Using the infrared detector of the STSS LEO demo satellite as an example, the detectability of aerial hypersonic targets such as AGM-183A was analyzed. To visually compare the detectability of the targets under different conditions, the number of pixels on the focal plane with output signal-to-noise ratio(SNR) higher than a specified threshold was quantified. First, the aerodynamic temperature and spectral radiant energy of the hypersonic target were calculated. The infrared detector model was used to predict the peak value of the SNR and the number of responding pixels in the focal plane for specified detection distances and angles. The analysis results indicate that in the sub-satellite point detection mode, the SNR of the focal plane reaches the highest value (335), and the number of responding pixels with SNR higher than the threshold (6) reaches its maximum(54?54), representing the maximum detectability of the LEO detector for AGM-183A targets. In the edge detection mode, the variation in target detectability with detection angle and target temperature was calculated. The results show that, when the target temperature approaches 800 K and the detection azimuth angle ψ is less than 10° (or greater than 170°), the number of responding pixels on the focal plane reaches the lowest value of 4×4, indicating that the AGM-183A target detectability approaches the theoretical limit of the LEO detector (3×3). By comparison, changes in the target temperature have a more substantial impact on target detectability. In edge detection mode, the escape probability of the target is relatively high when the target uses active cooling to reduce its surface aerodynamic temperature to less than 800 K. From the perspective of improving early warning capability, the SNR threshold value of LEO detector focal plane should be increased for targets with surface temperatures approaching 800 K at the most unfavorable angles of ??(less than 10° or greater than 170°, with the minimum number of responding pixels in the focal plane no fewer than 8×8).
This study presents the design of a flexible mechanism with a large deflection angle for piezoelectric-driven fast-reflecting mirrors, to address the common issue of small deflection range. First, a study was conducted on the correlation among the nested hierarchy, configuration, natural frequency, and amplification factor of the flexible mechanism. Accordingly, a preliminary plan was developed for the design of a three-stage hybrid configuration. The mechanism was discretized into flexible hinges, rigid bodies, and concentrated masses as basic units. Subsequently, a general dynamic stiffness model was constructed for the flexible mechanism using the matrix displacement method. This model establishes a mapping relationship between the structural parameters of the flexible mechanism and the deflection angle of the fast-reflecting mirror. On this basis, a modal analysis of the flexible mechanism was performed,whereby the key dimensional parameters of the fast-reflecting mirror's flexible mechanism were optimized.Compared to similar research conducted domestically and internationally, this configuration achieves a mechanical deflection angle greater than 100 mrad by ensuring miniaturization and a higher first-order natural frequency.
Infrared search and tracking systems based on the mobile platform have become the mainstream trend of the new generation in optoelectronic search and track systems, and miniaturization and lightweight guarantee high mobility. The angular velocity disturbance, coupled with the carrier's motion attitude change and internal torque disturbance of the system, raises serious challenges to the optical-axis stability control of the optoelectronic load. The traditional optic-axis stability method based on a combination of multi-axis, multi-frame, and high-precision gyro feedback control, is no longer applicable. In this study, a doublevelocity closed-loop same-order cascade control method is proposed based on square PI and Luenberger disturbance observation and feedforward, for the optical axis stability control of the optoelectronic load on a two-axis two-frame mobile platform infrared search and tracking system. Simulations and experiments show that compared with the conventional single-gyro closed-loop and double-velocity closed-loop stability control methods, the proposed stability control method can effectively improve the stability accuracy of the optical axis under low-frequency disturbance of the carrier’s motion. Under a disturbance of 1?/1 Hz carrier motion, the stability accuracy of the simulated optical axis improved to 2.7817 ?rad and that of the actual experiment improved to 35.85 ?rad. Under a disturbance of 1??/2 Hz carrier motion, the stability accuracy of the simulated optical axis improved to 38.199 ?rad and that of the actual experiment improved to 119.1?rad. Finally, using the stability control method proposed in this study, the two-axis two-frame infrared search and track system based on the mobile platform effectively overcame the low-frequency angular velocity disturbance coupled with the carrier's motion attitude change between marching, to realize a highly stable and highly dynamic optical-axis-oriented control performance of the optoelectronic load.
Surface components of HgCdTe (MCT) films were examined using x-ray photoelectron spectroscopy after etching with different solutions, including Br2:methanol (Br2:Me), Br2:HBr, and Br2:HBr:ethanediol (Br2: HBr: Eg). The surface degradation after etching by bromide-based solutions arises from the Te-rich element on the surface of HgCdTe, and the enrichment degree of Te is (Br2: HBr: Eg)< (Br2: HBr)<(Br2: Me). The wet-etch method is difficult to apply in eliminating Te-rich components to achieve a near-stoichiometric surface. In the commonly used method, oxidation is followed by corrosion. Plasma oxidation offers advantages, such as strong oxidation, stability, safety, and environmental protection. Therefore, oxygen plasma treatment has been introduced for various etchants to eliminate oxides, including hydrochloric acid, lactic acid, and ammonia. The results indicate that the use of low-concentration hydrochloric acid immersion generates a better effect without introducing any new dopants, and the defect density of the CdTe/HgCdTe interface decreases significantly after treatment.
The bare complex layout of PCBs cause low contrast, uneven brightness, small defect positions, and irregular shapes in detected images, resulting in a large number of parameters, overfitting, and loss of feature information with increasing network depth. In this study, a PCB detection model PA-YOLO v5 based on YOLO v5 and mixed attention mechanism fusion with higher accuracy is proposed to suppress interference from general features and ensure that the network pays more attention to the detailed features of defect targets during feature extraction. The adaptive bidirectional feature pyramid network(BiFPN) is taken as reference to fully utilize the different scales of each feature map, thereby assigning different weights to different detection targets, to improve the network's ability to express various features. Finally, the FReLU activation function is used to expand the ReLU space into a 2D activation function, which enhances the receptive field's ability to capture details and improves model robustness and generalization. Six types of defects were tested using the DeepPCB dataset, and the experimental results showed that the proposed PA-YOLO v5 detection model achieved an accuracy of 99.4%. The effectiveness of the model was verified through ablation and comparative experiments.
During infrared (IR) image capture, the shaking of camera equipment or rapid movement of the target causes motion blur in the image, significantly affecting the extraction and recognition of effective information. To address these problems, this study proposes an infrared image deblurring method based on a dense residual generation adversarial network (DeblurGAN). First, multiscale convolution kernels are employed to extract features at different scales and levels from infrared images. Second, a residual-in-residual dense block (RRDB) is used, instead of the residual unit in the original generation network, to improve the detail of the recovered IR images. Experiments were conducted on the infrared image dataset collected by our group, and the results show that compared to DeblurGAN, the proposed method improves PSNR by 3.60 dB and SSIM by 0.09. The subjective deblurring effect is better, and the recovered infrared images have clear edge contours and detail information.
An improved latent low-rank representation(ILatLRR) is proposed to make the target more prominent and the background information more abundant after infrared and visible image fusion. First, the underlying layer obtained using LatLRR was decomposed at multiple levels to obtain additional underlying detail layers. Second, multilevel decomposition control was adopted based on the energy of the horizontal and vertical components of the detail layer and global contrast of the base layer to avoid invalid decomposition. Finally, different fusion strategies were adopted for the base and detail layers. The experimental simulations show that the fusion result of ILatLRR displays a sense of hierarchy; the image is clear; and the texture is rich. The contour details of the infrared thermal radiation target are maintained, retaining a large number of visible light image background features, with an objective evaluation index better than those of other algorithms.
To improve the temperature measurement accuracy of online infrared thermal imagers in foggy weather, the effects of distance, relative humidity, and fog on temperature measurement accuracy of infrared thermal imagers were studied. A secondary thermal infrared fault data acquisition system was used to build an experimental platform for temperature measurement experiments under single-and multi-factor interference,thereby obtaining a piecewise polynomial fitting relationship between distance and error temperature. Based on the prior theory of dark channel, the quantitative description of fog was realized, and the exponential function fitting relationship between transmittance and error temperature was obtained. By way of algebraic sum, an error compensation model was proposed to compensate the measurement error caused by the interaction of distance and fog. Experimental results show that this model can significantly improve the temperature measurement accuracy of thermal imagers. For an online infrared thermal imager, collecting and storing temperature data for a long time in foggy environments are of great significance in building an equipment fault data feature database.
Owing to comparatively small working distances, IR-guided weapons are usually used in either terminal air defense or complex guided weapons. Radar/IR and INS/IR composites have recently gained popularity as tactical weapons. Reliable switching to the infrared mode with low guide precision is an important technique in composite seekers, making scan-technique and view-compound obligatory approaches for reliable handover. Based on the characteristics of the diminutive coordinator, this study designs a quick-scan technique, integrating into the INS assembly arithmetic-based image characteristics.To address the anterior error of complex images, compound arithmetic based on the Kalman filter is designed, combining INS communication with image characteristics. Finally, through simulations, the method was demonstrated to effectively eliminate accumulated errors. High-quality image stitching can also be achieved in extremely specific scenarios. This method yields a larger infrared detection field of view.
An ICMOS is fabricated by directly coupling the output window of the image intensifier with a CMOS resulting in the characteristics of high sensitivity, fast response, and adjustable spectral range. In this study, the influence of the cathode, microchannel plate, phosphor screen, CMOS, and other components on the imaging performance of direct-coupled ICMOS are analyzed according to the composition of ICMOS, thereby proposing the principle for selecting the image intensifier and CMOS for ICMOS. The advantages of image intensifier manufacturing combined with the actual low-light imaging performance of directcoupling ICMOS are verified on an 18 mm NVT-7 image intensifier and 1-inch CMOS. The results show that the ICMOS camera can be used under 5×10-4 lx light conditions with a resolution of 16 lp/mm. In addition, the gain of the image intensifier for ICMOS should not exceed 4000 cd/(m2?lx), and the output brightness of the phosphor screen has minimal effect on performance under the condition of an appropriate gain.
Photomultiplier tubes(PMTs) are sensitive photodetectors used widely in numerous fields. To ensure the long-term stability of the PMT, the material should be carefully selected. As the cable jacket material is in contact with the liquid medium for extended periods, its compatibility with the liquid considerably affects the detection efficiency of the PMT. Therefore, the selection of the cable jacket material is of great significance. In this study, the variation in the transmittance of liquid samples after immersion in different jacket materials was measured using a spectrophotometer. The compatibility of four types of jacket materials(PFA, HDPE, FEP,and PTFE) with three liquids (pure water, oil, and liquid scintillator) was studied. The results show that the four types of jacket materials have good compatibility with water and oil but relatively poor compatibility with the liquid scintillator. Among these, PFA and FEP exhibited the best compatibility.
To improve the efficiency and accuracy of the field diagnosis of insulation layer deterioration of the cable intermediate joint, a non-contact diagnosis method based on adaptive deep learning of surface temperature is proposed. First, infrared thermal imaging was performed on the insulating surface of the cable joint and cables at both ends. The surface temperatures of multiple symmetric areas on both sides of the center of the cable joint and cables at both ends were collected without contact. Subsequently, a deep learning network based on a two-hidden autoencoder extreme learning machine was constructed to mine the deep hidden features in the surface temperature data. The extracted deep hidden features were used as input to the random forest diagnosis model. A quantum rotation gate with a nonlinear dynamic adaptive rotation angle was further proposed to improve the update strategy of the quantum firework algorithm and optimize the parameters of the diagnostic model. Finally, by combining the infrared temperature of the joint surface and loss angle tangent value of the insulating medium, a dataset was constructed to train and test the diagnostic model in the field. The experimental results show that the improved quantum fireworks algorithm can better approximate the global optimal solution and has high convergence efficiency. The deep learning random forest diagnostic model exhibited strong feature extraction and classification capabilities, whereby the classification accuracy and stability of the diagnostic model were effectively improved after parameter optimization, and better diagnostic results were achieved under the condition of a small sample training set. Therefore, noncontact diagnosis of joint insulation deterioration is achievable.
This study proposes an improved YOLO v5 method to solve the problems of inaccurate classification and insufficient feature extraction from power equipment infrared images. First, the visible light data and infrared images of the power equipment were fused using the transfer learning method. The triplet attention mechanism was then embedded into the feature extraction network for weighted intensification of key feature information. Finally, different targets were identified using multiscale fusion.The results show that compared with faster R-CNN and SSD, the proposed method has higher recognition accuracy and efficiency and is suitable for image recognition of multi-type power equipment in complex backgrounds. This method realizes a lightweight network model with a size of only 4.1 MB, which is a reduction of 80.8% compared to that of SSD, providing a novel and feasible scheme for intelligent infrared image detection of power equipment.
Based on the chalcogenide glass IRG206 (As40Se60) substrate, 3.7?4.8 ?m and 7.5?9.5 ?m dualband antireflection coatings were developed, which can be used in automotive infrared night-vision imaging systems. Using electron beam and resistance evaporation ion-assisted deposition technology, combined with chemical bond analysis, the connecting layer material was selected to improve the adhesion between the substrate and film layer. Ion-assisted stress control technology was used to optimize the stress of the film layer and realize the matching of the stress of the film layer, to solve the problem of chalcogenide glass stripping.In test results, the average transmittance of the film layer in the 3.7?4.8 ?m and 7.5?9.5 ?m bands was 98.31% and 97.43%, respectively, passing environmental tests such as firmness, salt spray, high and low temperature,and abrasion, thereby satisfying the requirements of the automotive infrared night-vision imaging system.