Journals >Electronics Optics & Control
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2025
Volume: 32 Issue 4
16 Article(s)
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On Solution of Dynamic Attack Area Under Active Defense of the Enemy
GE Jun, LI Haonan, LI Zhongchao, WANG Teng, and ZHANG Hairuo
Active defense is a novel combat technology that significantly enhances the effectiveness of aircraft air combat. It is a process in which the aircraft intercept enemy missiles by actively launching missiles to avoid their threats. The traditional attack zone models fall short in addressing the computational needs for Active defense is a novel combat technology that significantly enhances the effectiveness of aircraft air combat. It is a process in which the aircraft intercept enemy missiles by actively launching missiles to avoid their threats. The traditional attack zone models fall short in addressing the computational needs for decision-making support in active defense air combat. In order to solve this problem, this paper establishes a new model to evaluate the decision-making index of air combat, namely the dynamic attack zone under the condition of active defense of the enemy, and analyzes the connotation of the dynamic attack zone. Firstly, based on differential game theory, a game model of target-defensive missile-attack missile is established, and the optimal pursuit-escape control strategy under the background of dynamic game is solved. Then, according to the actual constraints of active defense process, a dynamic attack zone boundary solution strategy based on improved advance and retreat method is designed. Finally, a variety of typical situations are selected to simulate and verify the dynamic attack zone model. The experimental results show that the dynamic attack zone under the condition of the enemy’s active defense can accurately represent the launching range of the attack missile, which provides more clear quantitative guidance information for air combat decision-making..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 1 (2025)
Twistor-Based Integrated Modeling and Control of Quadrotor UAV Position and Attitude
ZHOU Jiaxing, CHEN Wei, CHEN Xiang, LI Qing, DENG Yifan, and CHEN Yuhao
The traditional quadrotor UAV uses dual quaternion description method to realize the integrated modeling of position and attitude. However, dual quaternions need to satisfy normalization constraints and have parameter redundancy. Addressing the aforementioned issues, this paper proposes an integrated position and attitThe traditional quadrotor UAV uses dual quaternion description method to realize the integrated modeling of position and attitude. However, dual quaternions need to satisfy normalization constraints and have parameter redundancy. Addressing the aforementioned issues, this paper proposes an integrated position and attitude modeling method based on twistor, which utilizes only six parameters to describe position and attitude movements, avoiding normalization constraints and parameter redundancy. To enhance the robustness and control performance of the quadrotor UAV, an integrated position and attitude controller within the twistor framework is proposed based on sliding mode theory. The stability of proposed system is proven by using Lyapunov theory and the LaSalle invariant set principle. Finally, the accuracy of twistor-based position and attitude integrated modeling is verified through simulation, and the effectiveness and robustness of the sliding mode controller within the twistor framework is demonstrated..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 8 (2025)
An SAR Ship Detection Algorithm Based on Receptive Field Enhancement and Cross-Scale Fusion
HUANG Yingzheng, LIU Gang, YAN Shuguang, and HOU Enxiang
In view of the complex maritime background, ship targets with large scale changes and noise interference, the ship detection accuracy of Synthetic Aperture Radar (SAR) is low and the missed detection is serious. An improved YOLOv7 model is proposed to solve these problems. Firstly, the Receptive Field Enhancement FeatuIn view of the complex maritime background, ship targets with large scale changes and noise interference, the ship detection accuracy of Synthetic Aperture Radar (SAR) is low and the missed detection is serious. An improved YOLOv7 model is proposed to solve these problems. Firstly, the Receptive Field Enhancement Feature Extraction Module (RFEFM) is designed to reconstruct the backbone network, enhance the receptive field and improve the multi-scale target feature extraction ability. Secondly, a High-Low Dimensional Feature Fusion Pyramid Network (HLF-FPN) is proposed to filter the noise and background information of interference and efficiently fuse the information of different scales. Then, a new F-MPDIoU loss function is proposed, which accelerates the convergence of the model and improves the problems of missed detection and false detection. Finally, the experiment on HRSID dataset shows that compared with the original YOLOv7 model, the proposed model improves the mAP@0.5, accuracy and recall by 4.9, 9.4 and 13.4 percentage points respectively, with the value of FPS reaches 68 frames per second, which meet the requirements of real-time detection..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 17 (2025)
An Infrared Maritime Ship Detection Algorithm Based on Improved YOLOv7
RAO Xingchang, ZHENG Yingying, LU Wanhao, and HUANG Sungang
Aiming at the problems of false detection and missed detection in the infrared maritime ship image detection in the scene of near-shore dense, far-sea small target and low-resolution, as well as making the model lighter, an improved infrared maritime ship detection algorithm based on YOLOv7 is proposed. In order to enhAiming at the problems of false detection and missed detection in the infrared maritime ship image detection in the scene of near-shore dense, far-sea small target and low-resolution, as well as making the model lighter, an improved infrared maritime ship detection algorithm based on YOLOv7 is proposed. In order to enhance the feature extraction capability of the backbone network, REP-DSConv-ELAN module is reconstructed to replace ELAN module in original network. Secondly, the InceptionNeXt module is introduced into the neck network to reduce the loss of high-dimensional characteristic information caused by the increase of network depth, and to better carry out multi-scale fusion to improve the detection effect of ships. Finally, the boundary box regression loss function with minimum point distance, namely MPDIoU is used in the detection head to enhance the detection ability in the low-resolution small target scenes. Experimental results on infrared ship dataset show that the precision, recall and mean average precision of the improved algorithm are increased by 3.99, 2.55 and 3.40 percentage points respectively, compared with original YOLOv7 algorithm, and parameters is reduced from 37.23×106 to 31.98×106. In conclusion, the improved algorithm can effectively ameliorate the problems of false detection and missed detection while ensuring the accuracy of infrared ship detection..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 23 (2025)
An Improved YOLOv7-tiny Ship Recognition Algorithm Based on Channel Pruning
ZHANG Shang, XIONG Zhongyue, and WANG Hengtao
Ship target identification at sea is a crucial part of maritime monitoring and a significant solution for national security in coastal regions worldwide. Aiming at the problems of low recognition accuracy and large training model in ship target detection in SAR images, an improved YOLOv7-tiny maritime ship recognition Ship target identification at sea is a crucial part of maritime monitoring and a significant solution for national security in coastal regions worldwide. Aiming at the problems of low recognition accuracy and large training model in ship target detection in SAR images, an improved YOLOv7-tiny maritime ship recognition algorithm based on channel pruning is proposed. Firstly, the original backbone network is replaced by MobileNetV3 to reduce the calculation and volume of the model and realize the lightweight of the model. Secondly, MPDIoU is introduced to simplify the calculation process and optimize the convergence of the model. Finally, through channel pruning, the model accuracy is improved, while the reduction of model volume and calculation amount is balanced, and the network model is further optimized. The experimental results show that compared with YOLOv7-tiny, the improved algorithm improves the recall and mAP by 5.85 and 3.69 percentage points respectively, the parameter is reduced by 63.35%, and the FLOPs is reduced by 70%..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 31 (2025)
An Improved Vovnet Remote Sensing Target Detection Algorithm Based on Context Information Fusion
ZHANG Zhaoheng, LIU Yunqing, YAN Fei, and ZHANG Qiong
Aiming at the problems of dense target distribution, complex background and many small targets in remote sensing image target detection, this paper improves Vovnet, adds a CoT global feature extraction module to the feature extraction backbone, which cooperates with cross-perspective feature extraction, and retains theAiming at the problems of dense target distribution, complex background and many small targets in remote sensing image target detection, this paper improves Vovnet, adds a CoT global feature extraction module to the feature extraction backbone, which cooperates with cross-perspective feature extraction, and retains the perspective information of receptive field on multiple scales to extract the context information of targets for different scales and enhance visual representation. At the same time, a context information fusion module, namely MSSFPN, is designed based on FPN, which is built on the deep feature map. The image features are fused at the scale level to enhance the feature representation of the target. The depth hyperparametric convolution layer is introduced for prediction, and independent weights are adopted for the feature map of each channel so that the network can adapt to the image features extracted in different scales to improve detection accuracy. Compared with the original Vovnet algorithm, the mAP of the improved algorithm in the public Visdrone dataset is improved by 6.80 percentage points, which is also superior to other target detection algorithms. Experimental results further verify the accuracy and effectiveness of the improved algorithm in target detection in remote sensing images..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 37 (2025)
A Small Target Detection Algorithm in Aerial Images Based on Improved RT-DETR
XUAN Suihan, LUO Yinsheng, and SONG Wei
Real-time and accurate localization and identification of target such as aircraft, ships and vehicles in aerial images is the fundamental basis for further decision-making. Aiming at the issues of low efficiency and accuracy in detecting small targets in aerial images, this paper proposes an improved RT-DETR based smalReal-time and accurate localization and identification of target such as aircraft, ships and vehicles in aerial images is the fundamental basis for further decision-making. Aiming at the issues of low efficiency and accuracy in detecting small targets in aerial images, this paper proposes an improved RT-DETR based small target detection algorithm in aerial images. Firstly, an efficient CCFM-P2ASF scale sequence feature fusion module is constructed to obtain more semantic information and improve the sensitivity to small targets. Secondly, it integrates more flexible learnable positional coding to provide clearer position definition, and then, a more efficient bounding loss function is designed to reduce the deviation of target location prediction and provide more accurate bounding information. Finally, an EMA reparameterization response module is designed to extract input image features more efficiently. The experimental results show that the model size of the improved RT-DETR is reduced by 38.3%, the accuracy, mAP50 and mAP50∶95 is increased by 5.1, 5.0, and 2.2 percentage points respectively compared with the original model. The proposed algorithm achieves better detection performance compared with other mainstream algorithm models..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 44 (2025)
Ship Object Detection with Lightweight Attention Mechanism and Cross-Scale Fusion
LI Dongqin, PENG Qi, and WU Yang
A lightweight attention mechanism and cross-scale fusion based ship target detection algorithm is proposed to address the issues of slow detection speed and low detection rates caused by limited computing power resources onboard. Based on YOLOv5s algorithm, a lightweight attention mechanism of SimAM is introduced into A lightweight attention mechanism and cross-scale fusion based ship target detection algorithm is proposed to address the issues of slow detection speed and low detection rates caused by limited computing power resources onboard. Based on YOLOv5s algorithm, a lightweight attention mechanism of SimAM is introduced into the backbone network and fused cross-scale with the neck network, thereby improving the detection accuracy of the algorithm. Lightweight convolutions of C3Ghost and GhostConv are incorporated to reduce the parameters of the detection algorithm, enabling real-time ship detection. For bounding box regression loss, adaptive parameters are employed to enhance the adaptability and robustness of anchor box. Finally, comparative and ablation experiments with mainstream algorithms are conducted on the SeaShips dataset. The experimental results validate the effectiveness of the proposed algorithm..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 52 (2025)
On Influencing Factors of Anti-submarine Effectiveness of Sonar Buoys
LIU Xubo, and NIAN Xuan
In view of the influencing factors of the anti-submarine effectiveness of sonar buoys, the combat process of aviation anti-submarine is analyzed. In the initial stage of the mission, according to the characteristics of the superior information, the probability distribution model of submarine is established, and the MonIn view of the influencing factors of the anti-submarine effectiveness of sonar buoys, the combat process of aviation anti-submarine is analyzed. In the initial stage of the mission, according to the characteristics of the superior information, the probability distribution model of submarine is established, and the Monte Carlo-like method is used to visually characterize the submarine distribution in several typical combat scenarios. In the mission planning stage, based on the law of large numbers, the influence characteristics and mechanism of the geometric elements of the buoy array on the anti-submarine effectiveness are obtained by using the method of multiple statistical treatment of simulation test results. In the mission execution stage, the influence characteristics of ocean currents on the anti-submarine effectiveness of sonar buoys are clarified by comparing and analyzing the simulation results. Based on the research results of influencing factors, the design focus of the anti-submarine auxiliary decision-making system is proposed..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 65 (2025)
Adaptive Disturbance Rejection Control Algorithm for Airborne Optoelectronic Stabilized Platform
GUO Haorui, HU Leili, LI Li, and WANG Jian’gang
Aiming at the problems of large temperature variation range, obvious parameter perturbation of current airborne optoelectronic stabilized platform, and insufficient disturbance suppression ability of existing control methods, a Model Reference Adaptive Linear Active Disturbance Rejection Control (MRALADRC) method is prAiming at the problems of large temperature variation range, obvious parameter perturbation of current airborne optoelectronic stabilized platform, and insufficient disturbance suppression ability of existing control methods, a Model Reference Adaptive Linear Active Disturbance Rejection Control (MRALADRC) method is proposed. Firstly, a Model-based Linear Extended State Observer (MLESO) is designed to realize perturbation observation, combined with the analysis of parameter perturbation by generalized root locus method and the idea of Model Reference Adaptive Control (MRAC) of automatic control parameter, adjusting an adaptive disturbance rejection control law is designed, and the stability is analyzed by Lyapunov method. The simulation results show that when the characteristic gain is 0.2, 1 and 5 times of the original value respectively, compared with Model-based Linear Active Disturbance Rejection Control (MLADRC) method, the peak value and variance of the disturbance isolation error of the stabilized platform are greatly reduced after adopting the proposed method. In step response experiment, when the characteristic gain is 0.44, 0.66, 1.5 and 2.25 times of the original value, the step response time of MLADRC increases by 24%~122%, while the adjustment time of MRALADRC remains unchanged. It can be seen that MRALADRC method can effectively improve the disturbance isolation ability of optoelectronic stabilized platform and reduce the dynamic error in the presence of large-scale parameter perturbations, it also shows strong robustness..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 71 (2025)
Time-sensitive Networking Traffic Scheduling Method Based on Time Windows
LI Chao, LI Hongshuo, DONG Zhe, SHI Yuntao, and LI Wenhao
Time-Sensitive Networking (TSN) is a new type of deterministic network. As its core mechanism, traffic scheduling mainly guarantees the quality of service of time-triggered stream transmission through Gate Control List (GCL). However, the design of gated scheduling in terms of frames requires the calculation of the speTime-Sensitive Networking (TSN) is a new type of deterministic network. As its core mechanism, traffic scheduling mainly guarantees the quality of service of time-triggered stream transmission through Gate Control List (GCL). However, the design of gated scheduling in terms of frames requires the calculation of the specific time slot allocation for each frame, which has the problem of excessive computational complexity. For this reason, in order to address the complexity of solving the time slot configuration, the scheduling object is improved from frames to time windows. An Integer Linear Programming (ILP) scheduling method is designed based on time windows and its results are obtained using Gurobi optimizer. The simulation results show that the proposed scheduling method effectively reduces the computational complexity based on guaranteeing the flow characteristics of time-triggered flow. Compared with the frame-based scheduling method, the total end-to-end delay is reduced by about 6% and the solution time is reduced by about 41%..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 77 (2025)
A Change Detection Method Combining Multi-Level Features and Global Features
YU Yongxun, ZHANG Zhaoxiang, and ZHANG Shengwei
With the development of deep learning technology, some achievements have been made in the field of change detection. However, there are still problems such as inaccurate detection of the edges of the change region and incomplete detection of the interior of the change region in the existing change detection methods. InWith the development of deep learning technology, some achievements have been made in the field of change detection. However, there are still problems such as inaccurate detection of the edges of the change region and incomplete detection of the interior of the change region in the existing change detection methods. In view of this, this paper proposes a change detection method that combines multi-level features and global feature, in which a feature extraction network with dense connection of features between different levels is designed based on the Siamese network and the encoder-decoder architecture to fully extract and fuse features from different levels. For the fused features, this paper also designs a global feature modelling module to model their global context information. Moreover, a difference feature enhancement module is embedded between the encoder and the decoder to strengthen the learning of difference features of bi-temporal images. The proposed method is compared with some mainstream methods on the large-scale public datasets CDD and SYSU-CD through experiments, and the results show that the proposed method has good performance on both datasets..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 82 (2025)
An Small Object Drone Detection Method in Complex Background
ZHOU Lei, MOU Yi, and CHEN Weizhen
To address the issues of low detection accuracy and false or missed detection of small-sized drones in complex flying environments such as schools and parks, a small target drone detection method based on the improved YOLOv8n is proposed. Firstly, drone images in different flight backgrounds are collected to build an eTo address the issues of low detection accuracy and false or missed detection of small-sized drones in complex flying environments such as schools and parks, a small target drone detection method based on the improved YOLOv8n is proposed. Firstly, drone images in different flight backgrounds are collected to build an experimental dataset. Secondly, the multi-scale feature fusion network is redesigned, introducing TPE and SSFF modules to improve the multi-scale feature fusion method of the neck network, and a small target detection layer is added to enhance the network ability to resist background interference and the detection accuracy for small targets. Finally, Inner-CIoU is used as the loss function of the improved model to enhance the model detection accuracy and convergence speed. Experimental results on the self-built drone Anti-Drone dataset show that compared with YOLOv5s, YOLOv7-tiny, YOLOv7, and YOLOv8s algorithms, the proposed method increases the value of mAP50 by 0.8, 15.5, 9.8, and 5.2 percentage points respectively, which demonstrates the effectiveness of the improved method in detecting small-scale drones in complex backgrounds..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 89 (2025)
Low SNR Drone Radio Frequency Signal Identification Based on YOLOv8n-PEM
LIU Kun, KONG Lingxuan, and YAN Xingwei
Aiming at the problem that the current drone RF signal identification models have low identification accuracy under the condition of low SNR, and do not support the key parameters such as detection signal duration and bandwidth, this paper proposes a drone radio frequency signal identification method based on YOLOv8n-PAiming at the problem that the current drone RF signal identification models have low identification accuracy under the condition of low SNR, and do not support the key parameters such as detection signal duration and bandwidth, this paper proposes a drone radio frequency signal identification method based on YOLOv8n-PEM target detection model. Firstly, the original drone RF signal is downsampled based on discrete wavelet transform, and then the time-frequency characteristics are extracted by short-time Fourier transform. Finally, the signal identification and parameter estimation are completed by using YOLOv8n-PEM model. In terms of the model, the CPF module is designed based on partial convolution to enhance the extraction ability of advanced time-frequency features and improve the robustness of the model. At the same time, the EMA mechanism is introduced to suppress the interference of background noise on the model reasoning. The experimental results show that the YOLOv8n-PEM model has an mAP of 96.08% and an FPS of 107 frames per second under low SNR conditions of -20 to -10 dB, the model parameters are reduced by 38% compared with the baseline model, indicating its value for practical deployment..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 96 (2025)
Design of Controllable Focusing Intensity Ratio Bifocal Metalens Based on Spatial Multiplexing
MU Yonghao, ZHANG Haijun, LONG You, PENG Fei, and ZHOU Jiawu
Bifocal lens is an important optical component. In response to its miniaturization and lightweight requirements, combined with the fine control capabilities of metalens for light fields, a proportion of focusing intensity controllable bifocal lens based on spatial multiplexing is proposed. Firstly, as a sub-metalens ofBifocal lens is an important optical component. In response to its miniaturization and lightweight requirements, combined with the fine control capabilities of metalens for light fields, a proportion of focusing intensity controllable bifocal lens based on spatial multiplexing is proposed. Firstly, as a sub-metalens of bifocal metalens, the design principle of off-axis metalens is analyzed. Then, two types of metalen coupling methods, the splicing method and the spatial multiplexing method, are compared and analyzed. The results show that the spatial multiplexing method can better restore the performance of the sub-metalens. Finally, based on the idea that control focusing intensity by adjusting the number of microstructures, bifocal metalens design with intensity ratios of 1∶2 and 3∶2 is realized, which verifies the effectiveness of focusing intensity control..
Electronics Optics & Control
- Publication Date: Apr. 11, 2025
- Vol. 32, Issue 4, 104 (2025)
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