The US Army's laser maneuver short-range air defense system is the world's first officially deployed tactical laser air defense system. It is mainly designed to address the current threats faced by the US Army, such as low to medium altitude drones and mortars. This article reviews the development process, composition and main functions of the system, and summarizes and analyzes the main technical indicators of the system. The paper theoretically calculated the destructive ability of this type of system and analyzed the deployment methods that the system can adopt to effectively respond to the threat of drones in the accompanying air defense operations of the army. The article provides a reference for an understanding of the potential methods and roles that this type of tactical laser air defense system can play in future battlefields.

Multi-band common aperture confocal surface optical system as a result of ‘an optical lens and a detector’ performance is equivalent to the ability of the traditional optical system (two optical lenses with two detectors). Co-calibre confocal plane imaging enables the matching of the field of view with the target information and provides a prerequisite for the subsequent fusion of information in all bands. Based on its small number of optical components and small size, this system has become a new research direction of multi-band optical system. In particular, the annular multi-reflection optical systems, with their small size, occupy an important position in the fields of gun sights, guide heads, and machine vision. Taking the development of multi-band common aperture confocal surface optical system as the main line, the research status of multi-band optical system is analyzed, and various types of multi-band common aperture confocal surface optical system are comprehensively introduced. The characteristics of the optical structure, optical system performance, and the advantages and disadvantages of these optical systems are discussed and compared, which provide a reference for researchers in the field of optical design.

To enhance the reliability of array fiber output lasers, the flexibility and high fault tolerance of the k/n (G) voting model are exploited, and the redundant design of the fiber array is carried out by using a cold reserve unit, so as to achieve the service life enhancement under the premise of controllable laser output power. The k/n∶M (G) cold standby voting system consists of n working units and M cold standby units, and the system works normally when at least k units work properly. In this paper, a 3/5∶2(G) cold standby voting system equivalent model is established by combining a 5-module controllable array fiber laser, the reliability and average lifespan of the fiber laser under different redundancy strategies are solved, and then the voting system is optimized according to the best strategy. Finally, the Monte Carlo simulation test is used to prove the correctness and feasibility of the method.

This paper establishes the theoretical model of 3.5 m-Er∶ZBALN fiber laser and fiber amplifier using dual-wavelength pumping (DWP) technology. The kinetic behavior of the energy level particle number for the laser oscillator and amplifier and the laser power distribution along the fiber length under different fiber parameters are investigated. The effects of fiber length, output reflectivity, and erbium-doping concentration on the 3.5 m laser power are calculated and analyzed. The results show that an optimal range of fiber length and output reflectivity. High erbium-doped fibers require only a lower 976 nm pump power compared to low erbium-doping to achieve an effective 3.5 m laser output. The cross-relaxation process of high doped concentration prevents the quenching behavior. Simulations achieve an efficient amplification of the 3.5 m fiber laser. The numerical simulation results are instructive for the 3.5 m laser oscillator parameter design and high power amplification.

Due to the effect of nonlinear frequency modulation of FMCW laser source, the spectrum of beat frequency appears to be broadened, which reduces spectral resolution, so the nonlinear correction of the light source is a prerequisite for the accurate ranging of this system LIDAR. To avoid the defects of the existing nonlinear correction technology with complex system structure and high measurement cost, a nonlinear correction method of FMCW laser source based on error compensation is proposed in this paper. Through the error between the output signal and the linear regression data, correction data with the opposite trend of change in the output signal frequency is constructed as the modulation signal source of the laser. After several cycles of compensation, the coefficient of determination between the output signal frequency and the linear regression data is gradually improved to more than 0.9995. At last, the effectiveness and feasibility of this method is verified by range measurement experiments.

A method of Euclidean distance clustering of centroids using improved information entropy is proposed to complete the point cloud alignment, for the traditional point cloud alignment method is susceptible to noise, outliers and overlap, and solves the shortcomings such as causing low alignment accuracy and low efficiency. First of all, voxel grid down sampling is performed on the two point clouds to accelerate the efficiency of subsequent processing. Different from Euclidean clustering directly using distance clustering, this method computes the feature values of points. By calculating the information entropy based on the feature vectors, a feature tensor is employed for cluster selection. Subsequently, key points representing each cluster are extracted, and the KD-tree algorithm is employed for point pair searching and correspondence. Utilizing the positional information of corresponding point pairs, an initial transformation matrix is estimated, serving as input for precise registration and providing a favorable initial pose for subsequent refinement. Finally, a bidirectional KD-tree-enhanced point-to-plane ICP algorithm is employed for accurate registration. A road point cloud data with a length of about 300 m is selected for the experiment, and compared with the four methods at an overlap of 10%, the results show that the RMSE of the algorithm is 0.074 m and the overall time consumed by the alignment process is 30.256 seconds, which is higher than the four algorithms in terms of accuracy and efficiency of the alignment.

In response to the inaccuracy of pedestrian feature recognition and the potential for misjudgment due to the lack of height information in detecting pedestrian positions using 2D LiDAR, a method for pedestrian identification based on the joint detection of 2D LiDAR and depth camera is proposed in this paper. Firstly, a Support Vector Machine (SVM) is used to filter out point cloud segments belonging to the pedestrian's leg curve in the LiDAR point cloud data, and determine the pedestrian's position. Additionally, a human body recognition algorithm is applied to the visual images to delineate pedestrians, utilizing the image coordinates of the center point of the outer body box and depth values to calculate pedestrian positions. Finally, the actual pedestrian position is derived through weighted fusion of the pedestrian position information obtained from both LiDAR and the camera. Th experimental results demonstrate that the proposed method greatly reduces the false judgement rate while inheriting the measurement accuracy of LiDAR, which proves the effectiveness of the method.

Ultrashort laser micromachining for optimization involves a number of parameters, and there are often complex relationships between these parameters, resulting in poor optimization performance of the optimization method. For this reason, a mathematical modeling and optimization analysis method for ultrashort laser micromachining is proposed. First of all, the thermal accumulation effect of ultrashort laser micromachining on the surface is analyzed based on the differential equation of heat conduction, and the mathematical model of ultrashort laser micromachining is constructed according to the analysis results. Then, through the mathematical model, the influence of different polishing parameters on the thermal effect and thermal stress in the processing process is analyzed. Finally, the hierarchical structure is adopted to optimize the distribution uniformity, surface layer depth and hardness of the hardened layer, so as to build the mathematical optimization model of ultrashort laser micromachining and achieve the optimization analysis. The experimental results show that the average machining accuracy is as high as 99%, the lowest roughness is only 5%, and the finish and uniformity are more than 90%, which is of good practicability.

In this paper, the detection accuracy of Laser-Induced Breakdown Spectroscopy (LIBS) technology under kHz laser conditions is verified, which is of great significance for the application of LIBS in the large-area, high-efficiency monitoring of laser paint removal processes. Firstly, a high-frequency (kHz) 1064 nm infrared pulsed fiber laser is used to clean the paint layer on aircraft skin. By integrating results from an energy spectrometer and atomic spectral database data, the impact of kHz lasers on the wavelength positions and the peak intensity of LIBS spectral lines is investigated, and the accuracy of LIBS technology is verified during kHz laser de-painting processes. The results indicate that the wavelength position of the kHz peak is shifted towards the blue-light region compared with that of the Hz peak, with a shift no greater than 0.18 nm. The spectral peaks between 380 nm to 425 nm disappear completely, and the signal-to-noise ratio of peaks within the 425 nm to 550 nm range decreases. The spectrometer test results corroborating with kHz LIBS spectra validate the detection accuracy under kHz laser conditions, demonstrating that LIBS technology can be used for monitoring the laser paint removal process on large aircraft skin areas efficiently. The study provides a reference for LIBS monitoring in kHz laser de-painting processes.

3D point cloud registration based on local features is a core problem in the field of computer vision and robotics, and most of the existing 3D local feature descriptors are of floating-point type. In this paper, a binary local feature descriptor, the Binary Depth Image Descriptor (BDIF), is proposed for describing 3D local features, and a registration algorithm based on the BDIF is also put forward for point cloud registration of large scenes. The BDIF encodes the local structure as a bit string based on the distance of the local surface to the projection plane. Specifically, the BDIF descriptor establishes a local reference frame near the keypoints to achieve rotational invariance, and then encodes the spatial information on three orthogonal projection surfaces. After that, binarization is completed based on the thresholding method and the segmentation threshold is determined using the maximum inter-class variance. An efficient point cloud registration algorithm is developed based on BDIF, which employs the adaptive scale Welsch to estimate the spatial variation parameters, and can effectively deal with the point cloud data collected from large scenes. Finally, extensive experiments are conducted on Retrieval and WHU-TLS datasets, respectively, and the experimental results demonstrate the effectiveness and overall superiority of the BDIF and BDIF-based point cloud registration algorithm proposed in this paper.

To evaluate the performance parameters of n-on-p long-wave HgCdTe infrared focal plane array (IRFPA) detector at low luminous flux, a low-luminous test platform is set up in this paper. Firstly, the low luminous flux test platform is introduced and the dark current test results of the devices are analyzed. Then, the blackbody response performance of long-wave HgCdTe infrared detector in a low luminous flux is investigated on the low-luminous flux test platform. At last, the changes of performance parameters such as responsivity and band detectivity under different luminous fluxes are compared and analyzed, and the results of the influence of device operating temperature on device performance are given. The test results show that for the 10.8 m@50 K MCT focal plane detector, the device band detectivity reaches a peak value of 1.5×1012 cm·Hz1/2·W-1 under the conditions of device operating temperature of 50 K and a luminous flux density of 5.8×1014ph·s-1·cm-2, when the detectivity is no longer increased with the increase of integration time. The experimental calculation results and related parameters can provide a reference for the application of the detector.

In this paper, a holographic imaging method in a water mist environment is proposed. Firstly, an infrared laser with a wavelength in the “atmospheric window” is adopted, and the fog environment is simulated through experiments to investigate the effect of different fog concentrations on the holographic imaging effect of infrared light, and set up a visible light digital holography experiment for comparison. By comparing different image quality evaluation parameters of the reconstructed images of infrared light and visible light, the results show that the system can be clearly imaged by infrared light, while the visible light digital holography system has poor imaging performance. In addition, since the holographic reconstruction of infrared light through water mist is seriously affected by the zero level and scattering, the hologram reconstruction obtained in this paper is processed by eliminating the zero level and enhancing Lee filtering to remove the noise, in order to achieve the purpose of improving the image quality.

In the process of infrared target tracking, the imaging area of infrared targets is usually small due to changes in target distance and perspective, resulting in a lack of sufficient pixel information in the infrared image, and making it difficult to accurately extract target features and increasing the difficulty of target tracking. Therefore, a long-distance multi-scale infrared target tracking technology based on anchor boxes is proposed. First of all, the tracking block diagram and the real block diagram are corresponded by encoding to get the coordinate value of the center of the target frame. To ensure the accuracy of the calculation, a feature fusion threshold is set to determine the high overlap between the extracted information and the true information, and the pixel grid is divided according to the edge, center, and vertex coordinates of the anchor box, and a classification output vector is output based on the feature values to complete the infrared target feature extraction. As a result, a loss function is used to provide the category loss of anchor boxes, target boxes, and actual boxes, as well as different category loss functions such as candidate boxes. According to the voxel values of different points in the grid, the candidate box and real box data are compared one by one in the form of coding, and the long-distance multi-scale infrared target tracking is achieved through iteration. The experimental results show that the proposed method has good recognition performance for long-distance multi-scale infrared targets, and the recall curve is basically maintained above 0.9. It indicates that the proposed method has good long-range multi-scale infrared target tracking performance.

Metal additive manufacturing is a rapidly developing high-efficiency material processing technology in recent years. In order to ensure the quality and reliability of fabricated parts, the formation of defects within them that have a significant impact on the mechanical properties of the structure should be avoided. In this paper, the application of laser infrared thermography for detecting subsurface defects in metal additive manufacturing is investigated. Firstly, based on finite element simulation results, the reliability of laser infrared thermography for detecting subsurface defects of different depths and sizes in metal additive manufacturing is studied. Furthermore, the influence of rough surfaces on detection is taken into account, and the noise suppression performance of commonly used infrared thermography sequence processing algorithms is comparatively verified. Finally, Experimental validation of the processing of selective laser melting specimens with artificial internal defects is carried out. The simulation and experimental results demonstrate that laser infrared thermography can reliably detect the internal sub-surface defects of metal additive manufacturing with a width-to-depth ratio greater than 1 and the spatial noise caused by rough surface interference can be effectively suppressed by the commonly used pre-processing method of thermography sequences. In addition, laser infrared thermography inspection is expected to be a reliable technology for online monitoring of metal additive manufacturing due to its advantages of high efficiency, non-contact and visualization.

In order to solve the current problem of poor portability and low sampling rate of wearable functional near-infrared spectroscopy (fNIRS) detection equipment at home and abroad, a modulation and demodulation method of fNIRS based on FPGA is proposed in this paper. Firstly, the "frequency division -time division multiplexing" modulation of the light source driving signal is realized based on FPGA, and the frequency division is carried out between multiple wavelengths of a single light source, and the time division is carried out between multiple light sources, so as to improve the sampling frequency (frequency division) while taking into account the size of the system volume (time division). Secondly, the embedded digital demodulation and digital filtering of the detection signal is realized based on FPGA, so as to facilitate the interpretation of the blood oxygen signal and embedded application directly based on the embedded system. Compared with the current portable cerebral oximetry acquisition equipment, the design and development of FPGA-based embedded near-infrared spectroscopic imaging system improves the sampling rate, decreases the requirements for FPGAs, and reduces the size to increase portability. PGA-based signal demodulation lays an important foundation for the implementation of embedded applications and is expected to be suitable for more application scenarios.

The power control of the interaction of dressing fields of sequential-doubly-dressed four-wave mixing (FWM) in Y-type four energy-level systems is studied in this paper. Firstly, the eigenvalues of the dressed states are solved when two dressing fields are incident simultaneously in the nonlinear optical media based on the theory of the dressed states. And the relation of the position of the dressed states and the power of the dressing fields is researched. Then, the interaction of two dressing fields of FWM is shown by scanning the frequency detuning of the probe field along with changing the power of the dressing field. It is found that three embellishment energy levels are produced in the atomic medium when the detuning of the two embellishment fields are different, and when they are the same, two embellishment energy levels are produced. Moreover, changes in the power of the two embellishment fields cause the position of the embellishment energy levels to be shifted, and the degree of the shift is determined by the detuning of the embellishment fields.

The camera DMS and OMS in the car cabin identify the mental state, vital characteristics, and body expression of the occupants. In order to ensure the success rate of the algorithm recognition, there are certain requirements for the brightness of the face or body in the image. In view of the feature of near infrared imaging requiring active light source, a technical scheme of near infrared light supplement for the camera in the car cabin is proposed. A calculation model is established based on the theories of spatial flux distribution of infrared radiation source, near infrared characteristics of human skin, refractive and reflective law of geometric optics, photoelectric conversion ability of Sensor, etc. The relationship formula between the power of the fill light LED and the face brightness DN value in the image is derived. In the data model, the formula is used to calculate the luminance DN value of the face captured in the image after the 5.2 W near infrared supplementary light LED is used to fill the light. At the same time, a verification system is set up for the camera inside the cabin. Through the analysis of the brightness DN value of the image data taken by the test vehicle after the actual light filling, and the comparison with the theoretical calculation results, the error between the experimental results and the calculated results is less than ±5%, which meets the algorithm requirements, and verifies the feasibility of the technical scheme of near infrared light filling for the camera inside the cabin.

In this paper, an infrared image enhancement algorithm based on an improved Laplacian pyramid is proposed to address the common issue of detail and edge texture loss during infrared image enhancement. Firstly, in constructing the Laplacian pyramid, Canny edge detection is incorporated into the existing difference operation to extract the base and detail layers of the image. Secondly, the -CLAHE algorithm is applied to improve contrast and brightness in the base layer, and the Laplacian operator is then used to further enhance edge textures in the detail layer. Lastly, the enhanced infrared image is reconstructed by combining the detail layer with the base layer. Experimental results demonstrate that compared to traditional methods such as Clahe algorithm, Gamma correction, and others, the proposed algorithm achieves a maximum increase of 5.34 in PSNR, 0.6 in SSIM, and 2.07 in entropy, which validates the algorithm's effectiveness in enhancing contrast, highlighting edge information, and preserving structural characteristics during infrared image enhancement.

Infrared detection is widely used in the field of infrared small target detection due to its strong penetration, long detection distance and good concealment. In this paper, an infrared small target detection method based on dual-enhanced local contrast measurement is proposed on the basis of the local contrast method of human visual mechanism. Firstly, the noise and background are suppressed by local gray mean contrast, followed by target enhancement by a combination of relative grey scale gradient and local variance contrast, and the infrared small target is detected by threshold segmentation finally. The algorithm has higher detection rate and lower false alarm rate for different sky scene sequences, the former is up to 100%, and the latter is 0. The target detection performance of the same complex scene is significantly improved compared with other similar methods, signal-to-clutter ratio gain and background suppression factor are also significantly improved, resulting in excellent target detection performance.

A multimodal industrial anomaly detection method based on normalizing flow is proposed to address the issue of interference between high-dimensional features in multimodal industrial detection, resulting in unsatisfactory detection rates. Firstly, the depth information of the 3D point cloud of the image is extracted and added to the RGB image as the fourth channel to generate the fused RGBD image. Then, the fused image features are extracted using a pre-trained feature extraction network. Finally, a normalizing flow model for anomaly detection is obtained using feature training. The experimental results show that the anomaly detection model achieves an average Pixel AUROC of 95.8% and an average AUPRO of 86.2% on the MVTec 3D-AD dataset, which is an improvement of 2.6% and 9.1%, respectively, compared to other models.