With the development of infrared technology, infrared dim and small target detection plays an essential role in precision guidance and early warning systems. Due to the low contrast and signal-to-noise ratio that characterizes infrared dim and small target in images, the dim and small target can easily be drowned out by noise and background. A new infrared dim and small target detection network (CA-U2-Net) is proposed to address the challenge of infrared weak target detection and shape retention in complex backgrounds. Specifically, firstly, the U2-Net network structure has been improved to prevent the loss of shallow information due to increased network depth and to make it more suitable for detecting the dim and small target. Then, the upper and lower attention module was designed on the network to make the model more focused on dim and small target features while suppressing irrelevant information, further improving the detection rate. Finally, a contour detection branch was added to the top of the model to fuse the contour detection map with the feature map to get a better target shape. After experimental evaluation, the method achieved a detection rate of 97.17% and retained a more accurate infrared dim and small target shape. Compared to other advanced methods, our method performs better in detection rate, false detection rate and shape retention. In addition, a new infrared dim and small target dataset consisting of 10,000 images was constructed.

With spring up of infrared imaging related industry, infrared imaging technology has become mainstream development direction of intelligent photoelectrical detection due to its good concealment, wide detection range, high positioning accuracy, long distant penetration, light weight, little volume, low power dissipation and high solidity. However, the features of infrared dim-small target image such as less details and low SNR become bottleneck of infrared image application. How to enhance imaging effect of infrared dim-small target becomes research hotspot. Starting from the point of ‘restoration as foundation’, the theory and technology of infrared dim-small target super-resolution restoration by utilizing the theory and technology of super-resolution restoration are explored in this paper. This paper mainly focuses on the research of super-resolution restoration algorithm of infrared dim-small target based on infrared micro-scanning optical model. Aiming at solving super-resolution restoration problem of infrared dim-small target, the traditional super-resolution restoration algorithm is optimized and the improved algorithm is proposed. Meanwhile, infrared micro-scanning optical model is introduced to break theoretical limit of simple image processing algorithm. And the performance of infrared image super-resolution restoration is improved.

The inherent shortcoming of POCS (Projection Onto Convex Sets) is its sensitiveness to noise. The restoration quality of POCS based super resolution will severely decline when the noise is larger. In practical applications, the low resolution images generally include some kinds of noise, such as camera internal noise, transmission system noise and coherent noise. Therefore POCS cannot be used directly in super-resolution restoration for observed low resolution images. In order to solve the noise sensitive problem of the traditional POCS restoration algorithm, we firstly propose to optimize the BM3D (Block-Matching 3D) filtering by mean pre-screening of image blocks and limiting the number of image blocks. Then we combine the optimized BM3D filtering with POCS restoration in this paper. Experimental results show that the proposed POCS super resolution restoration algorithm based on BM3D can achieve better restoration effect when the low resolution images contain noise. Furthermore no noise can be perceived in the restored high resolution image basically.

Infrared tiny target detection is faced with difficulty that tiny-sized targets are easily drowned out in complex backgrounds. To address this problem, this paper presents an end-to-end deep learning framework, which consists of an anchor-free U-shaped backbone for extracting image features and a detection head block for target attributes regression. To capture fine-grained details and semantic information, a multi-hop and multiscale fusion block is designed in the backbone for effective progressive feature fusion. Moreover, to adaptively adjust the constraints for the localization and shape description of the targets, a tolerance-adjustable measure is proposed in the detection head to provide a reliable means for determining the authenticity of targets. Extensive experiments indicate that our trained model can simultaneously detect infrared targets with various sizes, especially achieve an excellent detection performance for point targets with 98.2% F1-score, 3.1% error rate and 0.5% omission rate. Comparative experiments indicate that the proposed method is superior to SOTA deep learning based methods, especially for tiny targets.

Based on the higher order cumulates, OFDM signals are discriminated from single-carrier signals by the proposing feature. Determination of the modulation type for sub carriers of an OFDM signal is carried out in the paper by combined features. The performances of proposed algorithm are tested using Monte-Carlo simulations. It is shown by the results that the algorithm is stable with a low complexity and high percentage correct identification for the modulation identification of OFDM signals.

In this paper, we propose a novel equivalent composite control architecture for electro-optical equipment. The improved tracking performance and loss of robustness caused by this structure have a clear relationship with $a_2$, the time coefficient of the compensation circuit. The compensation circuit can make the speed quality factor and the acceleration quality factor of the system infinite, and the jerk quality factor can be expanded to $1/a_2$ times the original acceleration quality factor, but it will cause a main zero point of the servo system to be far away from the virtual axis and the main poles to be close to the virtual axis. As the time coefficient of the compensation loop controller decreases, the tracking performance of the system increases, but the robustness decreases, the dynamic response deteriorates, the water bed effect becomes more obvious, and the system is more susceptible to noise and disturbances. Compared to the existing method, our method focuses on system performance and robustness. Experimental results show that our method can achieve target tracking with a peak accuracy of 64$″$ and 22$″$ (RMS), which is superior to the tracking performance without equivalent composite control.