The real-time tracking of moving objects has extensive applications in various domains. Existing tracking methods typically utilize video image processing, but their performance is limited due to the high information throughput and computational requirements associated with processing continuous images. Additionally, imaging in certain spectral bands can be costly. This paper proposes a non-imaging real-time three-dimensional tracking technique for distant moving targets using single-pixel LiDAR. This novel approach involves compressing scene information from three-dimensional to one-dimensional space using spatial encoding modulation and then obtaining this information through single-pixel detection. A LiDAR system is constructed based on this method, where the peak position of the detected full-path one-dimensional echo signal is used to obtain the target distance, while the peak intensity is used to obtain the azimuth and pitch information of the moving target. The entire process requires minimal data collection and a low computational load, making it feasible for the real-time three-dimensional tracking of single or multiple moving targets. Outdoor experiments confirmed the efficacy of the proposed technology, achieving a distance accuracy of 0.45 m and an azimuth and pitch angle accuracy of approximately 0.03° in localizing and tracking a flying target at a distance of 3 km.

Atmospheric turbulence is a key factor contributing to data distortion in mid-to-long-range target observation tasks. Neural networks have become a powerful tool for dealing with such problems due to their strong ability to fit nonlinearities in the spatial domain. However, the degradation in data is not confined solely to the spatial domain but is also present in the frequency domain. In recent years, the academic community has come to recognize the significance of frequency domain information within neural networks. There remains a gap in research on how to combine dual-domain information to reconstruct high-quality images in the field of blind turbulence image restoration. Drawing upon the close association between spatial and frequency domain degradation information, we introduce a novel neural network architecture, termed Dual-Domain Removal Turbulence Network (DDRTNet), designed to improve the quality of reconstructed images. DDRTNet incorporates multiscale spatial and frequency domain attention mechanisms, combined with a dual-domain collaborative learning strategy, effectively integrating global and local information to achieve efficient restoration of atmospheric turbulence-degraded images. Experimental findings demonstrate significant advantages in performance for DDRTNet compared to existing methods, validating its effectiveness in the task of blind turbulence image restoration.

Fourier ptychography imaging technology is a method developed in recent years to achieve high-resolution imaging. In the traditional macro Fourier ptychography technology, the scanning method when the camera captures low-resolution images mostly uses the rectangular linear grid format. These acquired images contain a small amount of complementary information, and a large number of low-resolution images are needed to achieve high-resolution imaging. Redundant measurements will extend the sampling and reconstruction time, and require more computing resources. In this paper, we propose to obtain the target image spectral energy distribution by pre-sampling. And according to the energy distribution, we use irregular and non-uniform sampling modes to restore the target image. With the same number of samples and same reconstruction time, higher resolution imaging can be achieved compared with traditional methods. Simulation and experimental studies are carried out in this paper, and the results confirm the effectiveness of the proposed methods. Compared with the traditional sampling mode, the two sampling modes proposed in this paper increase the resolution from 4.49 lp/mm to 5.66 lp/mm and 5.04 lp/mm respectively.