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TIAN Biao, CHEN Junyan, WAN Yanyu, et al. Low-altitude target dataset and multifeature recognition method based on holographic staring radar[J]. Journal of Radars, in press. doi: 10.12000/JR25212
Citation: TIAN Biao, CHEN Junyan, WAN Yanyu, et al. Low-altitude target dataset and multifeature recognition method based on holographic staring radar[J]. Journal of Radars, in press. doi: 10.12000/JR25212
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Low-altitude Target Dataset and Multifeature Recognition Method Based on Holographic Staring Radar

DOI: 10.12000/JR25212 CSTR: 32380.14.JR25212

  • School of Electronics and Communication Engineering, Sun Yat-sen University Shenzhen, Guangdong 518107, China

Funds:  The National Natural Science Foundation of China (62371477, U2133216)
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    Abstract
  • The threat from low-altitude targets to airspace security such as airport is increasing, making accurate detection and recognition essential for radar systems. High-quality measured radar datasets are crucial for advancing low-altitude target recognition. However, most existing public radar datasets for these targets consist of simulation data or short-range collected data, which have difficulty accurately reflecting and verifying radar target recognition performance in long-range scenarios. To overcome these limitations, this study creates a low-altitude target detection and recognition dataset based on holographic staring radar, including measured data collection and recognition validation for typical low-altitude targets in outdoor environments. The dataset includes common targets such as multirotor unmanned aerial vehicles, sparrows, and large migratory birds, along with representative motion scenarios like hovering, circling, and radial flight. It also offers synchronized target micro-Doppler waterfall plots and radar-measured track information (including azimuth and elevation angles, radial velocity, and normalized signal-to-noise ratio), providing a data foundation for exploring the intrinsic link between target detailed features and motion states. Building on this, a multimodal adaptive feature fusion network is developed to extract and combine Doppler and kinematic features from different targets, demonstrating the dataset’s effectiveness in distinguishing various low-altitude targets.

     

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