Search Results (47)

To address the issue of the Geiger-mode Avalanche Photodiode (GM-APD) LiDAR’s echo being easily overwhelmed by strong noise under low signal-to-background ratio conditions, leading to degraded performance in range parameter estimation and low target restoration accuracy, this paper proposes a range parameter estimation method based on multi-scale tracking differentiator. This method eliminates the reliance on complex statistical models and spatial prior information and uses a nonlinear dynamic tracking mechanism to extract target information. Firstly, a dual-scale tracking differentiator system is constructed, where the large-scale factor captures the transient mutation characteristics of the echo signal, and the small-scale factor estimates the overall evolution trend of the signal. Secondly, the difference between the dual-scale outputs is obtained to acquire the residual signal, and nonlinear mapping enhancement is performed in combination with the photon trigger probability characteristics to deeply suppress noise and highlight the target peak. Finally, the peak threshold method is used to complete the range calculation. Simulation results show that when the SBR = 0.06, compared with typical methods such as the neighborhood kernel density method, the method in this paper is more robust, the root mean square error of the range estimation is reduced by at least 38.35%, and the target restoration degree is improved by at least 19.99%, which provides a highly efficient way for high-fidelity single-photon three-dimensional imaging and target detection under strong noise. Full article

This research investigates a vertically structured Ge-on-Si avalanche photodetector (APD) fabricated in a separate absorption, charge, and multiplication configuration. The application of ramp gating enables reverse bias beyond the punch-through voltage, allowing the device to operate in linear avalanche mode. A significant dark avalanche current is observed under steady conditions, exhibiting linear multiplication approximately proportional to the input gating and thermal generation rate. Notably, this linear behavior persists even at voltages beyond the Geiger mode. The observed results are attributed to Ge/Si interface traps caused by the 4.18% lattice mismatch and deep-level traps introduced during fabrication. Under 1550 nm short-wave infrared normal-incidence pulsed illumination, the device exhibits negative differential resistance, attributed to illumination-induced self-quenching of electric field in multiplication region and modification of the barrier at the Ge/Si interface. A light-induced slow transient decrease in the absolute dark-state current is followed by a sustained inverse quenching effect, restoring the large dark-state current. These findings offer insights into the dynamic behavior of Ge-on-Si APDs, with potential implications for advanced optoelectronic applications. Full article

Buildings in hot–humid climates experience increasing thermal stress due to urban heat islands and climate change, leading to greater reliance on air conditioning. Passive cooling is therefore a crucial low-carbon strategy for maintaining thermal comfort. This paper reviews thermal comfort ranges and passive-cooling techniques across Köppen–Geiger hot–humid climate classes. A two-stage approach was adopted: thermal comfort data from 35 field studies were analyzed by climate class and ventilation mode, while more than 70 application studies were qualitatively reviewed to assess mechanisms, performance, and climate suitability. The results indicate that occupants in hot–humid areas exhibit broad thermal tolerance, particularly in naturally ventilated buildings, with neutral temperatures ranging from 19.5 °C in humid subtropical climates to 36.3 °C in tropical savanna climates. Natural ventilation is the most widely applicable passive-cooling strategy, but its effectiveness depends on integration with climate-responsive measures. Ventilation, combined with solar protection and courtyards, is most effective in Af and Am climates, whereas shading, solar chimneys, evaporative cooling, night ventilation, thermal mass, and phase-change materials provide greater benefits in Aw, Cfa, and Cwa climates. However, no single strategy is sufficient across all climates. The review provides climate-specific guidance for designing low-carbon, thermally resilient buildings in hot–humid regions. Full article

To address the challenge of accurately reconstructing multi-depth targets using single-photon Light Detection and Ranging (LiDAR) under few-frame conditions in high-precision applications such as autonomous driving perception, remote sensing, and military reconnaissance, this paper proposes a computational imaging method named the Jensen–Shannon Divergence Weighted Pixel Fusion Constant False Alarm Rate (JSWPF-CFAR) approach. First, the proposed method utilizes the Jensen–Shannon (JS) divergence to characterize the statistical similarity between adjacent pixels, thereby constructing adaptive weights to achieve the effective fusion of echo signals. The key innovation lies in the formulation of a JS divergence-based weighting factor, which fully exploits the inherent spatial correlation within 3D target structures to optimize the pixel fusion process and enhance the signal statistics of target echoes. Subsequently, a CFAR detection model tailored for Geiger-mode Avalanche Photodiode (GM-APD) multi-depth echo signals is constructed to estimate the noise photon count within a local sliding window; this estimate is then used to calculate a photon counting threshold for identifying and extracting high-confidence target intervals. Finally, a peak-picking method is employed to perform the 3D reconstruction of multi-depth targets. Compared with existing techniques such as matched filtering and Reversible Jump Markov Chain Monte Carlo (RJMCMC), the proposed method exhibits superior reconstruction quality under few-frame and low Signal-to-Background Ratio (SBR) conditions. The experimental results demonstrate that the proposed method achieves an improvement in target restoration degree (RD) of at least 21.16% and a relative variance (Var) optimization of at least 62.90% over the matched filtering and RJMCMC baselines. These results indicate that the proposed approach effectively enhances the multi-depth estimation performance of single-photon LiDAR in complex scenes. Full article

This work presents the development and experimental characterization of a compact ultraviolet (UV) telescope based on silicon photomultipliers (SiPMs) designed for the detection of faint atmospheric optical tracks. Such transient optical phenomena include meteors, transient luminous events (TLEs), space debris reentries, and other faint atmospheric emissions. Nuclearite-induced atmospheric emission is considered as a benchmark case for evaluating the expected signal levels of rare luminous track events. We detail the fabrication, assembly, and testing of the SiPM sensor array, comprising parallel Geiger-mode avalanche diodes with high fill factor and photon detection efficiency, alongside custom readout electronics using self-triggering ASICs, precision optical components, and a stable mechanical mount. This photon-counting telescope provides a compact and mechanically robust alternative to conventional PMT-based systems, with demonstrated capability for detecting low-light atmospheric tracks under controlled laboratory conditions. Full article

Geiger-mode avalanche photodiode (GM-APD) array light detection and ranging (LiDAR) has significant advantages in low-light scenes due to its single-photon-level detection sensitivity. However, it is susceptible to noise, which leads to a decrease in target localization accuracy. Traditional methods rely on long-term accumulation to distinguish signal photons from noise photons, making it difficult to achieve efficient processing, especially in scenarios with sparse echo photons and low signal-to-noise ratio (SNR), where performance is limited. To quickly and accurately obtain three-dimensional (3D) information of the target under such extreme conditions, this paper proposes a method for target detection and temporal window depth estimation based on intensity information guidance. First, noise suppression is performed on the intensity image according to its statistical characteristics, and an outlier detection mechanism based on neighborhood sparsity is introduced to remove outliers, thereby completing the target detection. Next, by exploiting the spatial continuity and reflectivity similarity of the target, local fusion of photon data within the target neighborhood is performed to construct highly consistent “superpixels”. Finally, according to the distribution difference between signal photons and noise photons on the time axis, temporal window screening is applied to the superpixels to extract depth information, and empty pixels are filled using a convex segmentation method to achieve depth estimation of the target. The experimental results demonstrate that under conditions of low photon counts and strong noise, the proposed method significantly outperforms traditional and existing methods in target recovery and depth estimation by effectively integrating target intensity information. Furthermore, this method achieves faster reconstruction speed, enabling high-precision and high-efficiency 3D target reconstruction. Full article

This study evaluated airborne laser scanning (ALS) as a large-scale tool for forest carbon quantification by comparing ALS-derived estimates with traditional field sampling across multiple forest strata. Above-ground biomass was estimated using two different, commonly used equations, while below-ground biomass was derived from peer-reviewed root-to-shoot ratios. ALS and field estimates differed across forest strata and carbon pools: ALS detected higher live tree carbon in harvested areas—capturing residual trees often missed in traditional cruises—but underestimated dead wood carbon, relative to field-based methods. Consistent differences were also observed between biomass equations, with Woodall estimates being 12.8% and 16.7% lower than Jenkins estimates for ALS and field methods, respectively. The study further incorporated soil organic carbon (SOC) and carbon dating data, providing additional insight into subsurface carbon stocks and the temporal dynamics of forest carbon pools. Overall, ALS proved to be an efficient, repeatable, and scalable method for carbon assessment, offering clear advantages in monitoring carbon flux over time when integrated with forest management protocols. Although further research is needed to refine biomass equations and explore emerging technologies such as Geiger Mode LiDAR, ALS has strong potential to enhance forest carbon crediting processes and support climate change mitigation goals. Full article

This study proposes a complete data-processing framework for Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (LiDAR) echo signals. It investigates the feasibility of classifying target and background noise using machine learning. Four feature processing schemes were first compared, among which the PNT strategy (Principal Component Analysis without tail features) was identified as the most effective and adopted for subsequent analysis. Based on this framework, nine models derived from six baseline algorithms—Decision Trees (DTs), Support Vector Machines (SVMs), Backpropagation Neural Networks (NN-BPs), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and k-Nearest Neighbors (KNN)—were systematically assessed under Monte Carlo simulations with varying echo signal-to-noise ratio (ESNR) and statistical frame number (SFN) conditions. Model performance was evaluated using eight metrics: accuracy, precision, recall, FPR, FNR, F1-score, Kappa coefficient, and relative change percentage (RCP). Monte Carlo simulations were employed to generate datasets, and Principal Component Analysis (PCA) was applied for feature extraction in the machine learning training process. The results show that LDA achieves the shortest training time (0.38 s at SFN = 20,000), DT maintains stable accuracy (0.7171–0.8247) across different SFNs, and NN-BP models perform optimally under low-SNR conditions. Specifically, NN-BP-3 achieves the highest test accuracy of 0.9213 at SFN = 20,000, while NN-BP-2 records the highest training accuracy of 0.9137. Regarding stability, NN-BP-3 exhibits the smallest RCP value (0.0111), whereas SVM-3 yields the largest (0.1937) at the same frame count. In conclusion, NN-BP-based models demonstrate clear advantages in classifying sky-background noise. Building on this, we design a ResNet based on NN-BP, which achieves further accuracy gains over the best baseline at 400, 2000, and 20,000 frames—12.5% (400), 9.16% (2000), and 2.79% (20,000)—clearly demonstrating the advantage of NN-BP for GM-APD LiDAR signal classification. This research thus establishes a novel framework for GM-APD LiDAR signal classification, provides the first systematic comparison of multiple machine learning models, and highlights the trade-off between accuracy and computational efficiency. The findings confirm the feasibility of applying machine learning to GM-APD data and offer practical guidance for balancing detection performance with real-time requirements in field applications. Full article

Laser ranging technology holds a key position in the military, aerospace, and industrial fields due to its high precision and non-contact measurement characteristics. As a core component, the performance of the photon detector directly determines the ranging accuracy and range. This paper systematically reviews the technological development of photonic detectors for laser ranging, with a focus on analyzing the working principles and performance differences of traditional photodiodes [PN (P-N junction photodiode), PIN (P-intrinsic-N photodiode), and APD (avalanche photodiode)] (such as the high-frequency response characteristics of PIN and the internal gain mechanism of APD), as well as their applications in short- and medium-range scenarios. Additionally, this paper discusses the unique advantages of special structures such as transmitting junction-type and Schottky-type detectors in applications like ultraviolet light detection. This article focuses on photon counting technology, reviewing the technological evolution of photomultiplier tubes (PMTs), single-photon avalanche diodes (SPADs), and superconducting nanowire single-photon detectors (SNSPDs). PMT achieves single-photon detection based on the external photoelectric effect but is limited by volume and anti-interference capability. SPAD achieves sub-decimeter accuracy in 100 km lidars through Geiger mode avalanche doubling, but it faces challenges in dark counting and temperature control. SNSPD, relying on the characteristics of superconducting materials, achieves a detection efficiency of 95% and a dark count rate of less than 1 cps in the 1550 nm band. It has been successfully applied in cutting-edge fields such as 3000 km satellite ranging (with an accuracy of 8 mm) and has broken through the near-infrared bottleneck. This study compares the differences among various detectors in core indicators such as ranging error and spectral response, and looks forward to the future technical paths aimed at improving the resolution of photon numbers and expanding the full-spectrum detection capabilities. It points out that the new generation of detectors represented by SNSPD, through material and process innovations, is promoting laser ranging to leap towards longer distances, higher precision, and wider spectral bands. It has significant application potential in fields such as space debris monitoring. Full article

The Geiger-Mode Avalanche Photodiode (Gm-APD) LiDAR system demonstrates high-precision detection capabilities over long distances. However, the detection of occluded small objects at long distances poses significant challenges, limiting its practical application. To address this issue, we propose a multi-scale spatio-temporal object detection network (MSTOD-Net), designed to associate object information across different spatio-temporal scales for the effective detection of occluded small objects. Specifically, in the encoding stage, a dual-channel feature fusion framework is employed to process range and intensity images from consecutive time frames, facilitating the detection of occluded objects. Considering the significant differences between range and intensity images, a multi-scale context-aware (MSCA) module and a feature fusion (FF) module are incorporated to enable efficient cross-scale feature interaction and enhance small object detection. Additionally, an edge perception (EDGP) module is integrated into the network’s shallow layers to refine the edge details and enhance the information in unoccluded regions. In the decoding stage, feature maps from the encoder are upsampled and combined with multi-level fused features, and four prediction heads are employed to decode the object categories, confidence, widths and heights, and displacement offsets. The experimental results demonstrate that the MSTOD-Net achieves mAP₅₀ and mAR₅₀ scores of 96.4% and 96.9%, respectively, outperforming the state-of-the-art methods. Full article

Woody debris (WD) is an important element in forest ecosystems. It provides critical habitats for plants, animals, and insects. It is also a source of fuel contributing to fire propagation and sometimes leads to catastrophic wildfires. WD inventory is usually conducted through field surveys using transects and sample plots. Light Detection and Ranging (LiDAR) point clouds are emerging as a valuable source for the development of comprehensive WD detection strategies. Results from previous LiDAR-based WD detection approaches are promising. However, there is no general strategy for handling point clouds acquired by different platforms with varying characteristics such as the pulse repetition rate and sensor-to-object distance in natural forests. This research proposes a general and adaptive morphological WD detection strategy that requires only a few intuitive thresholds, making it suitable for multi-platform LiDAR datasets in both plantation and natural forests. The conceptual basis of the strategy is that WD LiDAR points exhibit non-planar characteristics and a distinct intensity and comprise clusters that exceed a minimum size. The developed strategy was tested using leaf-off point clouds acquired by Geiger-mode airborne, uncrewed aerial vehicle (UAV), and backpack LiDAR systems. The results show that using the intensity data did not provide a noticeable improvement in the WD detection results. Quantitatively, the approach achieved an average recall of 0.83, indicating a low rate of omission errors. Datasets with a higher point density (i.e., from UAV and backpack LiDAR) showed better performance. As for the precision evaluation metric, it ranged from 0.40 to 0.85. The precision depends on commission errors introduced by bushes and undergrowth. Full article

Array Geiger-mode avalanche photodiode (GM-APD) Light Detection and Ranging (LiDAR) has the advantages of high sensitivity and long imaging range. However, due to its operating principle, GM-APD LiDAR requires processing based on multiple-laser-pulse data to complete the target reconstruction. Therefore, the influence of the device’s movement or scanning motion during GM-APD LiDAR imaging cannot be ignored. To solve this problem, we designed a reconstruction method based on coordinate system transformation and the Position and Orientation System (POS). The position, attitude, and scanning angles provided by POS and angular encoders are used to reduce or eliminate the dynamic effects in multiple-laser-pulse detection. Then, an optimization equation is constructed based on the negative-binomial distribution detection model of GM-APD. The spatial distribution of photons in the scene is ultimately computed. This method avoids the need for field-of-view registration, improves data utilization, and reduces the complexity of the algorithm while eliminating the effect of LiDAR motion. Moreover, with sufficient data acquisition, this method can achieve super-resolution reconstruction. Finally, numerical simulations and imaging experiments verify the effectiveness of the proposed method. For a 1.95 km building scene with SBR ~0.137, the 2 × 2-fold super-resolution reconstruction results obtained by this method reduce the distance error by an order of magnitude compared to traditional methods. Full article

In order to solve the problem of detecting, tracking and estimating the size of “low, slow and small” targets (such as UAVs) in the air, this paper designs a single-photon LiDAR imaging system based on Geiger-mode Avalanche Photodiode (Gm-APD). It improves the Mean-Shift algorithm and proposes an automatic tracking method that combines the weighted centroid method to realize target extraction, and the principal component analysis (PCA) method of the adaptive rotating rectangle is realized to fit the flight attitude of the target. This method uses the target intensity and distance information provided by Gm-APD LiDAR. It addresses the problem of automatic calibration and size estimation under multiple flight attitudes. The experimental results show that the improved algorithm can automatically track the targets in different flight attitudes in real time and accurately calculate their sizes. The improved algorithm is stable in the 1250-frame tracking experiment of DJI Elf 4 UAV with a flying speed of 5 m/s and a flying distance of 100 m. Among them, the fitting error of the target is always less than 2 pixels, while the size calculation error of the target is less than 2.5 cm. This shows the remarkable advantages of Gm-APD LiDAR in detecting “low, slow and small” targets. It is of practical significance to comprehensively improve the ability of UAV detection and C-UAS systems. However, the application of this technology in complex backgrounds, especially in occlusion or multi-target tracking, still faces certain challenges. In order to realize long-distance detection, further optimizing the field of view of the Gm-APD single-photon LiDAR is still a future research direction. Full article

With the wide application of UAVs in modern intelligent warfare as well as in civil fields, the demand for C-UAS technology is increasingly urgent. Traditional detection methods have many limitations in dealing with “low, slow, and small” targets. This paper presents a pure laser automatic tracking system based on Geiger-mode avalanche photodiode (Gm-APD). Combining the target motion state prediction of the Kalman filter and the adaptive target tracking of Camshift, a Cam–Kalm algorithm is proposed to achieve high-precision and stable tracking of moving targets. The proposed system also introduces two-dimensional Gaussian fitting and edge detection algorithms to automatically determine the target’s center position and the tracking rectangular box, thereby improving the automation of target tracking. Experimental results show that the system designed in this paper can effectively track UAVs in a 70 m laboratory environment and a 3.07 km to 3.32 km long-distance scene while achieving low center positioning error and MSE. This technology provides a new solution for real-time tracking and ranging of long-distance UAVs, shows the potential of pure laser approaches in long-distancelow, slow, and small target tracking, and provides essential technical support for C-UAS technology. Full article

Geiger-mode avalanche photodiode (GM-APD) is a single-photon-detection device characterized by high sensitivity and fast response, which enables it to detect echo signals of distant targets effectively. Given that weak and small targets possess relatively small volumes and occupy only a small number of pixels, relying solely on neighborhood information for target reconstruction proves to be difficult. Furthermore, during long-distance detection, the optical reflection cross-section is small, making signal photons highly susceptible to being submerged by noise. In this paper, a noise fitting and removal algorithm (NFRA) is proposed. This algorithm can detect the position of the echo signal from the photon statistical histogram submerged by noise and facilitate the reconstruction of weak and small targets. To evaluate the NFRA method, this paper establishes an optical detection system for remotely detecting active single-photon weak and small targets based on GM-APD. Taking unmanned aerial vehicles (UAVs) as weak and small targets for detection, this paper compares the target reconstruction effects of the peak-value method and the neighborhood method. It is thereby verified that under the conditions of a 7 km distance and a signal-to-background ratio (SBR) of 0.0044, the NFRA method can effectively detect the weak echo signal of the UAV. Full article