of real-time detection of pedestrians, vehicles and other targets, so as to control the brightness of street lights to save power consumption. This paper firstly establishes the radar echo model of various targets, deduces the model formula of radar echo, and analyzes the micro-Doppler characteristics produced by the micro-moving parts of the target. Secondly, Secondly, the ResNet residual neural network training method is used to analyze the target micro-Doppler information, extract the depth information, and realize the target detection and recognition task.
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