AI 中文总结
针对ISAC网络低空目标识别问题，提出基于多基站协作与多尺度特征融合的方案，设计含Swin Transformer的识别网络，生成大规模数据集，经仿真验证该方案能实现高精度识别及强泛化能力，有效证明多尺度特征融合及多基站协作的作用。
AI 中文摘要
本文提出一种基于多基站协作与多尺度特征融合的低空目标（LAT）识别方案，用于一体化感知与通信（ISAC）网络。首先制定多基站协作监测场景下无人机、鸟类、车辆和行人的运动方程、回波通道及回波信号。接着从回波信号中提取各基站观测的速度分辨率优先时频谱、时间分辨率优先时频谱和速度转移时频谱作为LAT的多尺度特征。然后设计含Swin Transformer的多基站多尺度特征融合LAT识别网络，利用多尺度特征可视化图像经深度特征提取、基站内特征交互、基站间特征交互及目标识别输出联合识别目标。生成含1440000个样本的大规模回波信号数据集用于ISAC网络LAT识别，可作公共基准评估方案。仿真结果表明该方案实现高识别精度和强未见子类型泛化能力，证实多尺度特征融合及多基站协作的有效性。
英文摘要
In this paper, we propose a low-altitude target (LAT) recognition scheme based on multi-base station (BS) collaboration and multi-scale feature fusion for integrated sensing and communications (ISAC) network. Firstly, we formulate the motion equations, echo channels, and echo signals for unmanned aerial vehicle (UAV), bird, vehicle, and pedestrian under multi-BS collaborative monitoring scenario. Then we extract the velocityresolution-preferred time-frequency spectrum, time-resolutionpreferred time-frequency spectrum, and the velocity-transfer time-frequency spectrum observed by each BS from echo signals. We collectively refer to these three types of time-frequency spectrum as the multi-scale feature of the LAT. Next, we design a multi-BS and multi-scale feature fusion enabled LAT recognition network with Swin Transformer, which employs the visualized images of multi-scale feature to jointly recognize the target through deep feature extraction, intra-BS feature interaction, inter-BS feature interaction, and target recognition output. We generate a massive echo signal dataset comprising 1,440,000 samples for LAT recognition within ISAC network. This dataset can serve as a public benchmark to evaluate our proposed scheme and facilitate future research. Simulation results demonstrate that the proposed scheme realizes high recognition accuracy and robust unseen-subtype generalization, confirming the effectiveness of multi-scale feature fusion and the additional gains brought by multi-BS collaboration. The project page is available at: https://alivn999.github.io/COSMOS-Networked-ISAC-Enabl ed-Target-Recognition-Towards-Low-Altitude-Economy/.
