Journals >Electronics Optics & Control
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2025
Volume: 32 Issue 3
17 Article(s)
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An IRS-Aided Single Base Station Positioning Algorithm for Millimeter Wave System
XIA Yuping, and DENG Ping
To address the issue of drastically degraded positioning performance in non-line-of-sight environments due to occluded line-of-sight paths，a three-dimensional positioning and orientation estimation algorithm for millimeter wave single Base Station (BS) system with aid of a single Intelligent Reflecting Surface (IRS) isTo address the issue of drastically degraded positioning performance in non-line-of-sight environments due to occluded line-of-sight paths，a three-dimensional positioning and orientation estimation algorithm for millimeter wave single Base Station (BS) system with aid of a single Intelligent Reflecting Surface (IRS) is proposed. Firstly，through leveraging the sparse characteristics of millimeter wave signals and incorporating tensor theory，the problem of estimating channel parameters related to the location information is transformed into a tensor decomposition problem for resolution. Secondly，the estimated positioning-related parameters are used in a hybrid algorithm involving Time of Arrival (TOA)，Angle of Arrival (AOA)，and Angle of Departure (AOD) to estimate the coordinates and orientation of the Mobile Station (MS). To assess the performance of the proposed positioning algorithm，theoretical bounds for the estimation errors of MS location and orientation are derived. Simulation results demonstrate that the proposed algorithm closely approximates the theoretical error bounds，confirming the effectiveness of the proposed algorithm..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 1 (2025)
Performance Analysis of Collaborative Tracking Based on Bernoulli Filter and K-Rank Fusion
WU Junqing, WANG Fei, ZHOU Jianjiang, and HAN Qinghua
In addressing the demand for efficiency analysis in multi-radar collaborative target tracking，the mainstream fusion algorithms based on density estimation are modeled and their limitations are analyzed based on the entropy error theory of Gaussian-Uniform mixture distribution. A multi-radar collaborative tracking methoIn addressing the demand for efficiency analysis in multi-radar collaborative target tracking，the mainstream fusion algorithms based on density estimation are modeled and their limitations are analyzed based on the entropy error theory of Gaussian-Uniform mixture distribution. A multi-radar collaborative tracking method based on Bernoulli filtering of sequential Monte Carlo approximation and K-rank fusion is designed. Additionally，an error decreasing function is adopted as an indicator to quantitatively analyze the collaboration effectiveness of multi-radar target tracking. The simulation experiments show that:1) When the process noise of multi-radar target tracking is either too high or too low，there is negligible improvement in fusion tracking performance，indicating minimal efficiency gains from increasing radar quantity under such conditions; and 2) When the process noise is appropriate，there is significant improvement in fusion tracking performance，and the improvement showing a positive decreasing relationship with the increase of number of radars. The study provides a guidance for determining the number of radar nodes..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 7 (2025)
A Multi-target Tracking Algorithm Based on Graph Convolution and Clustering for Infrared UAV Swarm
LI Qi, XI Jianxiang, YANG Xiaogang, LU Ruitao, and XIE Xueli
In the scenario of multi-target tracking of infrared UAV swarm，there are challenges such as limited appearance features and severe target homogeneity，mutual occlusion of individuals in the swarm and platform jittering. To address the problems，this paper proposes a fusion tracking algorithm based on Graph Convolutional In the scenario of multi-target tracking of infrared UAV swarm，there are challenges such as limited appearance features and severe target homogeneity，mutual occlusion of individuals in the swarm and platform jittering. To address the problems，this paper proposes a fusion tracking algorithm based on Graph Convolutional neural Network (GCN) and clustering algorithms. Firstly，a self-attention feature mask is introduced to enhance GCN’s trajectory aggregation. Secondly，IoU and likelihood-based C-means clustering are adopted to improve motion feature extraction and adjacent target differentiation in the swarm. Finally，optimization of the tracking results is further achieved through a trajectory connection model and a Gaussian smoothing interpolation algorithm. The proposed algorithm integrates short-time trajectory aggregation and long-time trajectory matching，and achieves infrared UAV swarm multi-target tracking by only using motion information and interaction information. The experiments are conducted on infrared UAV swarm multi-target tracking dataset. The experimental results demonstrate its superior performance compared with that of other advanced tracking algorithms. MOTA and IDF1 of the proposed algorithm reach 84.9% and 80.2% respectively，and it has excellent tracking effects even in complex scenarios such as mutual occlusion of targets and platform jittering..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 15 (2025)
Path Planning Based on Improved RRT-Connect Algorithm
GE Chao, ZHANG Xinyuan, WANG Hong, and LUN Zhixin
Aiming at the path planning problem of military mobile robots，an improved algorithm based on RRT-Connect is proposed，which avoids the problems of low efficiency，large randomness，long search time，excessive number of iterations and nodes，and lengthy planned path in the algorithm planning process. Firstly，the algorithm taAiming at the path planning problem of military mobile robots，an improved algorithm based on RRT-Connect is proposed，which avoids the problems of low efficiency，large randomness，long search time，excessive number of iterations and nodes，and lengthy planned path in the algorithm planning process. Firstly，the algorithm takes the midpoint of the line between the starting point and the target point as the extension point，so that the algorithm changes from two-tree expansion to four-tree simultaneous expansion，and a range limiting function is introduced at the same time to optimize the path expansion and sampling process. Secondly，the dynamic step-size adjustment function is introduced into the improved algorithm to make the expansion process more target-oriented. Finally，the generated path is post-optimized to remove redundant nodes and shorten the path length. Simulation experiments are conducted for a comparison between the improved RRT-Connect algorithm，RRT algorithm and RRT-Connect algorithm in three different environments. Compared with the RRT-Connect algorithm，the improved algorithm has the number of overage iterations reduced by 83.8%，the number of average nodes reduced 59.3%，the average planning speed increased by 26.9%，and the average path length reduced by 12%..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 21 (2025)
An Aircraft Target Tracking Method Based on Kernelized Correlation Filtering
DU Xin, SHA Jianjun, ZHANG Xiang, SUN Dianxing, and TAN Cong
When the scale and viewing angle of flying targets such as aircraft change，Kernelized Correlation Filtering (KCF) algorithm may cause target tracking loss due to fixed tracking boundary and low filtering accuracy. To solve this problem，based on the KCF algorithm，a model updating strategy is added to improve the accuracWhen the scale and viewing angle of flying targets such as aircraft change，Kernelized Correlation Filtering (KCF) algorithm may cause target tracking loss due to fixed tracking boundary and low filtering accuracy. To solve this problem，based on the KCF algorithm，a model updating strategy is added to improve the accuracy of the model，and the YOLOv5l detection network is used to achieve accurate estimation of the target scale. Finally，the experimental results on the constructed aircraft target dataset show that the improved KCF algorithm has improved the accuracy and success rate by 0.315 and 0.285 respectively in comparison with the original algorithm，and it has good tracking performance when the target scale and viewing angle change..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 27 (2025)
Distributed Attitude Cooperative Control of Quadrotor UAV Formation Flight
WANG Yuxiang, CHEN Longsheng, XU Haitao, JIN Feiyu, and SHI Tongxin
The attitude tracking cooperative control problem under directed topology is addressed for a class of Quadrotor Unmanned Aerial Vehicle Formation System (QUAVFS) in the presence of dynamic disturbances and input saturation. Firstly，the attitude dynamic model of QUAVFS is established，and an auxiliary anti-saturation sysThe attitude tracking cooperative control problem under directed topology is addressed for a class of Quadrotor Unmanned Aerial Vehicle Formation System (QUAVFS) in the presence of dynamic disturbances and input saturation. Firstly，the attitude dynamic model of QUAVFS is established，and an auxiliary anti-saturation system is constructed to solve the input saturation problem of UAV actuators. Secondly，a finite-time disturbance observer based on the high-order sliding-mode differentiator technique is designed to compensate for time-varying dynamic disturbances. Finally，an attitude tracking cooperative controller for QUAVFS is designed by utilizing the backstepping method and the multi-agent consensus theory，and the bounded stability of the closed-loop system is demonstrated via Lyapunov stability theory. The simulation results illustrate the feasibility and effectiveness of the proposed scheme..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 33 (2025)
Switching Sliding Mode Control of Variable Swept-Wing NSV Under Discontinuous Disturbance and Actuator Composite Nonlinearity
MENG Lisha, CHEN Xiaoming, LIU Jiaji, and SHEN Danqing
To solve the problems of discontinuous disturbance，uncertainties，actuator dead zone and composite saturated nonlinearity of the variable swept-wing Near Space Vehicle (NSV)，an adaptive multi-model switching sliding mode attitude controller based on sliding mode disturbance observer and an auxiliary system is proposed. To solve the problems of discontinuous disturbance，uncertainties，actuator dead zone and composite saturated nonlinearity of the variable swept-wing Near Space Vehicle (NSV)，an adaptive multi-model switching sliding mode attitude controller based on sliding mode disturbance observer and an auxiliary system is proposed. Firstly，a nonlinear multi-model switching system under disturbance and actuator composite nonlinearity is established. Then，as for the uncertainties and the discontinuous disturbance，a mode-synchronized switching sliding mode disturbance observer is designed to estimate composite disturbance. As for the dead zone and composite saturated nonlinearity of the actuator，the dead zone right inversion function is introduced to equivalently transform the composite nonlinearity into a new saturation stage，and an auxiliary anti-saturation system is constructed to deal with it. Next，an adaptive multi-model switching sliding mode controller for attitude tracking is designed based on backstepping. Different from conventional sliding mode control，different sliding surfaces are designed for each subsystem of the switching system，and common coordinate transformation is realized through a transitional sliding surface. The Lyapunov theory and the average dwelling time theory are used to prove the stability of the closed-loop switching system. Finally，the effectiveness and superiority of the proposed method are verified by comparing it with the conventional sliding mode control..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 42 (2025)
An Object Tracking Algorithm Based on SAM and ATOM
HU Yinji, LIANG Zhenqi, LI Guoqiang, and JING Yuanping
Current object tracking algorithms often adopt bounding box or segmentation mask to initialize the template. However，it is difficult to obtain them under limited operation time or harsh scenarios. An object tracking algorithm with point prompt is proposed，which only needs the coordinates of any image in the object segmCurrent object tracking algorithms often adopt bounding box or segmentation mask to initialize the template. However，it is difficult to obtain them under limited operation time or harsh scenarios. An object tracking algorithm with point prompt is proposed，which only needs the coordinates of any image in the object segmentation mask to complete the template initialization. Firstly，SAM is adopted to perform zero-shot segmentation of the image object to obtain the segmentation mask，and the bounding rectangle is obtained and taken as the input of the ATOM tracking algorithm to complete the template initialization. The Gauss-Newton conjugate gradient method is adopted to quickly learn the template online to obtain the object locator，and the offline learning IoU prediction branch is applied to jointly complete the object tracking task. Finally，the simulation of the object tracking algorithm is completed according to the point prompt input. The simulation results show that: 1) The precision of the proposed algorithm reaches 79.7% and the AUC reaches 60.6% on the UAV123 dataset; 2) The tracking performance is better than that of the classic algorithms; and 3) The FPS reaches 87.4 frames per second，which meets the requirements of real-time tracking..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 51 (2025)
A Lightweight Target Detection Algorithm for Aerial Images
HE Qitian, LI Weixiang, CHENG Ming, SUN Yuan, and CHEN Chuang
In order to solve the problems of complex background，small target objects and difficult model deployment in UAV aerial images，a lightweight target detection algorithm for aerial images is proposed. The lightweight FasterNet module is introduced in the backbone network of YOLOv5m to replace C3 module，and the model paramIn order to solve the problems of complex background，small target objects and difficult model deployment in UAV aerial images，a lightweight target detection algorithm for aerial images is proposed. The lightweight FasterNet module is introduced in the backbone network of YOLOv5m to replace C3 module，and the model parameters is compressed to improve reasoning speed of the model. In the feature fusion network，the improved CBAM_L mechanism is used to focus on capturing small target information in aerial images while improving the target recognition accuracy of the model. The detection head in the detection network is replaced by a decoupled head，which solves the conflict between classification and regression when outputting variables in aerial images; and the loss function in the network is replaced by EIoU，which effectively improves the model regression accuracy. The verification results on the public dataset VisDrone show that the average accuracy mAP@0.5 of the improved model is increased by 0.014，the parameter quantity and the computation cost is respectively reduced to 34.3% and 32.4% of the original model，and the detection speed reaches 77 frames per second. The results show that the proposed algorithm exhibits good performance in both detection accuracy and speed..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 56 (2025)
A Multi-scale Structure Image Deblurring Method Integrating Transformer
GUO Yecai, YANG Gang, and MAO Xiangnan
In order to address the limitations in learning global feature information and the restricted receptive field of the existing image deblurring models，an improved multi-scale image deblurring network that integrates Transformer is proposed. Firstly，a multi-feature multi-scale fusion module is designed to enhance the modIn order to address the limitations in learning global feature information and the restricted receptive field of the existing image deblurring models，an improved multi-scale image deblurring network that integrates Transformer is proposed. Firstly，a multi-feature multi-scale fusion module is designed to enhance the model’s ability to learn global features and capture distant pixels. This module effectively combines local and global feature information by using a dual bypass structure while simplifying the Transformer to improve computational efficiency. Secondly，in order to alleviate the drawback of convolution operations lacking in input content adaptability，channel attention is introduced into the feature fusion module to dynamically learn useful information. On the benchmark dataset GoPro，the peak signal-to-noise ratio is 31.87 dB and the structural similarity is 0.952. The experimental results demonstrate that compared with mainstream methods，the proposed approach effectively restores image detail features and enhances the robustness of subsequent computer vision tasks..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 62 (2025)
Rotating Target Detection in Remote Sensing Images Based on Context Space Perception
LEI Bangjun, and ZHU Han
The rotating target detection task in remote sensing image processing has the characteristics of wide-range scale variations，complex backgrounds and arbitrary target directions，which pose challenges to automatic target detection. In order to solve the above problems，this paper proposes a rotating target detection frameThe rotating target detection task in remote sensing image processing has the characteristics of wide-range scale variations，complex backgrounds and arbitrary target directions，which pose challenges to automatic target detection. In order to solve the above problems，this paper proposes a rotating target detection framework based on context space perception by using YOLOv5s detector. Firstly，a Context Space Perception Module (CSPM) is designed to construct a backbone network to obtain more comprehensive local context information and global space perception information，so as to solve the problem that the network model has insufficient feature extraction capability for multi-scale targets. Secondly，the non-parametric attention mechanism of SimAM is introduced into the feature fusion section，and the important information is adaptively fused based on the principle of neuron suppression to solve the problem of false detection and missed detection of the model in complex backgrounds. Finally，the angle parameter is added to perform direction regression of the rotating target，which solves the problem of target regression in any directions. Meanwhile，Gaussian Wasserstein Distance Loss (GWDL) is used to calculate the loss of the rotating frame. The parameters are jointly optimized to improve the detection accuracy. The Recall，Precision and mAP50 of the proposed target detection algorithm on HRSC2016 dataset reach 0.955，0.916 and 0.904 respectively，which has the best detection effects. The algorithm also has a fine real-time performance with detection speed of 140.8 frames per second..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 69 (2025)
A Target Recognition Method of SAR Image Based on Regional Features
YANG Huiping, LAI Xiaolong, and LIU Dan
This paper proposes a method based on target and shadow regions for Synthetic Aperture Radar (SAR) target recognition under complex conditions. Zernike moment features are extracted from the target and shadow regions segmented in SAR images to describe geometric shape distributions of targets. Both the target and shadoThis paper proposes a method based on target and shadow regions for Synthetic Aperture Radar (SAR) target recognition under complex conditions. Zernike moment features are extracted from the target and shadow regions segmented in SAR images to describe geometric shape distributions of targets. Both the target and shadow regions can be used to analyze the shape of the target，which have good correlation. Therefore，joint sparse representation is used to comprehensively characterize the two Zernike moment feature vectors. Based on outputs from joint sparse representation，the reconstruction errors for the target and shadow regions achieved by different training classes are calculated and the target category is determined based on the principle of the minimum error. The integration of target and shadow regions can more comprehensively reflect the geometric shape information of the target in SAR images，which is helpful for discriminating different categories. Based on MSTAR dataset samples，one standard operating condition and three extended operating conditions including configuration difference，pitch angle difference and noise interference are set up for experimental analysis and comparison validation. The results show the performance superiority of the proposed method..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 76 (2025)
A Relative Navigation Algorithm ASUKF for Suppressing Radar Angular Glint
SHI Changyong, ZHANG Jingtian, GONG Cheng, and ZHANG Yuke
In view of the problem of nonlinear system filter under non-Gaussian noise caused by radar angular glint，conventional filter will be greatly affected and performance degradation or even divergence may occur. To solve the problem，a relative navigation algorithm of ASUKF filter is proposed to suppress radar angular glintIn view of the problem of nonlinear system filter under non-Gaussian noise caused by radar angular glint，conventional filter will be greatly affected and performance degradation or even divergence may occur. To solve the problem，a relative navigation algorithm of ASUKF filter is proposed to suppress radar angular glint and estimate the relative position and relative speed between the tracking spacecraft and the target spacecraft. ASUKF filter can use Sage-Husa method to adjust the strategy adaptively，quickly estimate the statistical characteristics of measurement noise，and use UKF to achieve accurate estimation of nonlinear system. It has certain robustness to the contaminated Gaussian distribution noise. Finally，the effectiveness of the relative navigation method is verified by simulation: 1) In the case of angular glint noise，the statistical mean value of relative position accuracy is better than 5.77 m，and the relative speed respectively is better than 0.22 m/s; and 2) It has better anti-interference ability than EKF and UKF，and can improve the robustness and stability of relative navigation system..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 82 (2025)
An Image Deraining Algorithm Based on Dual Attention Dense Residual Contraction Network
WANG Zhen, and NIU Xiaowei
To solve the problem that existing algorithms cannot remove the rain pattern thoroughly and may cause background information loss，an image deraining algorithm based on Dual Attention Dense residual Contraction (DADC) network is proposed. In the network，various scale information is collected through a mixed feature compTo solve the problem that existing algorithms cannot remove the rain pattern thoroughly and may cause background information loss，an image deraining algorithm based on Dual Attention Dense residual Contraction (DADC) network is proposed. In the network，various scale information is collected through a mixed feature compensation module. At the encoding stage，the DADC module is taken as the basic encoding module of the encoder，and as for the collected feature information，the useless information is zeroed out by using the soft threshold network，and dual-attention of spatial and channel is added to annotate the location information of the rain pattern. At the decoding stage，the feature information at different stages is aggregated，the spatial and channel excitation is conducted through the scSE attention mechanism，and the feature information is compressed and then passed into the decoder for decoding and outputting the rain removal image. The experiments are conducted on the publicly-available datasets of Rain100H，Rain100L，Rain800 and Rain12，and the Peak Signal-to-Noise Ratio (PSNR) is improved by 1.07～7.45 dB，and the structural similarity is improved by 0.021～0.139 on Rain100H compared with those of other algorithms..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 88 (2025)
Visibility Estimation Based on Simulated Images of Foggy Weather
QIU Shizhuo, YE Qing, HUANG Jiaheng, and LIU Jianping
Aiming at the shortage of fog image data set with visibility labels，a visibility detection method based on simulation fog images is proposed. The depth map of clear outdoor images is constructed by unsupervised depth estimation model，and the details of the depth map are refined by using feature fusion. The transmissionAiming at the shortage of fog image data set with visibility labels，a visibility detection method based on simulation fog images is proposed. The depth map of clear outdoor images is constructed by unsupervised depth estimation model，and the details of the depth map are refined by using feature fusion. The transmission map of outdoor images under set visibility is obtained by using dark channel method to estimate atmospheric light value，and the simulation fog image dataset with different visibility labels is further obtained. Based on this，the improved ShuffleNet V2 network is adopted to train the visibility estimation model. A verification experiment is conducted on the visibility grade estimation of the dataset and the real foggy images. The experimental results show that: 1) The proposed method has good visibility estimation results for foggy images with visibility less than 500 meters;2) The detection accuracy is higher than 90% for foggy images with visibility less than 200 meters; and 3) The overall accuracy is 87.8%; which indicating that the method is feasible and can be applied to estimate the visibility level under fog conditions..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 94 (2025)
Aircraft Detection in SAR Images Based on Improved YOLOv8
QIU Linlin, ZHU Weigang, LI Yonggang, QIU Lei, and LI Xuanchao
The aircraft detection in Synthetic Aperture Radar (SAR) images encounters several challenges including complex backgrounds，dimand small-scaleaircraft targets，big differences in targets under different imaging conditions，and fragmented target structures. To solve the problems，a novel aircraft target detection algorithmThe aircraft detection in Synthetic Aperture Radar (SAR) images encounters several challenges including complex backgrounds，dimand small-scaleaircraft targets，big differences in targets under different imaging conditions，and fragmented target structures. To solve the problems，a novel aircraft target detection algorithm named Aircraft Target Detection Model(ATDM) for SAR images is proposed to improve the detection accuracy of aircraft targets in SAR images in complex backgrounds. Taking YOLOv8s as the baseline model，the algorithm includes three key modules，namely，the Convolutional Block Attention Module (CBAM)，Omni-Dimensional Feature Extraction (ODFE) module，and Deformable Global Feature Fusion (DGFF) module，along with an improved loss function. In order to improve the feature extraction ability of the network in complex backgrounds，the CBAM is integrated into the backbone of the baseline network to capture aircraft target features across spatial and channel dimensions. The ODFE utilizes the dynamics of four dimensions of convolution kernel space，namely，the size of the kernel，the number of input channels，the number of output channels and the number of convolution kernels，to extract features from different types of aircraft targets across the four dimensions by using the parallel operation strategy，thereby enhancing the detection of aircraft targets，especially small targets with weak scattering characteristics in complex backgrounds. The DGFF dynamically adjusts the shapes and sizes of convolution kernels to accommodate variations in the imaging conditions，thereby facilitating global information feature fusion. Finally，the bounding box regression loss function is improved to be a dynamic non-monotonic focusing loss function WIoU. The dynamic non-monotonic focusing mechanism is adopted，and the outlier degree is used to evaluate the quality of the anchor frame to mitigate mislabeling effects in SAR images. In order to assess the performance of the proposed ATDM，the experiments are conducted on SADD and Gaofen-3 SAR aircraft datasets. The Average Precision (AP) achieved on the two datasets reaches 95.4% and 98.2% respectively. Ablation experiments and comprehensive analysis indicate the efficacy of the proposed three modules and loss function. Furthermore，compared with other target detection algorithms，the proposed algorithm achieves the highest AP..
Electronics Optics & Control
- Publication Date: Mar. 21, 2025
- Vol. 32, Issue 3, 101 (2025)
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