Tensor processing is a cornerstone of many modern technological advancements, powering critical applications in data analytics and artificial intelligence. While optical computing offers exceptional advantages in bandwidth, parallelism and energy efficiency, existing methods optimized for scalar operations struggle to efficiently handle tensor-based tasks, limiting their applicability in complex applications, such as neural networks. Here we report parallel optical matrix–matrix multiplication (POMMM), which enables fully parallel tensor processing through a single coherent light propagation. This approach addresses key limitations of current optical methods, scaling the performance with data dimension, while improving theoretical computational power and efficiency. We demonstrate its high consistency with GPU-based matrix–matrix multiplication across both real-valued and complex-valued domains. Moreover, we showcase its adaptability, scalability and versatility in tensor processing applications such as convolutional and vision transformer neural networks. Furthermore, we analyse the theoretical compatibility and efficiency of POMMM in relation to existing optical computing paradigms, highlighting its potential to outperform current state-of-the-art methods. By enabling a variety of computational tasks and supporting multi-wavelength and large-scale expansion, POMMM provides a scalable, high-efficiency foundation for advancing next-generation optical computing.

Traditional synthetic-aperture radar (SAR) imaging, while adept at capturing stationary scenes with clarity, often results in the blurring of moving targets due to Doppler spectrum disparities. To overcome this, this study introduces an innovative optical approach for imaging moving targets. By employing a spatial light modulator (SLM), the phase information of SAR data can be modulated onto a light beam. Then, the light propagation path is meticulously designed to take advantage of the free propagation of light to compensate for the phase differences in SAR data, thereby achieving focal clarity. Simulations and experimental validations have demonstrated that this method surpasses traditional digital imaging techniques in terms of focusing precision. Additionally, the processing delay is only as long as the time taken for light propagation. The optical path is simple, avoiding complex assembly and alignment. This novel approach opens up new possibilities for the SAR imaging of moving targets, offering potential applications in moving target extraction, separation, and velocity estimation.

Radar Cross Section (RCS) is a measurement of scattering performance of an object. RCS plays an important role in the design of stealth weapon system. However, the method of calculating RCS of every angle of azimuth and elevation is complex, and the efficiency is low. Evidently, it is of great significance for predicting the unknown RCS given a set of RCS data of an object. Towards this aim, we propose a novel approach using multilayers Long Short Term Memory (LSTM) networks based on unsupervised learning - the mechanism is similar to that of autoencoder. The encoder LSTM maps the input RCS into a fixed length representation. The decoder LSTM decodes the representation to predict RCS. For the purpose of this work, we create an 3D object and collect its RCS for a dataset with the help of electromagnetic simulation software, FEKO. The proposed networks have been applied on the test dataset and yield satisfactory results.

Electronic reconnaissance is to detect signals, extract their parameters, modulation types or direction of arrival and so on from a wide bandwidth range. It is difficult for digital signal processing device to process in real time under an ultra-wide bandwidth environment. This paper proposed a programmable optical system which can process signals from an instantaneous bandwidth up to 40GHz in real time. In the optical system, the signals are reconstructed at wavefront of a laser beam. The laser beam carrying signals passes through an optical system composed by lens, beam splitter, light modulator, etc. Signal processing operation is accomplished when laser beam arrives at a focal plane, and processing results are acquired by a high-speed camera. Typical pulse description words can be yielded from the results. The proposed optical system has a nano-second processing delay due to its meter-length light path. (c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

With the rapid development of synthetic aperture radar (SAR), delivery platforms are gradually becoming diversified and miniaturized. The SAR flight process is susceptible to external influences, resulting in unsatisfactory imaging results, so it is necessary to optimize imaging processing in combination with the SAR imaging quality assessment (IQA) index. Based on the principle of SAR imaging, this paper analyzes the impact of defocusing on imaging results caused by mismatched filters and draws on the assessment algorithm of motion blur, proposing a SAR IQA index based on average blurred edge width (ABEW) in the salient area. In addition, the idea of decomposing the transfer function in the frequency domain and fitting the matched filter with a polynomial is also proposed. The estimation of the flight trajectory is changed to a correction of the matched filter, avoiding the precise estimation of Doppler parameters and complex calculations during the time–frequency conversion process. The effectiveness of ABEW was verified by using SAR images of real scenes, and the results were highly consistent with the actual image quality. The imaging processing was tested using the echo signals generated by the errors introduced during the flight process, and more satisfactory imaging results were obtained by using ABEW with the filter for correction. The imaging process was tested using the echo signal generated by introducing errors during the flight, and the filter was corrected using ABEW as an index, obtaining a comparatively ideal imaging result.