The simulation of the influence of laser atmospheric transmission is of great significance to the practical application of various lasers, which can provide an important guarantee for the smooth development of laser practical tests. Inthis paper, the simulation of the effects of atmospheric attenuation, turbulence and thermal halo on laser beamsin laser atmospheric transport simulations and related work are mainly introduced. In terms of atmospheric attenuation, it is usually necessary to calculate the atmospherictransmittance, and there are mature simulation software. For atmospheric turbulence, there is currently a phase screen method can be equivalent to phase and refractive index perturbation to simulate random turbulence, laser beam transmission simulation in turbulence, cannot actually simulate turbulence. In terms of atmospheric halo, most scholars have only carried out steady-state thermal halo researchbecause of the randomness and transience of the thermal halo. Laser atmospheric transport simulation also needs further study in the coupling of turbulence and thermal halo, the joint establishment of a simulation system for linear and nonlinear effects, and the construction of a laser atmospheric transport simulation platform.

The infrared band is located between the visible light band and the microwave band, compared with visible light, has the characteristics of thermal effect, invisibility and strong penetration. With the rapid development of infrared holography technology using infrared laser as a coherent light source, infrared holography technology has shown incomparable advantages over visible light holography technology in the field of non-destructive testing and lensless imaging. In this paper, the development history of infrared holography technology is reviewed, and the research status of infrared digital holography (IRDH), scanning infrared digital holography (SIRDH), and infrared digital holographic microscopy (IRDHM) are introduced. Moreover, the detection advantages and typical applications of infrared holography technology are summarized, and finally the future development trend of infrared holography technology is prospected.

The Q-switched laser is extensively applied in fields such as laser material processing, laser ranging, and lidar. In this article, the few-layer Ti3C2Tx nanosheet saturable absorbers were fabricated by the liquid-phase exfoliation method. The surface morphology, phase composition, and thickness of the few-layer Ti3C2Tx nanosheets were characterized and analyzed with scanning electron microscopy and transmission electron microscopy. Subsequently, the few-layer Ti3C2Tx cuvette was employed as the Q-switching device and inserted into the resonant cavity of the Nd∶YVO4 laser. After adjustment and optimization, passive Q-switched pulsed outputs of the Nd∶YVO4 laser at 1064 nm and 1342 nm were achieved respectively. At the 1064 nm wavelength, the average output power reached 217 mW, the pulse width was 140 ns, and the repetition frequency was 235.8 kHz. At the 1342 nm wavelength, the average output power was 138 mW, the pulse width was 170 ns, and the repetition frequency was 191.9 kHz. Finally, the reasons for the distinctions between the passive Q-switched 1064 nm and 1342 nm Q-switched pulses of Ti3C2Tx were analyzed.

2 m laser, which is always used as the pump source of mid-infrared OPO, plays a significant role in manufacture and scientific research. Bulk oscillator using Ho3+ doped crystal is the main method to produce high-power 2 m laser beam. A high power Q-switched Ho∶YAG oscillator pumped by a Tm3+-doped fiber laser is presented. The maximum continuous output power of 36.1 W with a slope efficiency of 61.4% is achieved at the wavelength of 2090 nm. In the Q-switched regime, 33.6 kW peak power and 72 ns pulse width are obtained at the pulse repetition frequency of 15 kHz. This oscillator contains a single Ho∶YAG crystal which makes it compact and suitable for integration.

High-peak-power, high-beam-quality 1550 nm laser are widely used in biomedical, material processing and environmental monitoring. In this paper, an eye-safe pulsed fiber laser based on a main oscillator power amplifier (MOPA) is reported. The output peak power of 12.2 kW is achieved successfully. The optical conversion efficiency is 17.7%, the central wavelength is 1549.884 nm, and the line width of 3dB is 1.182 nm, the frequency of the light is 50 kHz and the pulse width is 6.146 ns, the beam quality is 1.11. In addition, this paper has carried on the engineering design of the eye-safe pulsed fiber laser to enhance its tolerance to environmental changes, thus further improving its application potential.

With the continuous development of industrial automation, the application of industrial robots is rapidly increasing. Simultaneous Localization and Mapping (SLAM) for autonomous mobile robots in unknown environments have become a research hotspot. Currently, laser SLAM technology has made great progress, and many open-source laser SLAM methods have matured, but their performance in real-world environments is not confirmed. In this paper, three mainstream laser SLAM algorithms are applied to a tracked mobile robot system in an indoor environment to evaluate the algorithms' localisation performance in real environments. The experimental results show that the Gmapping algorithm exhibits better localization performancein small-scale scenarios, while the Karto and Cartographer algorithms have smaller differences and need to be selected according to the complexity of the real scene.

In the dual comb interference process of bidirectional time-frequency transmission experiment, the waveform and spectrum of the interference signal change due to factors such as link dispersion and Doppler Effect, this will cause interference on key indicators such as clock deviation and frequency transmission accuracy. In response to the data simulation requirements of this experiment in a free space environment, the basic principles of comb pulse interference, link dispersion effect, and Doppler effect, as well as the functional composition of the simulation model, are studied. As a result, interference data is simulated and the peak data is captured on the C++ platform. The research results indicate that the model has a high degree of matching and a fast-processing speed, which in turn can provide a useful reference and assistance for subsequent practical experiments.

The signal-to-noise conditions can affect the temporal correlation of low signal-to-noise ratio LiDAR echo signals, leading to a decrease in the accuracy of signal recognition results. In this regard, a self-adaptive recognition method for low signal-to-noise ratio LiDAR echo signals is proposed. Firstly, under low signal-to-noise ratio environment, an independent component analysis model is used to perform independent component analysis on the low signal-to-noise ratio echo signal, separating the source echo signal and interference signal, and obtaining the echo signal after interference suppression. Then, using short-time Fourier transform to obtain the time-frequency spectrum of the echo signal, the improved SSD network model is trained through the time-frequency spectrum to achieve adaptive recognition of low signal-to-noise ratio echo signals. Simulation analysis shows that the proposed method is feasible and effective in the field of adaptive recognition of low signal-to-noise ratio echo signals, which can effectively improve the reliability of LiDAR echo signals.

In this study, a multi-wavelength Mie-Raman LiDAR is used to gather relative humidity and aerosol optical parameter data to investigate the effects of hygroscopic growth on the optical and microphysical characteristics of aerosol particles. Two typical cases observed at Nyingchi airport on the Tibetan Plateau are chosen for in-depth examination at the two-dimensional analysis level. Regardless of Case 1 or Case 2, the LiDAR data demonstrates that the hygroscopic growth factor firstly exhibits a fast-rising trend, and then grows slowly with the increase of relative humidity. In order to improve the accuracy of the analysis, several models are fitted and compared. The outcomes demonstrates that the three-parameter model proposed in this paper has the highest correlation coefficient, proving its validity and accuracy. Furthermore, the dependability of multi-wavelength Mie-Raman LiDAR in identifying aerosol hygroscopic growth is further validated by contrasting the observational data with nephelometers. The results show that the hygroscopicity, sedimentation, and absorption saturation of aerosol particles have a significant enhancement trend after examining the hygroscopic growth fac-tors of aerosols at various heights. This finding adds significant understanding of aerosol properties and offers important empirical support for research on changes in the atmospheric environment in the Tibetan Plateau.

In order to avoid the obvious drawbacks of traditional image enhancement methods in addressing the demand for multi-scale image enhancement, a laser image multi-scale enhancement based on variable structure deep learning is designed in this paper. Firstly, a variable structure deep learning model is constructed, and in the generative network module, a weighted least squares filter is used to filter and decompose the image. Then, the detail layer of images is enhanced through adaptive enhancement technology. Subsequently, the detail layer is fused with the base layer and reconstructed through the deconvolution layer of the convolutional neural network. In the discrimination network module, the PatchGAN structure is used to distinguish the authenticity of the generated image and the target image, and repeated optimization is achieved through the superposition training of the loss function to enhance the infrared laser image. The experimental results show that this method not only effectively preserves the edges and details of the image, but also achieves a global smooth and delicate effect, significantly improving the contrast and clarity of the image, and at the same time performs well in all the objective evaluation indicators.

The current non-destructive testing of ammonia fuel engine surfaces using laser ultrasonic Lamb waves result in severe frequency dispersion and multimodal phenomena, making the signal complex and unable to completely eliminate the dispersion effect, which has always been a challenge in this field. For this reason, an improved method for detecting corrosion damage in ammonia fuel engines based on laser ultrasonic Lamb waves is proposed. Firstly, a linear mapping dispersion compensation method is introduced to effectively correct the dispersion effect in the one-dimensional laser ultrasonic Lamb wave signal captured by laser excitation and reception on an ammonia fuel engine. Secondly, the amplitude spatial wavenumber spectrum of the frequency, wavenumber, and spatial position information of the signal at a specific center frequency is obtained through short space two-dimensional Fourier transform to determine the approximate location of corrosion damage on the scanning path of the ammonia fuel engine. Finally, based on signal characteristics, the RAPID method is used to determine the precise location of engine corrosion damage and achieve corrosion damage detection. The experimental results show that after compensation of the proposed method, the signal waveform is significantly improved, with compact time-domain and clear frequency-domain characteristics, and the proposed method can effectively improve the accuracy and reliability of corrosion damage detection in ammonia fuel engines.

Infrared detector development into the third generation, with a large array, long line array applications as a typical representative, of which the long line array is usually achieved using the splicing structure. The scale of the monolithic circuit involved in splicing is also expanding, and the line frequency is increasing. The increase in array specifications brings about the extension of the signal transmission link, the line frequency enhancement puts forward new requirements for the readout rate, and these must also take into account the detector's low-temperature applications, requiring the readout circuit to put forward a more reasonable and effective countermeasures. In this article, a novel output method is explored for long line array, which can suppress transmission noise by full differential output, while the differential module conversion module is independent of the main detector chip. The readout rate is significantly increased without affecting the overall thermal consumption of the chip. Depends on this design, the readout rate of line array is increased to 20 MHz, and for 1k scale, the line frequency reaches 19 kHz.

The temperature field of solid wall and tail jet gas on the test bench changes under different conditions of sunlight, which directly affects the infrared radiation test results of the aeroengine. Using the infrared radiation characteristics evaluation software with independent intellectual property rights, the influence of different sunlight conditions on the infrared radiation characteristics of aeroengine is analyzed, the influence law of sunlight on the infrared radiation characteristics of engines at different dates and times is derived, and the date and time suitable for the test of infrared radiation characteristics of aeroengine are given. The conclusion can provide reference for the design of aeroengine infrared radiation test.

In response to the need for manual recognition of infrared images of transmission line insulators and the difficulty in constructing a complete anomaly sample database, an intelligent detection algorithm is proposed based on the combination of domain transform infrared image processing module and unsupervised feature reconstruction anomaly detection module. The domain transform module is used to enhance the matching degree between input data and the intelligent model, while the unsupervised anomaly detection module utilizes feature reconstruction, knowledge distillation, and a memory matrix to discriminate anomalies by comparing input and reconstructed images. The domain transform module improves the detection accuracy of the unsupervised anomaly detection model by 2%~4% on a self-made insulator infrared image dataset, demonstrating success in various background comparison experiments. Furthermore, the AUCROC index of the unsupervised feature reconstruction anomaly detection module (FRAD) on MVTec AD dataset and self-made dataset reaches 88.78% and 88.56% respectively, which is in the leading position compared to models in the same domain.

The multi-axis consistence detecting device is used for measuring the consistence of each axis in self-propelled antiaircraft gun in the out-field. For testing the measurement accuracy of the testing device, the research on testing technology of multi-axis consistency is conducted, and the testing technology of double warp and woof with multi-spectrum parallel optical tube has been demonstrated, which realizes the accurate measurement of the consistency of multi-spectrum and large spacing of optical axes in the outfield. And the error analysis indicates that the accuracy this method is better than 2″, offering a new scientific and practical method to quantify testing technology of multi-axis consistency. In order to accurately assess the technical performance of the multi-axis consistence detecting device, in line with the tactical technical requirements of self-propelled anti-aircraft guns, a new idea on evaluating performance of detecting device based on axis-vector is put forward, solving the difficult problem that the axis angle cannot scientifically assessed by the measurement accuracy of the detecting device. Moreover, the detection technology of the axial vector and the assessment method of the measurement accuracy are thoroughly studied, and the uncertainty of the assessment method is analyzed, which provides a basis for scientifically assessing the technical performance of the multi-axis line consistency detection device.

Smoke screen has the characteristics of high cost-effectiveness ratio and convenient deployment in the application of countering precision-guided weapons. In view of the lack of quantitative evaluation methods for smoke screen jamming effectiveness at present, a smoke screen jamming effectiveness assessment method based on image correlation and image structure similarity is proposed according to the guidance mechanism of infrared guidance system. To improve the applicability of the tracking error-interference success rate evaluation criterion, an effective masking probability index for the smoke screen is put forward. The interference processes of smoke screen are analyzed through the external field experiment, and the test data are quantitatively analyzed using the mean normalized cross correlation (C2) and structural similarity index (SSIM). And the quantitative evaluation criterion for effectiveness smoke interference is proposed on the basis of the evaluation results. The experimental results show that the smoke-screen jamming effect can be quantitatively evaluated based on image quality evaluation and effective obscuring probability. When the value of C2 drops to less than 0.4 or the value of SSIM value is less than 0.72, the infrared guidance system loses the target and the smoke-screen jamming is effective. For smoke bombs loaded with nano-smoke materials and those enriched with carbon black aggregates, the probability of effective smoke screen masking is 0.503 and 0.659, respectively.

With the development of deep space exploration, satellite navigation and other fields, such as satellite communication networks based on laser communication and high-precision positioning based on laser ranging have achieved a large number of application results. Combining laser communication with laser distance measurement to achieve the integration of communication and ranging has become a research hotspot in the field. In this paper, the development status of laser communication and ranging integration at home and abroad is introduced, and then a design scheme of laser communication and ranging integration system with space debris detection capability is proposed in the light of the current situation in China. In order to verify the feasibility of the scheme, the original 1-meter laser communication telescope system in Changchun Station is modified to increase the ranging capability of space debris targets, and the measured data show that the effective data accuracy is better than 1 m at the highest. Finally, the future development direction of integrated laser communication and ranging system is prospected, aiming to provide ideas for further promoting the development of integrated laser communication and ranging system.

In this paper, a combination of particle swarm optimization and genetic algorithm is used to perform layered design and optimization of a bilayer metalens across a wide bandwidth, which achieves the enhancement of the focusing and achromatic performance of the bilayer composite nanostructure metalens. Firstly, the optical modulation characteristics of the micro cell structure required for the concave-convex metalens are studied, and the phase database is constructed by scanning the radius of the micro cell structure at a fixed height by numerical simulation software Lumerical. After that, according to the phase profile of the bilayer metalens, the optimal radius-phase matrix is found in the phase database, that is, the optimal element structure corresponds to each position of each layer of metalens. Finally, the bilayer metalens is constructed layer by layer, according to the optimization results, and the simulation experiments verify that the bilayer metalens can achieve the focusing in the near-infrared band (1000 nm, 1250 nm), and the achromatic effect is better than that of the single-layer metalens at the same focal length. This metalens provides a solution for the design of bilayer composite nanostructured metalens in the near-infrared band.

To address the problems of low optical precision and high false alarm rate in detecting unexploded ordnance (UXO) on the ground in various vegetation environments with drone-based optical detection, a UXO detection method based on the infrared features of unexploded ordnance using YOLOv5 is proposed in this paper. Firstly, the target data of unexploded ordnance is reconstructed, and the ECA attention mechanism is introduced to improve the recognition accuracy. At the same time, the ASPP hole space pyramid pooling is introduced to improve the recognition efficiency, and the CIoU~~NMS is used as the prediction box selection criterion. The experimental results show that on the bird's eye view UXO target infrared data set in multiple groups of different vegetation environments, the SAE-YOLOv5 algorithm has an improvement in UXO target precision from 83% to 87%, and the average precision mean is improved from 83.6% to 85%, compared with the original YOLOv5 algorithm model. The algorithm is effective in detecting UXO targets in the four complex backgrounds mentioned in the paper, and with a low false alarm rate.

Point cloud registration is a key step in 3D data processing. Aiming at the problem of low registration efficiency due to the weak representativeness and descriptiveness of feature points in the registration process, a point cloud registration method based on the improved 3D scale-invariant features (3DSIFT) algorithm is put forward in this paper. Firstly, the feature points extracted by the 3DSIFT algorithm are streamlined by combining the information entropy theory, and the representative and descriptive points are retained as the points to be registered. Secondly, the unique shape context (USC) description is added to the feature points. Then, coarse matching is completed based on the progressive sample consensus (PROSAC) algorithm. Finally, a bidirectional KD-tree is established for the source and target point clouds to reduce the search time and accelerate the iterative closest point (ICP) to complete the fine registration.

In order to alleviate the scarcity of image datain infrared small dimtarget detection, an image augmentation algorithm based on image-to-image translation is proposed. This method is a two-stage image generation algorithm. First, additional visible image is introduced, and the mapping between visible and infrared image is learned by U-GAT-IT model, converting the visible image into infrared background image. In order to solve the problem of overfitting in image translation, a channel regularization method is proposed to make the channel information of infrared and visible images consistent Then, an auto-encoder based on vision Transformer structure is designed to learn the distribution characteristics of infrared small targets and synthesize small targets on the obtained infrared background images in the way of masking and reconstructing. The method is trained and tested on SIATD data sets. The experimental results show that the proposed data augmentation algorithm can improve the detection indexes on three models to a certain extent, among which the AP index of the YOLOv3 model increases by 1.37%, which proves the effectiveness of the proposed data augmentation algorithm and can improve the performance of the target detection model in the infrared small dim target detection task.

Currently, research on unsupervised single-modality pedestrian re-identification mainly focuses on visible light images. With the proliferation of new infrared cameras, unsupervised infrared pedestrian re-identification also demonstrates its research value. Due to the low contrast and lack of color texture details in infrared images, global information is crucial for infrared pedestrian re-identification. This paper designs an unsupervised infrared pedestrian re-identification network based on F-ResGAM. The network first uses wavelet transform for image pre-processing to enhance feature extraction capabilities, and then introduces GAM (Global Attention Mechanism) in the ResNet50 network structure to focus on more global information. Furthermore, due to the high noise in infrared pseudo-labels, this paper proposes a group sampling strategy based on sample expansion (GSSE) to further optimize the generation of pseudo-labels, thereby improving the model's recognition accuracy. Experimental results show that the optimization methods proposed in this paper effectively enhance the accuracy of unsupervised infrared pedestrian re-identification, especially with a significant improvement in the rank metric.