Long-range Detection Research Articles

A Long-Range Lidar Optical Collimation System Based on a Shaped Laser Source

Semiconductor near-infrared lasers have been widely used in lidar systems. However, various source types have different shapes and divergence angles, causing more difficulties for long-distance detection. In this paper, an optical collimation system is designed for a long-range lidar system with a shaped laser source (the wavelength is 905 nm, the emitted spot size is 50 µm long by 10 µm wide, and the divergence angles are 33°and 15°, respectively, which are unconventional). On the basis of the traditional method of aspheric lens setting, a pair of asymmetric aspherical lenses were designed using an extended polynomial. The simulation results show that the spot shapes are all close to circular from 100 mm to 30 m and the spot size always remains the same value. The corrected optical system is put into the designed lidar system for verification. It results show that the average divergence angles in the long and short axis directions are 0.06°and 0.07°, which satisfy the project requirements. This optical system designed provides a collimation scheme and expands the application of vehicle-mounted lidar in the field of long-range detection.

Low-Power Field-Deployable Interdigital Transducer-Based Scanning Laser Doppler Vibrometer for Wall-Thinning Detection in Plates

Lamb waves have become a focal point in ultrasonic testing owing to their potential for long-range and inaccessible detection. However, accurately estimating the flaws in plates using Lamb waves remains challenging because of scattering, mode conversion, and dispersion effects. Recent advances in laser ultrasonic wave techniques have introduced innovative visualization methods that exploit the dispersion effect of Lamb waves to visualize defects via, for example, acoustic wavenumber spectroscopy. In this study, we developed an interdigital transducer (IDT)-based scanning laser Doppler vibrometer (SLDV) system without a power amplifier using a low-power IDT fabricated from lead magnesium niobate–lead zirconate titanate single crystals. To validate the proposed low-power IDT-based SLDV, four different defective plates were measured for defects. A comparison between a conventional IDT-based SLDV, a dry-coupled IDT-based SLDV, and the proposed method demonstrated that the latter is highly reliable for measuring thin plate defects.

Depth-prior-based range-gated 3D imaging method: integration of optical property into visual learning

3D Range-gated Imaging (3DRGI) has great potential for long-range detection in adverse weather conditions. Recently, vision-guided 3DRGI has brought new perspectives to this area as it overcomes hardware limitations and greatly increases flexibility. However, existing vision-guided methods do not consider the optical properties of range-gated imaging, which results in low accuracy. This paper proposes a depth-prior-based 3DRGI method to combine the advantages of optical-based and vision-guided methods. In this method, depth-prior is firstly deduced from principles of range-gated imaging and provides effective depth signals. Then, adaptive depth intervals are estimated using statistical methods, and a depth-prior-guided loss function is designed. The integration of the depth-prior-guided loss function within a vision-guided model enables focused attention on pixels with depth estimations that are inconsistent with the depth prior, thereby refining the overall accuracy of the depth map. To prove the feasibility of the proposed method, comparison experiments and ablation studies have been performed. The results show that the root mean square error improved by more than 12% under adverse weather conditions.

Optical signal characteristics analysis of atmospheric disturbance density fields generated by high-speed aircraft

Aircraft disturbs the adjacent atmospheric environment in flight, forming spatial distribution features of atmospheric density that differ from the natural background, which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft. In this paper, the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed, and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed. By modeling the spatial distribution of the disturbance density field, the scattered echo signal images of active light towards the disturbance field at long distance are simulated. On this basis, the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed. The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images. The disturbance signal can be further highlighted by differential processing of the adjacent scattering images. As the distance behind the aircraft increases, the diffusion range of the disturbance signal increases, and the signal intensity and contrast with the background decrease. Under the ground-based observation conditions of the aircraft at a height of 10000 m, a Mach number of 1.6, and a detection distance of 100 km, the contrast between the disturbance signal and the background was 30 dB at a distance of one time from the rear of the fuselage, and the diffusion diameter of the disturbance signal was 50 m. At a distance eight times the length of the aircraft, the contrast decreased to 10 dB, and the diameter increased to 290 m. The contrast was reduced to 3 dB at a distance nine times the length of the aircraft, and the diameter was diffused to 310 m. These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.

Active Region Mode Control for High-Power, Low-Linewidth Broadened Semiconductor Optical Amplifiers for Light Detection and Ranging.

This paper introduces a semiconductor optical amplifier (SOA) with high power and narrow linewidth broadening achieved through active region mode control. By integrating mode control with broad-spectrum epitaxial material design, the device achieves high gain, high power, and wide band output. At a wavelength of 1550 nm and an ambient temperature of 20 °C, the output power reaches 757 mW when the input power is 25 mW, and the gain is 21.92 dB when the input power is 4 mW. The 3 dB gain bandwidth is 88 nm, and the linewidth expansion of the input laser after amplification through the SOA is only 1.031 times. The device strikes a balance between high gain and high power, offering a new amplifier option for long-range light detection and ranging (LiDAR).

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.

NeuroCamTags: Long-Range, Battery-free, Wireless Sensing with Neuromorphic Cameras

In this research, we introduce NeuroCamTags, a battery-free platform designed to detect a range of rich human interactions and activities in entire rooms and floors without the need for batteries. The NeuroCamTag system comprises low-cost tags that harvest ambient light energy and utilize high-frequency modulation of light-emitting diodes (LEDs) for wireless communication. These visual signals are captured by an available neuromorphic camera, which boasts temporal resolution and frame rates an order of magnitude higher than those of conventional cameras. We present an event processing pipeline that allows simultaneous localization and identification of multiple unique tags. NeuroCamTags offer a wide range of functionalities, providing battery-free wireless sensing for various physical stimuli, including changes in temperature, contact, button presses, key presses, and even sound cues. Our empirical evaluations demonstrate impressive accuracy at long ranges up to 200 feet. In addition to these findings, we consider a range of applications such as battery-free input devices, tracking of human movement, and long-range detection of human activities in various environments such as kitchens, workshops, etc. By reducing reliance on batteries, NeuroCamTags promotes eco-friendliness and opens doors to exciting possibilities in smart environment technology.

Long-Range Imaging of Alpha Emitters Using Radioluminescence in Open Environments: Daytime and Night-Time Applications.

Alpha emitters like plutonium pose severe health risks when ingested, damaging DNA and potentially causing cancer. Traditional detection methods require proximity within millimeters of the contamination source, presenting safety risks and operational inefficiencies. Long-range detection through alpha radioluminescence (RL) offers a promising alternative. However, most of the previous experiments have been carried out under controlled conditions that preclude the overwhelming effect of ambient light. This study demonstrates the successful detection of a 3 MBq alpha emitter in an open environment using a compact alpha camera. This camera incorporates a deep-cooled CCD and a low f-number lens system designed to minimize the blue shift effects of filters. Night-time imaging was achieved with a dual-filter system using a sandwich filter assembly centered at 337 nm and 343 nm for capturing alpha RL and subtracting background light, respectively. At night, the alpha source was detected from 1 m away within one minute, and the lowest detection limit can be calculated as 75 kBq. The system was also evaluated under simulated urban lighting conditions. For daytime imaging, a stack of tilted 276 nm short pass filters minimized sunlight interference, enabling the detection of the alpha source at 70 cm within 10 min under indirect sunlight. This research highlights the viability of long-range optical detection of alpha emitters for environmental monitoring in real-world settings.

A Non-Source Optical Fiber Sensor for Multi-Point Methane Detection.

Fast, accurate, real-time measurement of gas concentration is an important task for preventing coal mining disasters. In order to develop an accurate monitoring method for methane gas concentration at different locations in a mine environment, a non-source optical fiber sensor for multi-point methane detection has been developed in this paper. A 16-channel fiber splitter and a multi-channel time-sharing acquisition module are employed within the sensor, enabling simultaneous detection of methane gas at 16 points by a host. Furthermore, the methane sensors are connected to the monitoring host via an all-optical method, achieving non-source and long-range detection of methane. To assess the performance of the methane gas sensor, experiments were conducted to evaluate its detection range, response time, and stability. The results indicated that the average detection error was approximately 1.84% across the full range, and the response time did not exceed 10 s. The minimum detection limit of the sensor, as determined by the 1σ criteria, was obtained as 58.42 ppm. Additionally, the concentrations of methane gas measured at varying distances (1 km, 2 km, 5 km) were found to be essentially consistent over an extended period. These results suggest that the development of this non-source optical fiber sensor holds significant potential for providing a method for mine environment, multi-point online methane gas detection.

A study on the wall effect of the Spherical Proportional Counter for long-range particle detection

A study on the wall effect of the Spherical Proportional Counter for long-range particle detection

Reconstruction algorithm of Gm-APD LiDAR based on synchronized pseudo-random time coding

Efficient photon counting imaging of long-range targets is challenging, especially in low signal-to-back ratio (SBR) situations. In this paper, a synchronized pseudo-random time coding detection method for Geiger-mode avalanche photodiode (Gm-APD) light detection and ranging (LiDAR) is proposed, which realizes Gm-APD LiDAR long-range detection by combining pseudo-random time coding and time of flight, and the multiple peaks are used to de-noise the image at the image level. Scenes of building with different SBR are simulated with Monte Carlo methods, and the effect of coding length on algorithm performance is discussed and compared with the performance of non-coding algorithms. The high performance of the proposed algorithm is verified by field experiments in low SBR scenes. Moreover, the proposed algorithm improves the target reconstruction correctness ratio by 70% over the other non-coding algorithms to 83% in the scene of building with SBR of 0.015, and has the smallest root mean square error (RMSE) of the distance. It improves the target integrity and provides the possibility of practical application of LiDAR target detection in very low SBR and long-range conditions, and even more support for target feature extraction and target recognition.

Strategy of Russian Military Cooperation with the Countries of the Arab Region

The article undertakes an analysis of the prospective domains of Russian military collaboration with Arab countries against the backdrop of geopolitical uncertainties. Despite maintaining its position as the second-largest arms exporter globally, Russia has experienced a proportional decline in its share of the global arms market. Nevertheless, a discernible uptick in the trade volume of conventional weaponry, coupled with cyclical enhancements in the tactical and technical attributes of military assets, reflects positive momentum in this sector. Amid prevailing external constraints and a noticeable scarcity of contemporary Russian weaponry in burgeoning segments of the arms market, Russia necessitates resolute and equitable measures to uphold and fortify collaborative ties with partner nations. Within this context, particular emphasis is placed on Russia's strategic military cooperation with Arab states, given the region's status as a focal point of geopolitical interests for major economies and a primary destination for arms imports.The article scrutinizes Russia's competitive standing in the arms and military equipment markets of the Arab world, acknowledging the region's significance as a pivotal importer of such goods. Employing a multifaceted evaluation encompassing absolute and relative statistical metrics and derived indices, the study assesses the region's role in global arms procurement. Moreover, it offers an appraisal of the existing state and potential avenues for enhancing the efficacy of the Russian-Arab military-technical collaboration framework. The discourse delves into the transformative prospects of this partnership within the framework of networkcentric warfare concepts. Furthermore, the article elucidates the principal risks inherent in military cooperation among nations at present, highlighting concerns regarding the possible manipulation of military-technical cooperation mechanisms to serve as leverage in furthering the geopolitical aspirations of specific stakeholders.In conclusion, the study posits forward-looking trajectories for bolstering bilateral relations, both sectorally and geographically. These include initiatives such as expanding licensing, leasing, offset transactions, and technology transfer initiatives; advocating for fifth-generation fighter aircraft and long-range radar detection and control systems, leveraging Industry 4.0 technologies; and prioritizing the training of foreign specialists.

Far3D: Expanding the Horizon for Surround-View 3D Object Detection

Recently 3D object detection from surround-view images has made notable advancements with its low deployment cost. However, most works have primarily focused on close perception range while leaving long-range detection less explored. Expanding existing methods directly to cover long distances poses challenges such as heavy computation costs and unstable convergence. To address these limitations, this paper proposes a novel sparse query-based framework, dubbed Far3D. By utilizing high-quality 2D object priors, we generate 3D adaptive queries that complement the 3D global queries. To efficiently capture discriminative features across different views and scales for long-range objects, we introduce a perspective-aware aggregation module. Additionally, we propose a range-modulated 3D denoising approach to address query error propagation and mitigate convergence issues in long-range tasks. Significantly, Far3D demonstrates SoTA performance on the challenging Argoverse 2 dataset, covering a wide range of 150 meters, surpassing several LiDAR-based approaches. The code is available at https://github.com/megvii-research/Far3D.

A laser field synchronous scanning imaging system for underwater long-range detection

A novel underwater optical imaging system is proposed to improve underwater image degradation, which is severely deteriorated due to absorption and scattering under artificial illumination. The light source employs a line laser with 520 nm wavelength and a mirror galvanometer that controls the one-dimension scanning of the laser beam to form a planar illumination light field. The camera adopts an sCMOS image sensor with an electronic rolling shutter exposure pattern, compressing the camera’s instantaneous field of view(IFOV) into a narrow strip by reducing the exposure time. Meanwhile, the system controls the light source to irradiate the IFOV of the camera only synchronously, which significantly compressed the overlapping volume of the light path between the camera and the light source so that the backscattered light captured by the camera was dramatically reduced. Experimental results at different turbidity and distances in pool environments show that the laser field synchronous scanning imaging system obtains excellent performance in contrast ratio (CR) and contrast signal-to-noise ratio (CSNR). In the turbidity experiment, the peak CR of the target image improved by a factor of 3.88, and the peak CSNR improved by a factor of 2.33 in 19.6 FTU. In the distance experiment, the peak CR of the target image improved by a factor of 5.57, and the peak CSNR improved by a factor of 1.67 at a distance of 13 m, achieving a clear imaging range of 15 m. This system substantially diminishes the impact of light backscattering on imaging quality, which effectively facilitates further development of the underwater task and renders the system highly applicable in underwater imaging.

Detection and Identification of Non-cooperative UAV Using a COTS mmWave Radar

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This article presents mmHawkeye, a passive approach for non-cooperative UAV detection and identification with a commercial off-the-shelf millimeter wave (mmWave) radar. mmHawkeye does not require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low signal-to-noise ratio and uncertain reflected signals from the UAV. After analyzing the theoretical model of the PMM feature, mmHawkeye can further track the UAV’s position containing range, azimuth and altitude angle with dynamic programming and particle filtering and then identify it with a Long Short-Term Memory–based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80 m.

Investigation into the Co-Phase Detection Methodology for Segmented Plane Mirrors Utilizing Grazing Incidence Interferometry

Segmented plane mirrors constitute a crucial component in the self-aligned detection process for large-aperture space optical imaging systems. Surface shape errors inherent in segmented plane mirrors primarily manifest as tilt errors and piston errors between sub-mirrors. While the detection and adjustment techniques for tilt errors are well-established, addressing piston errors poses a more formidable challenge. This study introduces a novel approach to achieve long-range, high-precision, and efficient co-phase detection of segmented plane mirrors by proposing a segmented plane mirror shape detection method based on grazing incidence interferometry. This method serves to broaden the detection range of piston errors, mitigate the issue of the 2π ambiguity resulting from piston errors in co-phase detection, and extend the detection capabilities of the interferometer. By manipulating the incident angle of the interferometer, both rough and precise adjustments of the segmented plane mirrors can be effectively executed.

Fully Sparse Fusion for 3D Object Detection.

Currently prevalent multi-modal 3D detection methods rely on dense detectors that usually use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV feature maps is quadratic to the detection range, making it not scalable for long-range detection. Recently, LiDAR-only fully sparse architecture has been gaining attention for its high efficiency in long-range perception. In this paper, we study how to develop a multi-modal fully sparse detector. Specifically, our proposed detector integrates the well-studied 2D instance segmentation into the LiDAR side, which is parallel to the 3D instance segmentation part in the LiDAR-only baseline. The proposed instance-based fusion framework maintains full sparsity while overcoming the constraints associated with the LiDAR-only fully sparse detector. Our framework showcases state-of-the-art performance on the widely used nuScenes dataset, Waymo Open Dataset, and the long-range Argoverse 2 dataset. Notably, the inference speed of our proposed method under the long-range perception setting is 2.7× faster than that of other state-of-the-art multimodal 3D detection methods. Code is released at https://github.com/BraveGroup/FullySparseFusion.

Long-range UWOC system-based high-sensitivity detection techniques

Long-range UWOC system-based high-sensitivity detection techniques

CNN Based Rat Detection using Thermal Sensor

The detection and control of rats in commercial buildings and industries are crucial issues due to the damage they can cause to Godowns and equipment. Traditional methods of rat detection and control can be time-consuming and expensive and may not always be effective. This has brought the exploration of machine learning-based approaches, which can provide more accurate and efficient detection of rats. One such approach is the use of thermal sensors in conjunction with machine learning algorithms to detect rats in commercial buildings, industries, etc. Thermal sensors can detect the body heat of rats, and machine learning algorithms can be trained to analyze thermal data and accurately identify the presence of rats. This approach has several advantages over traditional methods, including higher accuracy, long-range and faster detection. The machine learning algorithms used in this approach can be trained using large datasets of thermal images of rats, which can be obtained using thermal cameras

Long-range and high-precision localization method for underwater bionic positioning system based on joint active–passive electrolocation

Active electrolocation organ of weakly electric fish act as a proximity detection system with high accuracy in recognizing object parameters such as size and shape. In contrast, some fish with passive electrolocation organ are able to detect objects at a greater range. This paper proposes a joint active–passive electrolocation algorithm for long-range and high-precision underwater localization, inspired by the active and passive electroreceptive organs of fish. The study begins by designing a large experimental platform for the underwater localization system to investigate the response of underwater objects to active and passive electric fields. Based on the response, the paper proposes separate underwater active and passive electrolocation algorithms, which are then combined to form a joint algorithm. Experimental results demonstrate that the proposed algorithm achieves high localization accuracy and long detection distance. The joint active–passive electrolocation algorithm has potential applications in submarine resource exploration, underwater robotics, and maritime military projects, while also providing new ideas for future research on long-range underwater object detection and identification based on electrolocation.