arXiv subjects

Boxuan Sun

Publications and source records attributed to Boxuan Sun.

3 recordsLinked to original sources

arXiv2026

ISAC and Vision Fusion for Fine-Grained Low-Altitude Target Recognition

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.

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arXiv2026

Networked ISAC Enabled Target Recognition Towards Low-Altitude Economy

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/.

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arXiv2025

Bridge Micro-Deformation Monitoring Scheme with Integrated Sensing and Communications

In this paper, we propose a novel integrated sensing and communications (ISAC) scheme to perform bridge micro-deformation monitoring (BMDM) in complex environments. We first provide an excitation-bridge coupling model to represent the micro-deformation process of the bridge. Next, we design a novel frame structure for BMDM applications, and construct the OFDM echo channel model for basic scene of BMDM, including micro-deformation, dynamic objects, and static environment. Then, we develop a phasor statistical analysis method based on average cancellation algorithm to suppress the interference of dynamic objects, as well as a circle fitting method based on least squares algorithm to remove the interference of static environment near the monitoring area. Furthermore, we extract the micro-deformation feature vector from the OFDM echo signals after inverse discrete fourier transform (IDFT), and derive vertical micro-deformation value with the time-frequency phase resources. Simulation results demonstrate the effectiveness of the proposed BMDM scheme and its robustness against both dynamic interferences and static interferences.

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