The rapid development of infrared precision-guided technology has led to a rapid decline in the countermeasure effectiveness of traditional infrared decoys, infrared omnidirectional jamming and other protective measures. The directional infrared countermeasures system only radiates laser during interference, with narrow beam, concentrated energy, no need for heating, and strong concealment and timeliness, which is the mainstream platform for dealing with infrared precision guided strikescurrently. As a core component of directional infrared countermeasures system, laser source plays a significant role in jamming efficiency and has been receiving much attention. The research progress of six types of laser sources, such as quantum cascade lasers, which can be used in directional infrared countermeasure systems, is introduced, and an outlook on the future trend of directional infrared countermeasure systems is given in the light of the integration, stability and adaptability of laser sources.

This paper presents important applications for mid-infrared lasers with emission spectrums located in the 3~5 m band. The latest progress of mid-infrared lasers research at is summarized, the principal technical approaches are compared and analyzed, the key technologies are summarized. Combined with application scenarios, the difficulties and characteristics of different engineering applications are discussed. The future development of mid-infrared lasers is discussed.

Whispering Gallery Mode (WGM) microcavities exhibit ultrahigh Q factors and extremely small mode volumes, along with weak backward Rayleigh scattering. By utilizing this feedback mechanism in WGM microcavities, a laser linewidth compression by several orders of magnitude can be achieved, based on which a narrow linewidth laser is designed. Numerical simulations were used to analyze the optical behavior during the self-injection locking process, specifically the effects of changes in phase, detuning, backscattering and coupling efficiency on the system performance. Additionally, experimental validation is performed using a semiconductor laser with a central wavelength of 1552.126nm, and the 7 MHz linewidth laser output was narrowed to 500 kHz in the experiments. The narrow linewidth laser achieved through this approach has many advantages such as lightweight, high convenience, and stable performance. As a result, this narrow linewidth laser holds potential applications in areas such as laser interferometry, laser gyroscopes, laser ranging, and other related fields.

A saturable dual-wavelength laser based on a saturable absorption ring filter is designed. An unpumped erbium-doped fiber is inserted into a 3 dB ring mirror to form a saturable absorption ring mirror filter, which is inserted into a ring laser to achieve a stable dual-wavelength output by adjusting the polarization controller. After calculation,6.4 m erbium-doped fiber is selected as the gain medium, and a section of 2 m unpumped EDF is used to form a saturable absorption loop mirror filter in combination with a 2×2 3 dB coupler to suppress the supermode. In order to suppress the mode competition caused by the uniform widening of erbium-doped fiber at room temperature,1554 nm fiber Bragg grating (FBG) and 1562 nm FBG are selected for dual-wavelength lasing. Simulation is conducted on the Optisystem platform and experiments are set up to test the output laser power, signal-to-noise ratio, stability, etc. The output signal is observed and analyzed with a Optical Spectrum Analyzer. In the experiment, the stable output of dual-wavelength laser at room temperature is obtained, and the signal-to-noise ratio of the two wavelengths is 54.70 dB and 57.50 dB respectively. The 3 dB bandwidth is about 0.13 nm.

In this paper, the laser frequency stabilization hardware system is built using an industrial computer, NI data acquisition card and peripheral optical path, and a set of digital frequency stabilization control software for single-frequency fiber laser is developed using LabVIEW FPGA. Based on the servo control signal generated by the modulated transfer spectrum, the 780 nm laser frequency is locked to the ultrafine jump spectral line of rubidium atoms by acquiring the error signal and controlling the piezoelectric ceramic expansion and contraction after PI processing. Allan variance results demonstrate that a short-term frequency stability precision of 1.52×10-11 @10 s, and the long-term stability is better than 1.56×10-11 @100000 s, with continuous operation for over 20 days. This digital frequency stabilization software employs a graphical programming language, which significantly reduces development cycles, presenting an intuitive and concise user interface for convenient analysis and processing. It has been successfully applied to the rubidium 87 fountain clock system and possesses versatility for applications in precision measurement fields such as atomic interferometers and laser gyroscopes.

Aiming at the problem that speckle caused by high coherence of laser seriously affects the imaging quality, a speckle suppression method based on a singlepiezoelectric deformable mirror is proposed in this paper. A test system based on a 61-element piezoelectric deformable mirror is constructed, and the effects of surface complexity, amplitude and update frequency on imaging speckle contrast are investigated. When the number of actuators involved in the work is 61, the updating frequency of surface shape is 2.6 kHz, and the RMS of wavefront shape is 1.8 m, the speckle contrast is reduced to to less than 4 %, at which time the scattering is significantly suppressed. The experimental results show that the higher the surface complexity, the faster the update frequency, and the larger the amplitude, the smaller the speckle contrast of the obtained image and the better the image information.

Through-Silicon Vias (TSV) is a key technology enabling three-dimensional packaging, which has received much attention due to its unique vertical interconnect. However, the complexity of the silicon via process increases the chances of defects, which are not easy to be detected, thereby affecting the performance and reliability of the packaging. Consequently, a dynamic excitation-based internal detection method is proposed in this paper. By applying dynamic thermal excitation to the packaged chip, internal defects are stimulated to produce abnormal temperature distributions, and time series images of the temperature distribution on the outer surface of the package are collected, which are recognized and classified using deep learning to achieve external diagnosis of the internal defects. Firstly, a three-dimensional packaging model is constructed for transient thermal finite element simulation, and the simulation analysis reveals that internal defects have subtle impacts on the temperature distribution. Then, a three-dimensional Convolutional Neural Network (C3D) model is constructed to recognize and classify defects by analyzing the temporal changes in temperature gradient distribution images. Finally, an experimental testing platform is established, and three-dimensional packaging samples containing various defects are prepared for validation. The results show that the classification accuracy of the dynamic excitation-based internal defect detection method can reach up to 97.81 %, offering a new perspective for the detection of internal defects in three-dimensional packaging.

As an essential processing step in unordered picking tasks, point cloud segmentation directly impacts the subsequent accuracy of object recognition and pose estimation. To address the problem of inadequate segmentation performance of the traditional LCCP algorithm in complex object stacking scenarios, an improved LCCP point cloud segmentation algorithm that incorporates Gaussian curvature information is proposed in this paper. Initially, an enhanced VCCS algorithm is employed to partition the point cloud into super-voxel, and by integrating Gaussian curvature information, the issue of super-voxel easily crossing object boundaries is further addressed. Subsequently, concave-convex connectivity among adjacent super-voxel blocks is determined, followed by the merging of all convexly connected super-voxel to form the final segmentation results. The experimental results demonstrate that the method improves segmentation precision by 3.1 % to 22 % compared to LCCP and CPC, with a noticeable enhancement in overall algorithm performance.

Aiming at the phenomenon of unreliability of prism laser gyro ignition, the ignition method of laser device resonator excitation based on external electrode loading high frequency electric field is systematically studied. The factors influencing the acquisition of laser gyro ignition voltage are theoretically analyzed. Based on this, two measures are proposed to increase the ignition reliability of the resonant cavity. By shortening the distance between the ignition cathode and the ignition anode, the required ignition voltage value of the resonant cavity is effectively reduced, making it easier for the working gas to be locally excited, and then produce an avalanche effect, so that the entire internal space of the working gas is excited, to achieve the normal continuous operation of the laser device resonator. the experimental results show that the optimized laser gyro resonator can achieve more than 99.9 % reliable ignition at high, low and constant temperatures.

In this paper, a high dynamic range ROIC for short-wavelength infrared focal plane arrays are presented. A capacitive transimpedance amplifier structure is used for the input stage. By time-multiplexing the pixel op-amps in the odd-even rows as comparators, the conversion gain of each pixel can be individuallyswitched and adaptivelyadjusted according to the background radiation, and the modules such as integrating capacitors, sample-and-hold circuits, and source followers in the pixels are shared by the odd-even pixels to save the area and improve the freedom of pixel design. This structure increases the full-well capacity, reduces the equivalent noise charge number, and improves the dynamic range without increasing the pixel area. The pixel array size of the readout circuit is 64×64 with a pixel pitch of 30 m, and the simulation results show that the readout circuit has a noise charge of 57.8 e-, a full-well capacity of 12.5 Me-, a dynamic range of 106 dB, and a readout rate up to 10 MHz.

The custom windowing is an important technique for the observation of areas of interest and the detection of special areas in large-scale infrared focal plane applications by reconstructing image resolution to improve the readout frame rate. Based on the semi-custom design flow, a readout integrated circuit (ROIC) digital module with custom windowing function is proposedin this paper, which can realize the functions of integral time regulation, working mode switching, random windowing and anti-overflow under the control of five external input signals, and has the advantages of easy expansion, simple control and flexible operation. Aiming at the risk of race and hazard in the traditional decoding circuit, the design of row-level pulse width adjustable selection signal and the solution of column-level multi-port readout are proposedto further improve the reliability of the circuit. The simulation results show that the whole design can properly implement the function of custom windowing normally, and is suitable for large-scale infrared focal plane array.

A column-level ADC-based readout circuit design is introduced in this paper. The size of the readout circuit is 640×512, the pitch is 15m. The column level digital ADC structure adopts a three-order incremental Sigma Delta ADC structure, and its quantization resolution is 16 bits. The maximum frame frequency of the readout circuit is 240 Hz, and the maximum power consumption is 250 mW, and the output method is LVDS mode.

Commonly used infrared materials in the design of transmissive optical systems include ZnSe, BaF2 and LiF. In order to provide optical designers with accurate refractive index temperature coefficient parameters, a new method of relative measurement of the bias angle based on the auto-collimation method is proposed, and a refractive index temperature coefficient measurement system based on infrared single-point detection is constructed to analyze the accuracy advantage of the method. Experiments on ZnSe prisms are carried out by this system. And the experimental data are compared with the CHARMS system of NASA (National Aeronautics and Space Administration), and the relative error is within 1 %. This system solves the practical problem of refractive index temperature coefficient measurement of infrared optical materials under wide spectral and temperature domain conditions, and further improves the measurement accuracy of infrared optical systems.

In order to meet the demand of large turning angle range in optoelectronic countermeasure equipment, the calibration technology of two-dimensional large-angle fast steering mirror angle measurement system based on eddy current sensors is investigated, and the two-dimensional large-angle fast steering mirror calibration system is designed. The angular measurement principle of the calibration system is analyzed, and model construction and experimental testing are carried out by combining the two methods through theoretical and residual analyses of polynomial fitting and bilinear interpolation calibration models. The results show that the overall error of applying the bilinear interpolation fitting method is less than 30 ″in the angular range of ±7 °(±25200 ″). Comparison experiments with the traditional polynomial fitting and bilinear interpolation show that the bilinear interpolation fitting method has a smaller calibration error, higher localization accuracy, and is more suitable for two-dimensional large-angle fast reflector calibration.

The surface shape data of optical interferometry are required when the optical components are iteratively machined using equipment based on Computer Controlled Optical Surfacing (CCOS). However, when the off-axis concave parabolic mirror is measured using the autocollimation detection optical path without image handicap method, the projection distortion caused by the optical path distorts the detected surface map, which makes it difficult to quickly converge on the surface quality of the workpiece. To address this problem, a distortion correction method for off-axis concave paraboloid mirror based on autocollimation detection optical path is proposed in this paper. By determining the projection coordinate transformation of CCD coordinate system and mirror coordinate system in the interferometer, and combining with the calibration of a few mark points, the whole surface shape is reconstructed to correct the projection distortion. According to the autocollimation detection of 430 mm off-axis concave parabolic main mirror of a large diameter off-axis quad-mirror optical system, the surface shape accuracy RMS reaches /80(=632.8 nm) by using the detection data after distortion correction as the machining guide of Mr Equipment. This method achieves high-precision surface shape correction based on a small number of marks, which can effectively shorten the distortion correction time in CCOS iterative machining, and improve the machining efficiency of optical components while ensuring the surface quality requirements.

In this paper, a transmissive metalens operating in the mid-infrared band with polarization insensitivity is designed. Using silicon dioxide as the substrate and silicon as the dielectric material, the metalens has a radius of 100 m, an optimal focal length of 302 m, a focal efficiency of 70.72 %, and a simulated focal length with only 0.0013 % error from the theoretical calculation. Taking the minimum value of the focal length error as a reference, several metalens with different radii were designed, and the data of optimal focal length, numerical aperture, and focusing efficiency are obtained, which improved the efficiency of the subsequent design of metalens based on the same structure. The design method provides ideas for calculating the optimal focal length of metalens, and offers technical support for improving the efficiency of the mid-infrared superstructured lens.

Aiming at the problems of low matching accuracy and poor real-time performance of traditional local feature matching algorithms in complex scenes, an image matching method based on CenSurE star fusion of marginalization outliers is proposed in this paper. Firstly, fast bootstrap filtering preprocessing is performedon the template image and the image to be matched. Subsequently, an adaptive threshold based on CenSurE star algorithm is proposed for feature detection. Secondly, for the first time, the BEBELID (Boosted efficient binary local image descriptor) descriptor is used in conjunction with the improved CenSurE star algorithm to obtain efficient binary descriptors using machine learning based classification methods. Finally, MAGSAC++ (Marginalizing Sample Consensus) algorithm is introduced to marginalize outliers and obtain spatial geometric transformation relationships, eliminating errors in preliminary matching and improving matching accuracy. Through the experimental comparison of the standard Oxford dataset, compared with the BRISK, ORB, AKAZE, and the traditional CenSurE-star algorithms, this method has a more uniform distribution of feature points, fewer mismatched points, and possesses stronger robustness in terms of blurring, illumination, point-of-view, and scale variations, which improves the matching accuracy of the algorithm in complex scenes and further enhances the real-time performance.

Due to the strong complementarity between visible light and infrared images, more attention has been focused on tracking through the joint information of these two modalities. However, in existing tracking algorithms, hthe inability to effectively learn the complementary information of both and mine modality-specific features limits the performance of the tracker. In responseto this issue, a reversible multibranch bimodal adaptive fusion network for tracking is proposed. Firstly, a tri-branch structured network is designed for separate learning of thermal infrared, visible light, and their shared characteristics. This design not only maximizes the utilization of shared modal information, but also preserves the differential characteristics between infrared and visible data as well as the rich detail information. Furthermore, an adaptive module for modal feature interaction is introduced to efficiently mine complementary modal information and filter out redundant data. Extensive experiments conducted on multiple public datasets proves the effectiveness of this tracker, particularly showcasing remarkable anti-interference capabilities in scenarios involving scale changes, camera shakes, and occlusion.

Aiming at the problem of low resolution and unclear details of infrared polarization imaging of sea ships, a method combining wavelet transform and generative adversarial network is proposed to improve image resolution. Firstly, the pure convolutional neural network model (ConvNeXt) is used to improve the super-resolution network (SRGAN), and the original low-resolution ship infrared polarimetric image is denoised by using non-local mean. Then, the low-resolution image is initially super-resolved with the improved SRGAN, and the detail information of the initial super-resolved image is extracted using a two-dimensional discrete wavelet transform. Finally, the detail information is fused with the original low-resolution ship infrared polarization image through the inverse wavelet transform. Compared with the traditional super-resolution method, the peak signal-to-noise ratio and structural similarity of the super-resolution image obtained by the proposed method are significantly improved. In this paper, the infrared polarization image super-resolution and detail information fusion isachieved at the same time and the obtained super-resolution image not only retains the infrared polarization information of the original image, but also fuses the high-resolution detail information.

The investigation into cloud detection algorithms holds significant potential for applications in disaster prediction, meteorological research, and beyond. The focus of this research endeavor lies in the development of a cloud detection algorithm tailored for MODIS imagery, leveraging the power of deep learning's semantic segmentation techniques to enhance the accuracy of cloud detection from MODIS data. This study introduces a novel deep learning model, which integrates the strengths of U-Net, block self-attention mechanisms, and multi-scale network modules, to achieve a more precise differentiation between cloud and non-cloud regions in remote sensing images. Building upon the robust foundation of the U-Net architecture, our model incorporates attention modules and multi-scale network elements. These enhancements are specifically designed to bolster the model's capability in identifying subtle features of cumulus humilis and fractocumulus clouds, addressing the limitations of traditional cloud detection algorithms in detecting thinner cloud layers. The attention mechanism employed in this work harmoniously combines block self-attention and multi-scale channel attention. The former enhances the model's sensitivity to global contextual information, thereby mitigating the challenge of poor detection in thin cloud layers. The latter, by extracting channel-wise relevant features, complements the detection of smaller cloud formations that might otherwise be overlooked. In the experimental phase, we meticulously detail the dataset utilized, including near-infrared spectral bands among other carefully selected data channels. The evaluation results showcase the model's remarkable performance, with precision and recall rates of 88.58 % and 94.80 % respectively for cloud detection. These findings conclusively demonstrate the effectiveness of our designed deep learning model in accurately detecting clouds from MODIS imagery, underscoring its promising applications in advancing the field of remote sensing and related meteorological endeavors.

Long period fiber gratings (LPFGs) can sense changes in the environmental medium through the coupling between core fundamental modes and cladding modes, thereby achieving perception of the external environment. They have many advantages such as high sensitivity, small size, low back reflection, and resistance to electromagnetic interference, etc., and have been applied in various fields such as food safety, environmental detection, disease diagnosis, and so on. In this paper, the progress of the application of LPFGs in biological detection technology in recent years is summarized. Firstly, the principle of LPFGs for biological detection is elucidated. Secondly, the common fixing methods of the biological functional films on the surface of LPFGs are introduced, and then the applications of LPFGs in the detection of biological substances such as DNA, glucose, bacteria, cancer, and viruses are summarized. Finally, the challenges faced by LPFGs in biological detection technology are analyzed, and the future development of LPFGs in biological detection technology is prospected.

To simultaneously detect salinity and depth without temperature interference, a salt depth sensing system based on different packaged fiber sensors was designed. Three different packaging forms of fiber sensors are connected in series to achieve calculation of salinity depth and temperature. Constructed inversion models for salinity and depth, and analyzed the feasibility temperature compensation. The experiment tested the changes in salinity, depth, and temperature separately. The results showed that in the salinity test of 0～50 g/L, the average sensitivity of the system was 128.7pm·g-1·L-1, and the linearity of the wavelength shift between the main peak and the first main peak of salinity change was 0.9654. In the depth test of 1~15 cm, the average wavelength shift with depth is 751.9 pm/cm, and the linearity is 0.9931; In the temperature test of 23~45 ℃, the temperature response of the three fiber optic sensors was basically consistent. After temperature compensation using FBG2 test data, the wavelength deviation of LPG and FBG1 was only ±10 pm.