We propose and demonstrate a deep learning-assisted photonic approach for measuring the angle-of-arrival (AOA) with high-precision, which is suitable for long- baseline direction finding (DF). A non-uniform linear array with long- baseline is constructed to increase the precision of AOA estimation and to deal with the problem of ambiguity. The system realizes AOA-to-Voltage mapping by using dual-drive Mach Zehnder modulator (DDMZM) as phase detector and envelope detection in electrical domain. Finally, a deep neural network with long-short term memory (LSTM-DNN) is used for post-processing to establish a mapping relationship between the envelope voltage and real AOA, which not only simplifies the measurement process without phase calibration and transformation between phase difference and AOA, but also compensates the defects of the optoelectronic system and effectively improves the AOA estimation performance. Results obtained using the proposed structure demonstrate less than $0.3405^{\circ }$ errors over a - $80^{\circ }$ to $80^{\circ }$ AOA measurement range, and the mean absolute error (MAE) and root mean square errors (RMSE) are $0.1438^{\circ }$ and $0.3923^{\circ }$ respectively.

Deep learning techniques have been widely investigated as an effective method for signal measurement in recent years. However, most existing deep learning-based methods still face difficulty in deploying on embedded platforms and perform poorly in real-time applications. To address this, this paper develops two accelerators, as the core of the signal measurement system, for intelligent signal processing. Firstly, by introducing the idea of automated framework, we propose a simplest deep neural network (DNN)-based hardware structure, which automatically maps algorithms to hardware modules, supports configurable parameters, and has the advantage of low latency, with an average inference time of only 3.5 μs. Subsequently, another accelerator is designed with the efficient hardware structure of the long short-term memory (LSTM) + DNN model, demonstrating outstanding performance with a classification accuracy of 98.82%, mean absolute error (MAE) of 0.27°, and root mean square errors (RMSE) of 0.392° after model compression. Moreover, parallel optimization strategies are exploited to further reduce latency and support simultaneous frequency and direction measurement tasks. Finally, we test the actual collected signal data on the XCVU13P field programmable gate array (FPGA). The results show that the time of inference saves 28–31% for the DNN model and 71–73% for the LSTM + DNN model compared to running on graphic processing unit (GPU). In addition, the parallel strategies further decrease the delay by 23.9% and 37.5% when processing continuous data. The FPGA-based and deep learning-assisted hardware accelerators significantly improve real-time performance and provide a promising solution for signal measurement.

Angle of arrival (AOA) estimation and instantaneous frequency measurement (IFM) are important aspects of radar, communication, and electronic warfare, and there is an urgent need to find intelligent solutions that are faster and more accurate than traditional methods. Currently, deep learning (DL) has increasingly strong learning and feature extraction capabilities, which brings new opportunities to the field of electromagnetic signal processing. However, the models in DL have a large number of parameters, high storage requirements, and long computational latency, and not practical for deploying applications on resource-limited devices. In this paper, we build a complete AOA and IFM system for the first time, taking advantage of microwave photonics (MWP) technology, DL, and features of field programmable gate array (FPGA). Then, a novel hardware-friendly pruning-quantization-iteration compression (PQIC) algorithm is proposed for the post-processing of MWP signal measurement applications. By reducing the data width from 32 bits to 4 bits, the compressed algorithm reduces parameter storage requirements and hardware implementation complexity with negligible performance loss. Finally, we deploy the acceleration system and test the actual collected signal data on FPGA while running 200 MHz. The results show that with a 14× model compression and a 3.99× reduction in operations, the accuracy of AOA reaches 98.82%, with the mean absolute error (MAE) of 0.27°. More importantly, the running latency is only 20.78 μs, meeting the real-time processing requirements of MWP signal processing. It is also applicable to real-world signal intelligence processing and demonstrates superior performance compared to other existing algorithms in this field.