Embedded lightweight millimeter wave radar target identification method

Abstract

The invention discloses an embedded lightweight millimeter wave radar target identification method, and relates to the technical field of target identification, and the method comprises the steps: receiving electromagnetic wave envelope signals returned by an object at different distances or different speeds; de-noising the electromagnetic wave envelope signals through a wavelet threshold method, manually marking the de-noised electromagnetic wave envelope signals to generate a sample library, and constructing a category set; the sample library is used for training a lightweight convolutional network model, the convolution layer number range of the lightweight convolutional network model is 36-58, and the compression factor range of a compression layer is 0.3-0.5; collecting an electromagnetic wave envelope signal returned by a to-be-detected target through a millimeter wave radar, denoising the electromagnetic wave envelope signal through a wavelet threshold method, inputting the denoised electromagnetic wave envelope signal to the lightweight convolutional network model, and identifying the category of the to-be-detected target; object category identification is realized based on the lightweight convolutional network model and in combination with the millimeter wave radar sensor, the physical characteristics of the target object can be more effectively revealed, the flexibility is high, and the practical value is higher.

Description

technical field [0001] The invention relates to the technical field of target recognition, in particular to an embedded lightweight millimeter-wave radar target recognition method. Background technique [0002] The arrival of the intelligent era will bring more and more convenience to people. As a part of intelligent life, intelligent perception will play an important role in pedestrian tracking, traffic control, etc. [0003] Currently commonly used sensors include visual sensors, lidar, etc., but visual sensors cannot measure the distance information of the target and are easily affected by the weather; lidar is expensive and has poor applicability in rainy and foggy weather, and the data is output in point cloud format. However, millimeter-wave radar has low ranging accuracy, but has strong penetrability, and has the characteristics of all-weather and all-weather. It is suitable for relatively harsh environments and has a small amount of data. Therefore, we propose an em...

AI Technical Summary

Problems solved by technology

[0003] Currently commonly used sensors include visual sensors, lidar, etc., but visual sensors cannot measure the distance information of the target and are easily affected by the weather; lidar is expensive and has poor applicability in rainy and foggy weather, and the data is output in point cloud format. However, millimeter-wave radar has low ranging accuracy, but has strong penetrability, and has the characteristics of all-weather and all-weather. It is suitable for relatively harsh environments and has a small amount of data. Therefore, we propose an embedded lightweight Quantified millimeter-wave radar target recognition method

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