Single-photon Lidar Research Articles

Adaptive range gating based on variational Bayesian inference for space debris ranging with spaceborne single-photon LiDAR.

To enhance the accuracy of space debris localization, spaceborne single-photon LiDAR (SSPL) presents a promising technique for accurate target ranging. Extended Kalman filtering (EKF) plays a crucial role in range gating under high dynamic and nonlinear motion conditions of space debris, ensuring accurate state estimation and prior distance data. However, unknown and time-varying statistics of process and measurement noise significantly degrade state estimation accuracy, posing risks of filter divergence and reduced photon reception, ultimately rendering range gating ineffective. To address this challenge, we propose an adaptive range gating method based on variational Bayesian adaptive extended Kalman filtering (ARG-VBAEKF). This method leverages variational Bayesian (VB) posterior approximation to estimate the joint distribution of state and noise. Simulation results demonstrate that ARG-VBAEKF achieves accurate state and noise estimation, thereby effectively enhancing range gating performance in SSPL-based space debris ranging.

Surface roughness metrology with a raster scanning single photon LiDAR

We explore a novel, to the best of our knowledge, approach to surface roughness metrology utilizing a single pixel, raster scanning single photon counting LiDAR system. It uses a collimated laser beam in picosecond pulses to probe a surface, capturing the changes of back-scattered photons from different points on the surface into a single mode fiber, and counting them using a single photon detector. These back-scattered photons carry speckle noise produced by the rough surface, and the variation in photon counts over different illumination points across the surface becomes a good measure of its roughness. By analyzing the variation frequency as the LiDAR scans over the surface using machine learning techniques, we demonstrate general measurements of surface roughness from 1.21 (1.27±4.51) to 102.01 (87.97±10.55) microns.

Marine remote target signal extraction based on 128 line-array single photon LiDAR

LiDAR technology has garnered significant attention in recent years due to its superior directivity, high resolution, and precise 3D information acquisition capabilities, making it indispensable in navigation systems. Among its variants, single-photon LiDAR stands out for maritime applications, owing to its reduced power consumption and extended detection range. However, the high sensitivity of single-photon detectors often results in substantial noise, necessitating effective denoising before data can be utilized for identification, tracking, and other purposes. In this study, we present a novel 128-line, 1550 nm shipborne long-range single-photon LiDAR system, with data collected and analyzed in maritime environments. This system contends with challenges such as a large dynamic range, abundant noise photons, and the complexity of sea surface conditions. To address these issues, we propose an efficient and adaptive denoising algorithm based on the k-th nearest neighbor (KNN) methodology. By examining the distribution characteristics of signal and noise photons, our approach enables target extraction even under conditions of intense noise and sparse signals. Our method exhibits robust adaptability across various detection scenarios. Experimental evaluations demonstrate its efficacy, accurately identifying targets at distances of 3.2 km in clear weather and 1.6 km in foggy conditions.

Analysis of coded dual-repetition rate single-photon LIDAR

Single-photon Light Detection and Ranging (Lidar) has been widely used for long-range ranging due to its single-photon sensitivity and picosecond timing resolution. However, it is still a great challenge to range long-range targets with high accuracy in a short time regardless of whether target is static or dynamic. It is because that the existing long-range single-photon ranging techniques both have non-negligible drawbacks: Coded Signal Ranging (CSSR) with long computation time and Multi-Repetition Rate Ranging (MRRSR) technology with low accuracy. Therefore, the coded multi-repetition rate single-photon ranging (CMSPR) method is proposed to achieve high-accuracy ranging with a short computation time. And coded dual-repetition rate single-photon ranging (CDSPR) system with neighboring coding lengths is proposed to simplify the solution process, which can directly have analytical solutions. Besides, the data centralization method (DCM) based on the coding technique is proposed to further enhance the performance of CDSPR, which is a data pre-processing method based on the characteristics of CDSPR. Results show that CDSPR with DCM can provide a higher ranging error with less computation time.

Fast-scanning speed single-photon counting LiDAR based on front focal length modulation.

This Letter presents a method for fast-scanning speed LiDAR based on front focal length modulation in the transmitter, which compensates for the influence of the laser divergence angle on the scanning speed at close range of the long-range LiDAR. According to the thin lens equation for Gaussian beams, the laser divergence angle is affected by the distance from the lens to the object (the waist of the laser beam). The scanning speed of the LiDAR is correlated to the laser divergence angle during LiDAR operation, and the scanning speed can be improved by expanding the laser divergence angle by enlarging the front focal length of the transmitter. Through experimental analysis, the laser dispersion angle modulation of LiDAR can improve the scanning speed under the premise of guaranteeing the target detection performance in close-range detection.

Sequential Two-Mode Fusion Underwater Single-Photon Lidar Imaging Algorithm

Aiming at the demand for long-range and high-resolution imaging detection of small targets such as submerged submarine markers in shallow coastal waters, research on single-photon lidar imaging technology is carried out. This paper reports the sequential two-mode fusion imaging algorithm, which has a strong information extraction capability and can reconstruct scene target depth and reflection intensity images from complex signal photon counts. The algorithm consists of four steps: data preprocessing, extremely large group value estimation, noise sieving, and total variation smoothing constraints to image the target with high quality. Simulation and test results show that the imaging performance and imaging characteristics of the method are better than the current high-performance first-photon group imaging algorithm, indicating that the method has a great advantage in sparse photon counting imaging, and the method proposed in this paper constructs a clear depth and reflectance intensity image of the target scene, even in the 50,828 Lux ambient strong light and strong interference, the 0.1 Lux low-light environment, or the underwater high-attenuation environment.

Physics-Informed Masked Autoencoder for active sparse imaging

Imaging technology based on detecting individual photons has seen tremendous progress in recent years, with broad applications in autonomous driving, biomedical imaging, astronomical observation, and more. Comparing with conventional methods, however, it takes much longer time and relies on sparse and noisy photon-counting data to form an image. Here we introduce Physics-Informed Masked Autoencoder (PI-MAE) as a fast and efficient approach for data acquisition and image reconstruction through hardware implementation of the MAE (Masked Autoencoder). We examine its performance on a single-photon LiDAR system when trained on digitally masked MNIST data. Our results show that, with 1.8×10-6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.8\ imes 10^{-6}$$\\end{document} or less detected photons per pulse and down to 9 detected photons per pixel, it achieves high-quality image reconstruction on unseen object classes with 90% physical masking. Our results highlight PI-MAE as a viable hardware accelerator for significantly improving the performance of single-photon imaging systems in photon-starving applications.

Bayesian neuromorphic imaging for single-photon LiDAR.

This paper proposes a Bayesian approach to enable single photon avalanche diode (SPAD) arrays to be used as pseudo event cameras that report changes in the scene. Motivated by the working principle of event cameras, which produce sparse events associated with light flux changes, we adopt a changepoint detection strategy to generate intensity and depth change event streams from direct time-of-flight (dToF) sequences measured by SPAD arrays. Although not our main goal, the algorithm also produces as a by-product, intensity and depth estimates. Unlike the output of passive event cameras that only correspond to light flux changes, the change events detected from the sequential dToFs can relate to changes in light flux and/or depth. The integration of the proposed Bayesian approach with single-photon LiDAR (SPL) systems provides a novel solution to achieve active neuromorphic 3D imaging that offers the advantages of significantly reduced output redundancy and in particular the capacity to report scene depth changes. For each pixel of the SPAD array, asynchronous events are generated by performing online Bayesian inference to detect changepoints and estimate the model parameters simultaneously from individual single-photon measurements. Experiments are conducted on synthetic data and real dToF measurements acquired by a 172×126 pixel SPAD camera to demonstrate the feasibility and efficiency of the proposed Bayesian approach.

Advancement on target ranging and tracking by four-quadrant photon counting lidar.

Single-photon lidar stands out as a promising technology for long-distance lidar applications, owing to its attributes of single-photon sensitivity and high repetition rate. Existing single-photon lidar systems typically rely on single-point scanning for positioning and tracking, necessitating intricate and precise scanning control. In pursuit of a more concise and efficient positioning, we incorporate the four-quadrant theory to articulate the signal formula of photon detection, and propose a novel single-photon four-quadrant positioning method. Our method, which includes signal preprocessing, compensation for longitudinal motion, extraction of pixel intensity, and acquisition of lateral motion, facilitates motion acquisition and positioning for targets. Through simulation calculations, we analyze and compare the effectiveness of each step of the method. With longitudinal and lateral speeds of 100 m/s and 50 m/s, respectively, the trajectory error is 1.7%, and the average speed error is 1.8%. Moreover, for various verification experiments, the trajectory errors are all below 4.2%, and the average speed errors remain under 5.4%, effectively verifying the validity of our method in acquiring the motion information and positioning of targets. It provides an excellent option for acquiring motion information and tracking small moving targets over long distances.

Long-term Satellite-derived Bathymetry of Arctic Supraglacial Lake from ICESat-2 and Sentinel-2

Abstract. The polar ice sheets serve as natural thermostats, regulating Earth’s temperatures. The Greenland Ice Sheet (GrIS), the second-largest ice sheet, is a critical indicator of climate change and global warming. Estimating the volume of supraglacial lakes on the GrIS, which is directly linked to the extent of melting in the Arctic ice sheet, requires information on both lake area and water depth. Conventional bathymetric methods (i.e., airborne bathymetric LiDAR, shipborne echo-sounder) are commonly used for accurate water depth measurement. However, polar supraglacial lakes face challenging conditions, leading to uncertainties in their spatial and temporal distribution. To overcome the limitations, this study combines Sentinel-2 and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) to estimate bathymetry and detect changes in lake volume on the GrIS from 2019 to 2023. Firstly, Sentinel-2 images were pre-processed, and ICESat-2 single-photon LiDAR points were extracted using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, followed by the bathymetric corrections as the training data. Subsequently, three bathymetry models (i.e., log-linear, log-ratio, and BP (Back Propagation) neural network) were constructed using Sentinel-2 images and ICESat-2 data. Lastly, the highresolution ArcticDEM (Arctic Digital Elevation Model) was used as the validation data to assess the satellite-derived bathymetry accuracy. In this study, the log-ratio model yielded the best results with the R2, RMSE, and MAE of 0.92, 0.79 m (lower than 10% of the maximum depth), and 0.62 m. The results demonstrate the feasibility of the integrated active and passive remote sensing approach for bathymetry in Arctic supraglacial lakes.

Enhancing angular resolution in single-photon LIDAR for daytime long-range 3D imaging

Single photon LIDAR exhibits exceptional detection sensitivity and finds extensive applications in long-distance measurement and three-dimensional imaging domains. However, it is susceptible to background noise interference, leading to a trade-off between scanning imaging efficiency and angular resolution, thereby limiting its overall performance. In this paper, we propose a novel ‘compression-resampling’ high-resolution imaging method for single-photon scanning LIDAR based on the spatial correlation of the target scene. This approach significantly enhances the system's angular resolution without compromising imaging efficiency. By employing this method, we successfully conducted 3D imaging over distances exceeding 26 km even under challenging conditions with high levels of background noise during daylight hours. Within a target field of view measuring 1° × 1°, we reconstructed a high-resolution 3D point cloud image comprising 1000 × 1410 pixels. Notably, this achievement yielded an impressive angular resolution of 17.4 urad while maintaining an acceptable imaging time of approximately 20 minutes. The results unequivocally demonstrate the effectiveness of our proposed method in improving the quality of three-dimensional imaging and enhancing angular resolution while simultaneously reducing imaging time requirements, thus expanding potential application scenarios for single-photon LIDAR technology.

High accuracy ranging for space debris with spaceborne single photon Lidar.

The increasing risk posed by space debris highlights the need for accurate localization techniques. Spaceborne single photon Lidar (SSPL) offers a promising solution, overcoming the limitations of traditional ground-based systems by providing expansive coverage and superior maneuverability without being hindered by weather, time, or geographic constraints. This study introduces a novel approach leveraging non-parametric Bayesian inference and the Dirichlet process mixture model (DPMM) to accurately determine the distance of space debris in low Earth orbit (LEO), where debris exhibits nonlinear, high dynamic motion characteristics. By integrating extended Kalman filtering (EKF) for range gating, our method captures the temporal distribution of reflected photons, employing Markov chain Monte Carlo (MCMC) for iterative solutions. Experimental outcomes demonstrate our method's superior accuracy over conventional statistical techniques, establishing a clear correlation between radial absolute velocity and ranging error, thus significantly enhancing monostatic space debris localization.

Single-photon ranging lidar based on multi-repetition-rate pulse train correlation and accumulation.

A single-photon lidar based on multi-repetition-rate pulse train correlation and accumulation is proposed, and a ranging experiment is conducted on a 32 m target. By accumulating the correlation ranging results of pulse trains with internal spacings of 80, 100, and 125 ns, the signal-to-noise ratio of the cross correlation function is improved by about three-fold, which enables our method to improve the ranging precisions by more than 20% compared with the single repetition-rate method, and the shorter the acquisition time, the more obvious the advantage will be. Experimental results show that at an acquisition time of 0.01 s, our method can still achieve a ranging precision of 2.59 cm, while the single repetition-rate method can no longer obtain effective ranging results at this time. This method will be of great significance for realizing high-speed, large-scale unambiguous single-photon lidar ranging.

Development of a near-infrared single-photon 3D imaging LiDAR based on 64×64 InGaAs/InP array detector and Risley-prism scanner.

A near-infrared single-photon lidar system, equipped with a 64×64 resolution array and a Risley prism scanner, has been engineered for daytime long-range and high-resolution 3D imaging. The system's detector, leveraging Geiger-mode InGaAs/InP avalanche photodiode technology, attains a single-photon detection efficiency of over 15% at the lidar's 1064 nm wavelength. This efficiency, in tandem with a narrow pulsed laser that boasts a single-pulse energy of 0.5 mJ, facilitates 3D imaging capabilities for distances reaching approximately 6 kilometers. The Risley scanner, composing two counter-rotating wedge prisms, is designed to perform scanning measurements across a 6-degree circular field-of-view. Precision calibration of the scanning angle and the beam's absolute direction was achieved using a precision dual-axis turntable and a collimator, culminating in 3D imaging with an exceptional scanning resolution of 28 arcseconds. Additionally, this work has developed a novel spatial domain local statistical filtering framework, specifically designed to separate daytime background noise photons from the signal photons, enhancing the system's imaging efficacy in varied lighting conditions. This paper showcases the advantages of array-based single-photon lidar image-side scanning technology in simultaneously achieving high resolution, a wide field-of-view, and extended detection range.

Artifact Removal in Long-Range Single Photon LiDAR via Waveforms-Guided Depth Image Estimation

Artifact Removal in Long-Range Single Photon LiDAR via Waveforms-Guided Depth Image Estimation

PE-RASP: range image stitching of photon-efficient imaging through reconstruction, alignment, stitching integration network based on intensity image priors.

Single photon imaging integrates advanced single photon detection technology with Laser Radar (LiDAR) technology, offering heightened sensitivity and precise time measurement. This approach finds extensive applications in biological imaging, remote sensing, and non-visual field imaging. Nevertheless, current single photon LiDAR systems encounter challenges such as low spatial resolution and a limited field of view in their intensity and range images due to constraints in the imaging detector hardware. To overcome these challenges, this study introduces a novel deep learning image stitching algorithm tailored for single photon imaging. Leveraging the robust feature extraction capabilities of neural networks and the richer feature information present in intensity images, the algorithm stitches range images based on intensity image priors. This innovative approach significantly enhances the spatial resolution and imaging range of single photon LiDAR systems. Simulation and experimental results demonstrate the effectiveness of the proposed method in generating high-quality stitched single-photon intensity images, and the range images exhibit comparable high quality when stitched with prior information from the intensity images.

Velocity-Based Sparse Photon Clustering for Space Debris Ranging by Single-Photon LiDAR

Velocity-Based Sparse Photon Clustering for Space Debris Ranging by Single-Photon LiDAR

High-resolution 3D imaging through dense camouflage nets using single-photon LiDAR

High-resolution 3D imaging through dense camouflage nets using single-photon LiDAR

High-resolution single-photon LiDAR without range ambiguity using hybrid-mode imaging [Invited

High-resolution single-photon LiDAR without range ambiguity using hybrid-mode imaging [Invited

Target Detection in Single-Photon Lidar Using CNN Based on Point Cloud Method

To enhance the detection capability of weak targets and reduce the dependence of single-photon lidar target detection on the number of the time-correlated single-photon counting detection cycles, a convolutional neural network (CNN) based on the point cloud (CNN-PC) method is proposed in this paper for detecting targets in single-photon lidar. This approach utilizes the exceptional feature extraction capabilities offered by CNN. The CNN-PC method utilizes the feature extraction module of the trained CNN to simultaneously extract features from two-dimensional point cloud slices. Subsequently, it combines these features and feeds them into the classification module of the trained CNN for final target detection. By training the CNN using point cloud slices generated with a minimal number of detection cycles and employing a parallel structure to extract features from multiple point cloud slices, the CNN-PC method exhibits remarkable flexibility in adapting to varying numbers of detection cycles. Both simulation and experimental results demonstrate that the CNN-PC method outperforms the classical constant false alarm rate method in terms of the target detection probability at the same signal-to-noise ratio and in terms of the imaging rate and error rate at the same number of detection cycles.