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Fast Subspace and DOA Estimation Method for the Case of High-Dimensional and small Samples

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 3958-3975页

作者： Zhang, Xuejun Feng, Dazheng Zheng, Weixing Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW 2751 Australia

It is well-known that classical direction of arrival (DOA) estimation methods work well in the case of large samples. However, these methods may be theoretically invalid in the case of small samples, which frequently occur in large array systems. Such a large array has two effects: i) The number of samples is relatively quite small, and ii) the dimension of samples is very large. To handle the above problems, a more appropriate method for solving DOA estimators in the case of high-dimensional and small samples is proposed in this paper. First, considering the special structure of received samples, an alternative well-estimated second-order statistic, known as the Gram matrix, is originally constructed to better utilize the spatial and statistical information of signals and noise contained by small samples. Second, two novel methods for estimating the number of targets are derived by combining the Gram matrix and information-theoretic criteria. Third, a novel object function and the corresponding algorithm based on the Gram matrix are designed to estimate the signal subspace more efficiently, and then the DOAs of targets are obtained by multiple signal classification methods. In particular, the theoretical analysis indicates that the improved signal subspace estimation algorithm only needs to decompose the low-dimensional Gram matrix instead of the high-dimensional sample covariance matrix. Finally, simulation results are provided to demonstrate the high accuracy and lower computational complexity of the proposed methods in the case of high-dimensional and small samples.

关键词： Direction-of-arrival estimation Estimation Prediction algorithms Noise Covariance matrices Classification algorithms signal processing algorithms Multiple signal classification Accuracy Vectors High-dimensional and small samples gram matrix direction of arrival number of targets estimation method subspace estimation algorithm

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Joint Iterative Adaptive Approach for Sidelobe Suppression and Migration Correction of Migrating targets

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 2973-2995页

作者： Tian, Jing Zhang, Biao Cui, Wei Wu, Siliang Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

Detection of multiple closely spaced targets with range-Doppler (RD) migration is a challenging issue for radars, because range cell migration (RCM) and Doppler frequency migration (DFM) during the coherent processing interval (CPI), as well as high sidelobes of strong targets, may deteriorate the performance of target detection and parameter estimation. To realize migration correction and sidelobe suppression simultaneously, a joint iterative adaptive approach (IAA) based on RD processing outputs (RD-JIAA) is first proposed in this article. The input data of RD-JIAA are selected within a small processing window centered around the response peak trajectory in range-velocity domain obtained by the RD processing. Compared with IAA and wideband IAA (WIAA), RD-JIAA has low computational burden. Some instructive suggestions on the selection of processing window sizes are presented considering that most of the target energy should be included in the processing window. Then, a fast implementation, namely, RD-JIAA based on the signal sparsity (RD-SJIAA), is presented to further improve the computational efficiency with tolerable performance loss. Both RD-JIAA and RD-SJIAA are able to utilize the structure relationships between covariance matrices of adjacent range cells to reduce the computational complexity. Finally, the performance of the proposed methods is evaluated by numerical examples.

关键词： Iterative methods Doppler effect Wideband Radar signal to noise ratio Covariance matrices Trajectory Computational complexity Object detection Estimation

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ARBFPN-YOLOv8: auxiliary reversible bidirectional feature pyramid network for UAV small target detection

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signal IMAGE AND VIDEO processing 2025年第1期19卷 1-13页

作者： Luo, Feng Bian, Weixin Jie, Biao Dong, Haotong Fu, Xinglin Anhui Normal Univ Sch Comp & Informat Wuhu 241002 Peoples R China Anhui Prov Key Lab Ind Intelligence Data Secur Wuhu 241002 Peoples R China

Drones are widely used in fields such as agriculture, environmental protection, and public safety. In these applications, the ability to detect small targets typically directly determines the effectiveness of drone image analysis. Due to the small number of pixels in the image, feature extraction is very difficult for small targets. Traditional algorithms find it difficult to capture the details of small targets. Although multi-scale feature fusion technology can improve detection capability, feature loss and interference still occur after multiple samplings. To effectively address this challenge, an innovative architecture called Auxiliary Reversible Bidirectional Feature Pyramid Network (ARBFPN) has been proposed. The core design concept is to enhance the integrity of feature information by introducing auxiliary structures, and to prevent feature loss during transmission by using residual connections, thereby preserving more detailed information, which is crucial for small object detection in the feature extraction stage. Meanwhile, by optimizing the detection head through detail enhancement mechanism and gating mechanism, a Lightweight Detail Enhanced Gated Head (LDEGH) was innovatively proposed to improve the overall detection accuracy. To verify the effectiveness of the proposed architecture, relevant experiments were conducted on the VisDrone2019 dataset. The experimental results show that compared with existing technologies, its performance is significantly better than the state-of-the-art technology (SOTA), bringing new breakthroughs to the field of small object detection in drone images.

关键词： YOLOv8 Feature enhancement small object detection Auxiliary reversible branch

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Learning Source-Free Domain Adaptation for Infrared small Target Detection

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IEEE signal processing LETTERS 2025年 32卷 1121-1125页

作者： Jin, Hongxu Chen, Baiyang Lu, Qianwen Tao, Qingchuan Li, Yongxiang Sichuan Univ Coll Elect Informat Chengdu 610065 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Univ Tokyo Grad Sch Interdisciplinary Informat Studies Tokyo 1638001 Japan

Existing infrared small target detection (IRSTD) methods mainly rely on the assumption that the training and testing data come from the same distribution, a premise that does not hold in many real-world scenarios. Additionally, the inability to access source domain data in numerous IRSTD tasks further complicates the domain adaptation process. To address these challenges, we propose a novel Source-Free Domain Adaptation (SFDA) framework for IRSTD, denoted as IRSTD-SFDA. This framework comprises two key components: Multi-expert Domain Adaptation (MDA) and Multi-scale Focused Learning (MFL). MDA leverages the source model to generate pseudo masks for the target domain, facilitating the transfer of knowledge from the source to the target domain. To account for the inherent diversity of small targets across domains, MDA refines these pseudo masks through a series of operations, including target localization, rolling guidance filtering, shape adaptation, and multi-expert decision, thereby mitigating morphological discrepancies between the source and target domains. Meanwhile, MFL employs a global-local fusion strategy to focus on critical regions, enhancing the model's ability to detect small infrared targets. Extensive experimental evaluations across various cross-domain scenarios demonstrate the effectiveness of the proposed framework.

关键词： Adaptation models Training Filtering Testing Shape Object detection Location awareness data models Clutter Image edge detection Infrared small target detection source-free domain adaptation multi-expert domain adaptation

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Real-time and high-precision SVM-RF based prediction model for DOA estimation

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DIGITAL signal processing 2025年 161卷

作者： Xiao, Liyuan Zheng, Guimei Song, Yuwei Zheng, He Air Force Engn Univ Grad Sch Xian 710038 Peoples R China Air Force Engn Univ Air Def & Missile Def Coll Xian 710038 Peoples R China

DOA estimation is one of the core tasks in radar signal processing and of great significance in communication, military, and other daily activities. The traditional DOA estimation algorithms has a high time cost for angle estimation, while most available data-driven estimation methods cannot achieve high-precision estimation since converting DOA estimation into multi-label classification task. Based on this, this paper has proposed a dual layer DOA estimation model based on Support Vector Machine(SVM) classification and Random Forest (RF) regression (SVM-RF) for exactly and rapidly estimating the angle of signal sources received from the Radar antenna array system, which transforms the DOA estimation problem into regression task. This method extracts triangular data on the signal covariance matrix as feature input, first divides the dataset into several small sample sets using SVM, and then it conducts RF regression training separately. Six experiments were conducted to verify the validity, reliability and robustness in different SNRs and snapshots for single and multi-targets. Simulation results indicate that the SVM-RF proposed in this paper has implemented the super-resolution DOA Estimation with the estimation accuracy comparable to the classical algorithm MUSIC and CNN. Moreover, SVM-RF has low computational cost and can perform real-time DOA estimation.

关键词： Direction of arrival estimation Array signal processing Machine learning Random forest Support vector machine

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IR-MPE: A Long-Term Optical Flow-Based Motion Pattern Extractor for Infrared small Dim targets

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Liu, Xuyang Zhu, Wenming Yan, Pei Tan, Yihua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Multispectral Informat Intelligent Pr Wuhan 430074 Peoples R China

In complex scenarios, the utilization of temporal motion information can improve the detection performance of infrared small and dim targets. However, existing multiframe methods only consider short-term motion information at each moment, which is difficult to capture reliable motion information for small and dim targets. In addition, existing multiframe data-driven methods generally utilize complex network structures which have longer inference times compared with the single-frame models of infrared target detection. Such a problem limits the applicability of the existing multiframe methods. In this article, we propose a long-term optical flow (OF)-based infrared small target motion pattern extractor (IR-MPE) to generate long-term OF energy (OFE) maps, which reflect the motion patterns of targets at the current moment. First, we design a long-term OF adaptive accumulation module (OFAAM) to adaptively control the update of current motion information and the retention of previous motion information. Second, we design an offset correction module (OCM), which is embedded in the OFAAM module to rectify the OFE from the previous frame. Meanwhile, the OCM also corrects the output of the previous frame to assist in the detection of the current frame. Embedding our IR-MPE module into the existing single-frame methods can easily extend them as multiframe methods. The only modification is adding an extra input channel of the first layer whose input is set as the concatenation of our OFE and the original infrared image. Such a simple structure can significantly improve the detection accuracy while maintaining fast inference speed. Extensive experiments on various public datasets show that our approach outperforms the state-of-the-art methods in challenging scenarios.

关键词： data mining Optical flow Object detection Feature extraction Deep learning Fuses Detectors Dams Accuracy signal to noise ratio Infrared small dim target detection long-term optical flow (OF) motion pattern extractor

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Machine learning classifier for marine radar small targets based on normalization features 16

Machine learning classifier for marine radar small targets b...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Zhao, Zi-Jian Shui, Peng-Lang National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510689251

targets on the sea surface are of great concern, especially the classification of small targets, which is crucial for marine safety. Therefore, we propose a machine learning classifier for marine radar small targets based on normalization features, which can effectively classify targets with small radar cross section (RCS) and signal-to-clutter ratio (SCR) on the sea surface. We standardize the radar parameters because different radars have different system parameters, resulting in limited generalization ability of the trained classifier. We extract 9 features from multiple dimensions of the signal such as time domain, frequency domain and time-frequency domain and then classify targets using decision tree (DT) classifier, K-nearest neighbors (KNN) classifier, bootstrap aggregation (Bagging) classifier and adaptive boosting (AdaBoost) classifier. Experimental validation is performed on publicly available IPIX, self-measured radar data and data provided by the South African Council for Scientific and Industrial Research (CSIR), which contains four types of targets: floating ball, floating boats, low-altitude unmanned aerial vehicles (UAVs) and moving speedboats, and the results of the experiments show that the proposed classifier achieves a correct classification accuracy of 0.8537. © 2025 SPIE.

关键词： Marine radar

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small object detection in UAV imagery based on channel-spatial fusion cross attention

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signal IMAGE AND VIDEO processing 2025年第4期19卷 1-14页

作者： Li, Jianlong Zheng, Chunhou Chen, Peng Zhang, Jun Wang, Bing Anhui Univ Inst Phys Sci & Informat Technol Natl Engn Res Ctr Agroecol Big Data Anal & Applica Sch InternetInformat Mat & Intelligent Sensing La Hefei 230601 Peoples R China Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu 233030 Peoples R China Anhui Rocvis Intelligent Technol Co Ltd Hefei 230001 Peoples R China

Object detection in unmanned aerial vehicle (UAV) images has become an important research area in computer vision due to its unique value and challenges. UAV images are characterized by densely distributed small targets, significant changes in target scale, and background noise, which affect the accuracy and reliability of detection. To address these issues, we propose an small target detection network based on Enhanced Scale Sequence Fusion and channel space fusion cross-attention mechanism, called *** tackle the high proportion of small targets and scale variation in UAV images, we employ Enhanced Scale Sequence Fusion, integrating fine-grained information from shallow feature maps and semantic information from deep feature maps. Additionally, we incorporate an tiny target detection head to enhance the network's ability to extract fine-grained information features for small targets. To address the issue of background noise, we propose a channel space fusion cross-attention mechanism, which first performs attention calculation on local patch block feature maps, and then performs attention calculation global patch blocks. This captures both long-range dependencies and detailed information. The method for calculating attention combines spatial description information and channel description *** experiments were conducted to validate the effectiveness of the model on the VisDrone benchmark dataset, UAVDT dataset and our self-made UAV power inspection dataset PIDrone. In comparison to the YOLOv8s model, the CSFCANet demonstrated an improvement in mAP of 7% on the PIDrone, 2.4% on the VisDrone, and 3.6% on the UAVDT.

关键词： small object detection Attention mechanism Multi-scale feature fusion UAV Aerial Imagery

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Cross-YOLO: an object detection algorithm for UAV based on improved YOLOv8 model

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signal IMAGE AND VIDEO processing 2025年第6期19卷 1-10页

作者： Dong, Ying Guo, Jiahao Xu, Fucheng Dalian Minzu Univ Coll Sci 31 Jinshi Rd Dalian 116000 Liaoning Peoples R China Dalian Minzu Univ Sch Preuniv 31 Jinshi Rd Dalian 116000 Liaoning Peoples R China

In this study, Cross-YOLO, an enhanced version of the YOLOv8 model, is specifically designed to address the challenge of detecting small objects in UAV target detection scenarios. The model refines the original YOLOv8 through several innovative improvements: Firstly, in order to improve the detection accuracy of small targets, we propose Cross-FPN to bolster the original FPN. Secondly, we have redesigned a lightweight detection head, DELDH, to solve the problem of network bloat caused by the introduction of small object detection heads. Thirdly, a new attention mechanism CMCA is designed, that unifies the Coordinate attention mechanism with the Multi-scale convolutional attention mechanism to further enhance the feature extraction of small targets. Finally, the WIoU loss function is introduced to improve the accuracy of bounding box regression and improve detection performance. Experimental data in the Visdrone dataset indicate that, under the condition of the selected model size n, Cross-YOLO achieves a substantial reduction of 35.4% in parameter count compared to YOLOv8n, with only a marginal increase of 7.8% in computational load, and a significant improvement of 5.3% in mAP0.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$mAP_{0.5}$$\end{document}. Furthermore, its strong performance on the DOTA v1.5 and TinyPerson datasets confirms the model's generalization capabilities and practical applicability.

关键词： YOLOv8 small object Object detection UAV Attention

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Enhanced YOLOv8 for small-object detection in multiscale UAV imagery: Innovations in detection accuracy and efficiency

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DIGITAL signal processing 2025年 158卷

作者： Luo, Weixin Yuan, Sannan Shanghai Univ Elect Power Sch Elect & Informat Engn Shanghai 201306 Peoples R China

Due to the ability of drone technology to cover wide areas and reach difficult-to-access places, the detection of small targets by Unmanned Aerial Vehicles (UAVs) is crucial in various applications such as urban management planning and emergency response. This paper proposes innovations to the YOLOv8 architecture, significantly enhancing its performance in small target detection from multi-scale drone perspectives. We introduce Channel Priority Attention Dynamic Snake Convolution and Dynamic small Object Detection Head Layer (DyHead-SODL) to improve the model's ability to capture fine details and detection accuracy. Additionally, we implemented an enhanced loss function (MPDIoU) and a Deformable Attention Transformer (DAT) to optimize detection efficiency without increasing computational burden. Experimental results on the Visdrone and RSOD datasets demonstrate significant improvements, with the proposed method increasing mAP50 by 10.5 %, mAP95 by 6.9 %, reducing localization error Eloc by 1.27, and decreasing the miss rate by 2.42 on the Visdrone dataset, outperforming existing state-of-the-art detection models while maintaining low computational complexity. The proposed method has also been tested on four large public datasets: DOTA, HRSC, LEVIR, and CARPK, demonstrating the generalization capability of our model. These advances provide effective solutions for small target detection from drone perspectives and promote the development of single-stage object detection technology. These advancements provide an effective solution for small object detection from UAVs, advancing the field of one-stage object detection technology. (source code: https://***/GuccIceCream/yolov8/tree/master)

关键词： YOLOv8 small object detection Channel prioritized attention dynamic snake convolution Dynamic small object detection head MPDIoU Deformable attention transformer

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