Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Abstract

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.

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

“…Nonetheless, the landscape of PNNs has seen significant changes with the advent of large-scale photonic assembly and integration methods [17,18]. For example, silicon photonics has become a leading platform for producing extensive and cost-effective optical systems [19][20][21]. Concurrently, various evolving applications, such as resolving nonlinear optimization problems and processing multichannel GHz analog signals in real time, are seeking innovative computing platforms to fulfill their computational needs [22].…”