The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.

该研究使用两个深度卷积神经网络，基于多普勒雷达频谱图和视觉图像，研究了目标检测和分类。用于行走的人和拍打翅膀的小鸟的运动学模型已集成到MATLAB仿真中以创建数据集。动态模拟器识别了每个椭圆体段的最终位置，并考虑了其旋转运动以及每个采样点的整体运动，从而自然地描述了其特定运动。整体运动会引起微多普勒效应，并产生微多普勒信号，其响应于输入参数的变化而变化，例如变化的人体节段大小，速度和雷达位置。从目标对象返回的雷达信号的微多普勒签名识别由模拟器进行动画处理，需要基于信号的短时傅立叶变换分析的运动学建模。仅查看一次V3和Inception V3都用于检测和分类黑色或白色背景上具有不同红色，绿色，蓝色的对象。结果表明，在低可见度条件下，可以实现基于清晰的微多普勒签名图像的目标识别。这项可行性研究证明了多普勒雷达在黑暗中作为摄像机的备用传感器在自动驾驶汽车中的应用可能性。在这项研究中，动画运动模型及其同步雷达光谱图首次成功尝试进行物体识别。