AI 中文总结
针对细粒度低空目标识别问题，提出ISAC与视觉融合框架，利用ISAC系统估计目标位置，调整摄像机参数，通过多种技术处理回波信号和视觉图像，设计识别网络，并生成联合数据集，经仿真验证方案有效优越。
AI 中文摘要
本文提出了一种用于细粒度低空目标识别的信息感知与通信（ISAC）及视觉融合框架。首先利用ISAC系统估计低空目标位置，据此调整云台摄像机工作参数以捕捉数百米外微小低空目标图像。获取目标无线回波信号和视觉图像后，用短时傅里叶变换获取目标微多普勒频谱，设计基于条件生成对抗网络的去噪网络优化频谱质量，同时用YOLOv11检测视觉图像中的目标并裁剪特征图像，设计基于MobileViT的细粒度识别网络融合优化后的频谱和裁剪图像以区分目标子类。此外，基于AirSim和Wireless InSite生成联合数据集。仿真结果验证了该方案的有效性和优越性。
英文摘要
In this paper, we propose an integrated sensing and communications (ISAC) and vision fusion framework for fine-grained low-altitude target recognition. Specifically, we first utilize ISAC system to estimate the position of the low-altitude target. Then we adjust the working parameters of the Pan-TiltZoom (PTZ) camera based on the estimated target position, such that the camera can capture the image of tiny low-altitude target from several hundred meters away. After obtaining the wireless echo signal and visual image of low-altitude target, we employ the short-time Fourier transform (STFT) to obtain the micro-Doppler (mD) spectrum of the target from wireless echo signal, and design a conditional generative adversarial network (cGAN)-based denoising network to optimize the quality of the mD spectrum. Meanwhile, we employ YOLOv11 to detect the low-altitude target from visual image, and then crop the smallsized feature image of the target from the original image. Next, we design a fine-grained low-altitude target recognition network with MobileViT, which can fuse the optimized mD spectrum and the cropped feature image to distinguish the subcategory of low-altitude target. Moreover, we generate a joint ISAC and vision dataset (JIVD) for low-altitude target monitoring based on AirSim and Wireless InSite, which includes diverse target subcategories, scenarios, and weather conditions. The effectiveness and superiority of the proposed scheme have been demonstrated by simulation results.
