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Multiresolution Hierarchical Target Detection in High-Resolution Maritime Surveillance Radars

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第5期60卷 5937-5956页

作者： Zhang, Xiao-Jun Chang, Si-Yuan Xue, Yu-Fan Shui, Peng-Lang Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

High-resolution maritime surveillance radars need to detect small targets and moderate/large targets such as ships in short/medium-distance regions. At a single-range resolution, radar echoes of moderate/large targets severely affect the detection of small targets around them. In this article, a simple digital filtering method is given to realize range resolution conversion by which multiresolution radar echoes data are generated from high-range resolution data. As one of the main contributions, the compound-Gaussian model with inverse Gaussian textures (CGIG) is extended to characterize multiresolution sea clutter. Moreover, an across-resolution parameter estimation method is proposed to estimate the parameters of the multiresolution CGIG model based on across-resolution moment relationship. The other contribution is a hierarchical target detection scheme in the multiresolution CGIG clutter model. Simple nonadaptive noncoherent integration detectors are used at lower resolution channels to detect moderate/large targets and adaptive near-optimum coherent detectors in the CGIG-distributed sea clutter are adopted at higher resolution channels to detect small targets using the detections from the lower resolution channels as the prior information on environment. Finally, the multiresolution CGIG model and hierarchical target detection scheme are examined by an X-band island-based measured data using an unmanned aerial vehicle as a test target.

关键词： Clutter signal resolution Spatial resolution Radar cross-sections Filtering Radar detection Object detection

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

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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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Hybrid Deterministic Sensing Matrix for Compressed Drone SAR Imaging and Efficient Reconstruction of Subsurface targets

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REMOTE SENSING 2025年第4期17卷 595-595页

作者： Jo, Hwi-Jeong Lee, Heewoo Choi, Jihoon Lee, Wookyung Korea Aerosp Univ Sch Elect & Informat Engn Goyang 10540 Gyeonggi Do South Korea

Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and memory resources of small SAR platforms demand efficient recovery performance for high-resolution imaging. Compressed sensing (CS) algorithms are widely used to mitigate data storage requirements, yet they often suffer from challenges related to computational burden and detection errors. CS theory exploits signal sparsity and the incoherence of sensing matrices to reconstruct target information from reduced data measurements. Although random sensing matrices are commonly employed to ensure the independence of measured data, they incur high computational cost and memory resources. While deterministic sensing matrices provide fast data recovery, they suffer from increased internal interference, leading to degraded performance in noisy environments. This paper proposes a novel hybrid sensing matrix and recovery algorithm for efficient target detection in small drone-based SAR platforms. After establishing the principles of signal sampling and recovery, SAR imaging simulations are conducted to evaluate the performance of the proposed method with respect to data compression, processing speed, and recovery accuracy. For verification, a custom-built drone SAR platform is utilized to recover subsurface targets obscured by high-clutter backgrounds. Experimental results demonstrate the effective recovery of buried target images, highlighting the potential of the proposed method for practical applications in high-clutter environments.

关键词： synthetic aperture radar (SAR) subsurface target drone compressive sensing

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Subspace-based distributed target detection method with small training data samples

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IET RADAR SONAR AND NAVIGATION 2024年第12期18卷 2541-2551页

作者： Wei, Guangfen Zhou, Zhan Luo, Yuan Jian, Tao Tang, Xiaoming Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai Peoples R China Naval Aviat Univ Res Inst Informat Fus Yantai Peoples R China Yantai Sanhang Radar Serv & Technol Inst Yantai Peoples R China

Detecting distributed targets precisely in homogeneous environments has been a hot topic in radar signal processing. Generally, distributed targets are often modelled with subspace models of unknown coordinates, and clutter is modelled as the complex Gaussian distribution with zero mean and unknown covariance matrix, while covariance matrix is estimated with a set of training data without the target signal. However, in practice, the complexity of the external environment makes the training data that satisfy the condition of independent homogeneous distribution less available. Therefore, it is assumed that the covariance matrix of the clutter is persymmetric structure and the approach of dimensionality reduction using subspace transformations is introduced, two detectors based upon generalised likelihood ratio test criterion and Wald test criterion in homogeneous environments are proposed. Theoretical analyses indicate the constant false alarm rate characteristics of the two proposed detectors for unknown clutter covariance matrices. Simulation analyses indicate that the proposed detector works well even with fewer training data samples, and its detection performance outperforms that of existing contrast detectors.

关键词： adaptive signal detection array signal processing distributed sensors maximum likelihood estimation Monte Carlo methods parameter estimation phased array radar radar clutter space-time adaptive processing

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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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A Method for Detecting small Slow targets on Sea Surface Based on FC-MCNN

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IEEE ACCESS 2024年 12卷 15525-15534页

作者： Sun, Qing Zhao, Jing Xue, Chunling Yang, Xueting Baoji Univ Arts & Sci Sch Math & Informat Sci Baoji 721000 Peoples R China

Detecting small slow targets on sea surface has always been a hot topic in radar target detection. Classical target detection methods based on single input mode are particularly prone to losing the phase information of the radar echo signal, which affects the detection performance of the radar. To solve the problem of phase information loss, a False alarm Control method of Multi-input Convolutional Neural Network (FC-MCNN) is proposed in this article. First, several classic Convolutional Neural Network (CNN) models are compared and analyzed. Then, a Multi-input Convolutional Neural Network (MCNN) is designed to extract more useful information from radar echo signal. Simultaneously, in order to improve the generalization performance of the model and control the false alarm probability, a controllable false alarm Support Vector Machine (SVM) is used to modify the final output module. In addition, the datasets are also preprocessed before being input into the proposed model. Finally, the performance of the proposed method is verified by the IPIX measured data sets. The results show that the proposed method has a higher detection probability and better computational performance than the traditional CNN detection methods.

关键词： Target detection constant false alarm rate (CFAR) signal processing convolutional neural network (CNN) support vector machine (SVM)

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small target reconstruction method for array GM-APD LiDAR under a low SBR condition

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OPTICS EXPRESS 2024年第25期32卷 43929-43946页

作者： Ma, Le Sun, Jianfeng Lu, Wei Zhou, Xin Lu, Jie Liu, Feng Harbin Inst Technol Natl Key Lab Laser Spatial Informat Harbin 150001 Heilongjiang Peoples R China Harbin Inst Technol Zhengzhou Res Inst Zhengzhou 450000 Henan Peoples R China China Elect Technol Grp Corp Res Inst 44 Chongqing 400060 Peoples R China

Extracting targets under high noise conditions, particularly for small and weak targets at long distances, presents a significant challenge. In this context, a noise suppression method grounded in variable mode extraction (VME) is proposed. This approach separates signal and noise in the echo based on differences in the center frequency of the mode. By combining the residual signals from Geiger-mode avalanche photodiode (GM-APD) laser imaging, detection, and ranging (LiDAR) systems, the method enhances the signal-to-noise ratio (SNR) of the response distribution, enabling accurate target position extraction. The processing performance of this method was validated through simulation analysis and real experimental data. When the average signal-to-background light ratio (SBR) of the echo reaches 0.006, the probability of successfully extracting a single-pixel target signal at a distance of 2.5 km in a sky background can exceed 90%.

关键词： Avalanche photodiodes Detector arrays Imaging systems Laser imaging Lidar Single pixel imaging

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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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A Level Set Annotation Framework With Single-Point Supervision for Infrared small Target Detection

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IEEE signal processing LETTERS 2024年 31卷 451-455页

作者： Li, Haoqing Yang, Jinfu Xu, Yifei Wang, Runshi Beijing Univ Technol Fac Informat Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China

Infrared small Target Detection is a challenging task to separate small targets from infrared clutter background. Recently, deep learning paradigms have achieved promising results. However, these data-driven methods need plenty of manual annotations. Due to the small size of infrared targets, manual annotation consumes more resources and restricts the development of this field. This letter proposed a labor-efficient annotation framework with level set, which obtains a high-quality pseudo mask with only one cursory click. A variational level set formulation with an expectation difference energy functional is designed, in which the zero level contour is intrinsically maintained during the level set evolution. It solves the issue that zero level contour disappearing due to small target size and excessive regularization. Experiments on the NUAA-SIRST and IRSTD-1k datasets demonstrate that our approach achieves superior performance.

关键词： Deep learning infrared small target detection level set

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