In order to solve the problem of target detection in infrared images, a single-frame infrared point target detection method based on YOLO-v5 is proposed. Firstly, the problems and difficulties of single-frame infrared target detection are analyzed, and the principles of three commonly used single-frame detection algorithms based on background prediction are introduced. Then the algorithm principle and network structure of YOLO-v5 are elaborated in detail. Finally, 12 groups of infrared images with different backgrounds are constructed for target detection experiments, and the experimental results prove that YOLO-v5 has great advantages over traditional single-frame detection algorithms.

To address the new functional requirements brought by the introduction of new weapons and new combat modes, a comprehensive survey of the research progress in the area of combat simulation software is performed from the perspective of software engineering. First, the top-level specification, simulation engine, and simulation framework of combat simulation software are reviewed. Then, several typical combat simulation software systems are demonstrated, and the relevant software frameworks are analyzed. Finally, combining the application prospect of artificial intelligence, metaverse, and other new technologies in combat simulation, the development trends of combat simulation software are presented, namely intellectualization, adaptation to an LVC (live, virtual, and constructive) system, and a more game-based experience. Based on a comprehensive comparison between the mentioned simulation frameworks, we believe that the AFSIM (Advanced framework for simulation, integration, and modeling) and the E-CARGO (Environments—classes, agents, roles, groups, and objects) are appropriate candidates for developing distributed combat simulation software.

为研究俄乌冲突中空天攻防武器装备的作战应用,根据公开情报信息从空天打击和防空反导两方面梳理了俄乌双方参战的主要武器装备及其典型性能参数,从高超声速武器作战、导弹攻防作战、空中攻防作战和卫星支援作战四个方面分析了双方交战过程中空天攻防的典型案例.根据双方作战过程中的经验和教训,从抢占战场空天制权、提升通信保障能力、坚持动能反导路线、拓展反无人机样式等方面为我国空天攻防装备能力建设和作战应用提出启示建议.

The assembly of overlapping grids is a key technology to deal with the relative motion of multi-bodies in computational fluid dynamics. However, the conventional implicit assembly techniques for overlapping grids are often confronted with the problem of complicated geometry analysis, and consequently, they usually have a low parallel assembly efficiency resulting from the undifferentiated searching of grid nodes. To deal with this, a parallel implicit assembly method that employs a two-step node classification scheme to accelerate the hole-cutting operation is proposed. Furthermore, the aforementioned method has been implemented as a library, which can be conveniently integrated into the existing numerical simulators and enable efficient assembly of large-scale multi-component overlapping grids. The algorithm and relevant library are validated with a seven-sphere configuration and multi-body trajectory prediction case in the aspects of parallel computing efficiency and interpolation accuracy.

There are many multifaceted problems in the aerospace engineering domain, which demand the multidisciplinary simulations for coupling analysis and the development of coupling frameworks. In order to address the issue of lacking domestic coupling framework, we propose an aerodynamics-centric framework for multidisciplinary coupling analysis, and then demonstrate the usage of it in developing a structured-unstructured grid coupling software, which is validated with an external store separation case study.

Aiming at the detection of dim and small moving point targets in space-based infrared (IR) imaging systems, a detection method based on spatial-temporal local contrast (STLC) is presented. Firstly, use the spatial and temporal information to estimate the background gray value of each pixel, and obtain two prediction results respectively. Then, the spatial local contrast (SLC) and the temporal local contrast (TLC) are defined according to the original image and two estimated backgrounds. Next, the STLC is defined as the product of SLC and TLC. Finally, a threshold value is set in the STLC for target detection. Four groups of experiments are conducted, and the simulation results indicate that the proposed method has a great advantage in terms of background suppression factor (BSF) and gain of signal-to-noise (GSNR).

Exo-atmospheric infrared (IR) point target discrimination is an important research topic of space surveillance systems. It is difficult to describe the characteristic information of the shape and micro-motion states of the targets and to discriminate different targets effectively by the characteristic information. This paper has constructed the infrared signature model of spatial point targets and obtained the infrared radiation intensity sequences dataset of different types of targets. This paper aims to design an algorithm for the classification problem of infrared radiation intensity sequences of spatial point targets. Recurrent neural networks (RNNs) are widely used in time series classification tasks, but face several problems such as gradient vanishing and explosion, etc. In view of shortcomings of RNNs, this paper proposes an independent random recurrent neural network (IRRNN) model, which combines independent structure RNNs with randomly weighted RNNs. Without increasing the training complexity of network learning, our model solves the problem of gradient vanishing and explosion, improves the ability to process long sequences, and enhances the comprehensive classification performance of the algorithm effectively. Experiments show that the IRRNN algorithm performs well in classification tasks and is robust to noise.

External atmospheric infrared (IR) target recognition is an important research topic of space surveillance systems. The different micromotion states of the target result in respective features in the obtained sequence of IR radiation intensities, and such differences are difficult to visually describe and difficult to extract efficiently. Due to the difficulty of spatial experiments, the target sample data is very limited. In the case of limited labeled samples, there are few methods that can effectively classify time series effectively, resulting in low classification accuracy and difficulty in meeting practical application requirements. We use generative adversarial networks(GAN) for semi-supervised learning, combining neural network classifiers with adversarial generation models for the classification of infrared grayscale time series of spatial targets. The effective classification of the target is achieved with a small number of labeled target samples. The experiment proves the effectiveness of the method and the classification effect is better than other methods.

Object discrimination plays an important role in infrared (IR) imaging systems. However, at long observing distance, the presence of detector noise and absence of robust features make exo-atmospheric object classification difficult to tackle. In this paper, a recurrence-plots-based convolutional neural network (RP-CNN) is proposed for feature learning and classification. First, it uses recurrence plots (RPs) to transform time sequences of IR radiation into two-dimensional texture images. Then, a CNN model is adopted for classification. Different from previous object classification methods, RP representation has well-defined visual texture patterns, and their graphical nature exposes hidden patterns and structural changes in time sequences of IR signatures. In addition, it can process IR signatures of objects without the limitation of fixed length. Training data are generated from IR irradiation models considering micro-motion dynamics and geometrical shape of exo-atmospheric objects. Results based on time-evolving IR radiation data indicate that our method achieves significant improvement in accuracy and robustness of the exo-atmospheric IR objects classification.

Infrared imaging is widely applied in the discrimination of spatial targets. Extracting distinguishable features from the infrared signature of spatial targets is an important premise for this task. When a target in outer space experiences micro-motion, it causes periodic fluctuations in the observed infrared radiation intensity signature. Periodic fluctuations can reflect some potential factors of the received data, such as structure, dynamics, etc., and provide possible ways to analyze the signature. The purpose of this paper is to estimate the micro-motion dynamics and geometry parameters from the observed infrared radiation intensity signature. To this end, we have studied the signal model of the infrared radiation intensity signature, conducted the geometry and micro-motion models of the target, and we proposed a joint parameter estimation method based on optimization techniques. After analyzing the estimation results, we testified that the parameters of micro-motion and geometrical shape of the spatial target can be effectively estimated by our estimation method.

Infrared (IR) moving point target detection is an important technique in many onboard applications such as remote sensing, IR searching and tracking (IRST) and early warning system. However, it has been facing great challenges due to the complicated background and the limited processing resources in the onboard system. In this paper, a novel spatial-temporal local contrast method is proposed for moving point target detection in space-based IR imaging system. Firstly, a simple but effective spatial filter based on multi-direction filtering fusion is designed to obtain the spatial local contrast map (SLCM). An enhanced time domain difference method is also proposed to obtain the temporal local contrast map (TLCM). Then, the spatial-temporal local contrast map (STLCM) is obtained by multiplying the newly defined SLCM and TLCM. Finally, an enhanced threshold segmentation method is proposed for target detection and false alarm suppression. To verify the performance of our detection algorithm, we conduct several groups of experiments on four different real IR image sequences with simulated targets. The final experimental results show that our algorithm significantly outperforms other methods in terms of background suppression and target detection.

Waveforms classification is an important task in many applications such as disease diagnosis, earthquake prediction and speech recognition. In this paper, a sparse representation based method is proposed for waveforms classification. Firstly, K singular value decomposition (K-SVD) method is applied to each class of training samples to obtain a corresponding dictionary. Then, for a test sample, it is sparsely represented and reconstructed by each dictionary respectively, and assign it to the class with the smallest reconstruction error. To verify the classification ability of the proposed method, two experiments on both simulated and real-world data sets are conducted. The final experimental results demonstrate that our proposed method can obtain a good performance in terms of the classification accuracy and noise tolerance.

The curved debris separated from rockets, the most objects frequently appeared in the threat complex, make the classification of ballistic targets very hard. However, rocket debris has not drawn enough research attention and current data generative model of convex objects is not applied to non-closed curved debris. This paper explores the method of modeling the infrared (IR) irradiance signatures of rocket debris to support the research of target classification in the remote detection. The IR irradiance intensity of non-closed debris is formulated as a parametric function of geometrical shape patches, line-of-sight of IR sensor, and other radiant coefficients. Further, IR signature modeling is achieved with respect to debris' rotational motions. The data generative models of missile debris would become an important data source for ballistic target classification.

A modeling and simulation method of infrared signature of remote aerial targets is presented in this paper. It takes a comprehensive consideration of the influence of shape, material, attitude, motion and surface temperature field of the target. Firstly, the geometric shape of targets are modeled. Then motion models are built to obtain the position and attitude of targets in real time. Next, we divide the target surface into hundreds of grids and calculate its temperature distribution by establishing a thermal equilibrium equation for each grid. Finally, the infrared radiation power received by the detector from the target can be achieved according to above analyses and the observation condition. Simulation results demonstrate that targets with different shape or different motion information have different infrared radiation characteristics, which provides a feasibility of using infrared signature for remote aerial targets recognition.

Micro-motion dynamics and geometrical shape are considered to be essential evidence for infrared (IR) ballistic target recognition. However, it is usually hard or even impossible to describe the geometrical shape of an unknown target with a finite number of parameters, which results in a very difficult task to estimate target micro-motion parameters from the IR signals. Considering the shapes of ballistic targets are relatively simple, this paper explores a joint optimization technique to estimate micro-motion and dominant geometrical shape parameters from sparse decomposition representation of IR irradiance intensity signatures. By dividing an observed target surface into a number of segmented patches, an IR signature of the target can be approximately modeled as a linear combination of the observation IR signatures from the dominant segmented patches. Given this, a sparse decomposition representation of the IR signature is established with the dictionary elements defined as each segmented patch's IR signature. Then, an iterative optimization method, based on the batch second-order gradient descent algorithm, is proposed to jointly estimate target micro-motion and geometrical shape parameters. Experimental results demonstrate that the micro-motion and geometrical shape parameters can be effectively estimated using the proposed method, when the noise of the IR signature is in an acceptable level, for example, SNR>0  dB.

Infrared small target detection is an extremely challenging problem, especially under a complex background. Generally, targets can be easily detected by some simple and fast algorithms in the homogeneous area, but in the heterogeneous area, advanced and complicated algorithms are always needed. Therefore, heterogeneous area extraction is an important task for us to use different detection methods in different backgrounds to achieve simplifying computation while maintaining high detection performance. In this paper, a novel heterogeneous area extraction approach is proposed. Firstly, a traditional background suppression algorithm named mean filter is used to detect a group of interesting points. Then, a new adaptive clustering algorithm based on region growing is proposed to cluster the interesting points into several clusters. Finally, heterogeneous areas can be determined according to the size of cluster and the density of interesting points in the cluster. Experimental results show that our proposed method can extract heterogeneous areas of any size quickly and accurately.

The dynamic characteristics related to micro-motions, such as mechanical vibration or rotation, play an essential role in classifying and recognizing ballistic targets in the midcourse, and recent researches explore ways of extracting the micro-motion features from radar signals of ballistic targets. In this paper, we focus on how to investigate the micro-motion dynamic characteristics of the ballistic targets from the signals based on infrared (IR) detection, which is mainly achieved by analyzing the periodic fluctuation characteristics of the target IR irradiance intensity signatures. Simulation experiments demonstrate that the periodic characteristics of IR signatures can be used to distinguish different micro motion types and estimate related parameters. Consequently, this is possible to determine the micro-motion dynamics of ballistic targets based on IR detection.

A novel waveforms classification method based on convolutional neural networks (CNN) is proposed in this paper. Firstly, convolution and pooling operations are cross used for generating deep features, and then fully connected to the output layer for classification. Different from other traditional approaches which need human-designed features, CNN can discover and extract the suitable internal structure of the input waveform to obtain deep features for classification automatically. So that the generalization ability of this method is significantly improved comparing to other methods. Experimental results show that CNN can obtain state of the art performance for waveforms classification in terms of classification accuracy and noise tolerance.

Point target detection in space-based infrared (IR) imaging system is an important task in many applications such as IR searching and tracking and remote sensing. Although it has attracted great interest and tremendous efforts during last decades, it remains a challenging problem due to the uncertain heterogeneous background and the limited processing resources on the planet. Aiming at this problem, a novel background suppression method based on multi-direction filtering fusion is proposed in this paper. The process of background prediction for each pixel by this method can be divided into two steps. Firstly, eight predicted values are obtained by using linear filtering methods along eight different directions respectively. Then, Gaussian weighted sum of the eight predicted values is computed to generate the final result. We conduct several groups of experiments on different categories scenes with simulated targets, and the final experimental results demonstrate that our methods can not only obtain state-of-the-art performance on background suppression (especially for heterogeneous backgrounds), but also detect targets accurately with low false alarm rate and high speed in IR point target detection tasks.

Time series classification is an important task in time series data mining, and has attracted great interests and tremendous efforts during last decades. However, it remains a challenging problem due to the nature of time series data: high dimensionality, large in data size and updating continuously. The deep learning techniques are explored to improve the performance of traditional feature-based approaches. Specifically, a novel convolutional neural network (CNN) framework is proposed for time series classification. Different from other feature-based classification approaches, CNN can discover and extract the suitable internal structure to generate deep features of the input time series automatically by using convolution and pooling operations. Two groups of experiments are conducted on simulated data sets and eight groups of experiments are conducted on real-world data sets from different application domains. The final experimental results show that the proposed method outperforms state-of-the-art methods for time series classification in terms of the classification accuracy and noise tolerance.