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.

模拟特征提取 (AFE) 对于低延迟和高效的认知传感系统来说是一种有吸引力的策略，因为关键特征比奈奎斯特采样数据稀疏得多。然而，由于模拟电子电路的带宽和可编程性瓶颈，将 AFE 应用于宽带射频 (RF) 场景具有挑战性。在这里，我们介绍了一种基于光子学的方案，该方案从模拟域中的宽带射频信号中提取时空特征。受卷积神经网络启发的特征提取器结构在集成光子电路上实现，用于处理来自多个天线的射频信号，从时间和空间维度提取有效特征。由于光子器件的可调谐性，光子时空特征提取器是可训练的，这增强了提取特征的有效性。此外，提出了一种数模混合迁移学习方法，用于有效且低成本地训练光子特征提取器。为了验证我们的方案，我们演示了具有 4 GHz 瞬时带宽的雷达目标识别任务。实验结果表明，光子模拟特征提取器可以处理宽带射频信号，并将模数转换器的采样率降低至奈奎斯特采样的1/4，同时保持97.5%的高目标识别精度。我们的方案为在认知射频传感领域利用 AFE 策略提供了一条有前景的道路，有可能为自动驾驶、机器人和智能工厂等应用中的高效信号处理做出贡献。