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.

Geometric moment patterns are selected as non-orthogonal gray patterns used for single-pixel centroid detection. By using geometric moment patterns for structured illumination, the reflected light intensities collected by a single-pixel detector correspond to the zero-order and first-order geometric moments, which are leveraged to reconstruct and track the centroid of moving objects. However, the frame rate of the single-pixel tracking system is limited because geometric moment patterns are grayscale while the digital micromirror device (DMD) performs binary modulation much faster than grayscale modulation. Although spatial dithering addressed the problem by binarizing the patterns, the accuracy of tracking a small-sized moving object will decrease by the extra dithering error. Therefore, we propose a binary defocus method to improve the accuracy of tracking a small-sized moving object while ensuring a high frame rate. The key to the proposed method is to simulate grayscale geometric moment patterns by defocusing projection binary geometric moment patterns. The experimental results show that the proposed defocus method is still available for tracking small-sized moving objects even when the focused method fails, and ∼48% lower Centroid Estimation Error (CEE) than that of the focused approach when the frame rate is ∼7400 Hz. Meanwhile, our works also provide relationships between CEE of focused and defocus methods varies with the size of the moving object.

When imaging moving objects, single-pixel imaging produces motion blur. This paper proposes a method based on single-pixel imaging, which can achieve anti-motion blur imaging of fast-moving objects. In this method, the geometric moment patterns and Hadamard patterns are used to alternately encode the position information and the image information of the object with time-division multiplexing. In the reconstruction process, the object position information is extracted independently and combining motion-compensation reconstruction algorithm to decouple the object motion from image information. As a result, the anti-motion blur image and the high frame rate object positions are obtained. Experimental results show that for a moving object with an angular velocity of up to 0.5rad/s relative to the imaging system, the proposed method achieves a localization frequency of 5.55kHz, and gradually reconstructs a clear image of the object with a pseudo resolution of 512×512. The method has application prospects in single-pixel imaging for fast-moving objects.

Single-pixel imaging uses a single-pixel detector and sequential structured illumination to reconstruct images, offering remarkable flexibility across spectral bands. A longstanding challenge is that object or platform motion during acquisition is traditionally regarded as a source of blurring and degradation. Here, we present a paradigm in which object motion is reinterpreted as a beneficial source of sub-pixel sampling diversity rather than a detriment. By accurately tracking translational motion—accomplished via geometric moment illumination patterns fused with Hadamard patterns—we encode the known motion into the SPI forward model as structured perturbations. This approach converts motion from a blurring nuisance into a valuable imaging resource, effectively increasing the sampling density beyond the native grid. Incorporating the motion information directly into the inverse problem, we demonstrate that an enhanced-resolved reconstruction of the scene becomes achievable. The resulting linear measurement system, solved via total variation minimization, yields finer image details than SPI reconstructions acquired with a stationary target. Our work suggests that motion in SPI, far from being merely something to mitigate, can be harnessed to fundamentally improve SPI resolution and fidelity in dynamic scenes.