中心助理教授徐绍夫的工作——Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics（基于集成光子学的认知射频感知模拟域时空特征提取）的相关成果近期被Light: Science & Applications期刊接收发表，该工作得到了国家自然科学基金（T2225023、62205203）的部分资助。
在雷达、机器视觉、医学影像等智能感知领域，一个重要瓶颈是海量数据难以高效、实时的处理。本质上，有用信息的数据量远小于数字采集的数据量，若可以在数字采集之前实时提取到有用信息，则可以变海量数据为少量数据，大幅降低数据处理的压力。但目前，受限于电子器件的带宽与可编程性瓶颈，仅能实现音频、心脑电等窄带信号的有用信息提取，在宽带雷达等射频感知系统中仍存在巨大挑战。针对这一问题，该工作提出采用集成光子学方法来突破带宽与可编程性瓶颈的创新思想，针对射频感知设计实现了光子学时空特征提取芯片，可以直接将天线接收的海量射频信号转化为有用信息。实验表明，该方法可以将宽带雷达的采样率压缩4倍，且目标识别准确率保持在97.5%以上。该工作成功的将“有用信息实时提取”的思想推广到了射频感知领域，对未来的自动驾驶、机器人、6G通感一体等应用发展具有重要意义。
摘要: Analog feature extraction (AFE) is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled data. However, applying AFE to broadband radio-frequency (RF) scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry. Here, we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain. The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas, extracting valid features from both temporal and spatial dimensions. Because of the tunability of the photonic devices, the photonic spatiotemporal feature extractor is trainable, which enhances the validity of the extracted features. Moreover, a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature extractor. To validate our scheme, we demonstrate a radar target recognition task with a 4-GHz instantaneous bandwidth. Experimental results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%. Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing, with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving, robotics, and smart factories.