Article
Geochemistry & Geophysics
Yunkai Xu, Minjie Wan, Xiaojie Zhang, Jian Wu, Yili Chen, Qian Chen, Guohua Gu
Summary: Realizing robust infrared small target detection in complex backgrounds is important for infrared search and tracking (IRST) applications. An infrared small target detection method using local contrast-weighted multidirectional derivative (LCWMD) is proposed to address the challenging task. The method considers target property, background information, and their relation, and achieves better performance than other detectors in terms of SCR gain (SCRG) and background suppression factor (BSF).
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Xueling Liang, Bo Chen, Wenchao Chen, Penghui Wang, Hongwei Liu
Summary: The clutter background in modern radar target detection is complex and changeable. Existing methods based on parametric statistical modeling and data-driven deep learning have limitations. In this paper, a novel unsupervised method called GM-CVAE is proposed to model complex and changing clutter using a one-dimensional Convolutional neural network. An unsupervised narrow-band radar target detection strategy based on reconstructed likelihood is also developed.
Article
Geochemistry & Geophysics
Ruitao Lu, Xiaogang Yang, Weipeng Li, Jiwei Fan, Dalei Li, Xin Jing
Summary: A novel small target detection method based on multidirectional derivatives is proposed, which can effectively separate targets from backgrounds. The method constructs a local contrast measure, integrates MDWCM maps from all derivative subbands to enhance detection robustness, and ultimately achieves adaptive segmentation and extraction of small targets.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Baiqiang Zhang, Jie Zhou, Junhao Xie, Wei Zhou
Summary: This paper proposes a novel CFAR detector for Weibull background with known shape parameter, based on a robust weighted likelihood estimator with robustness to interferences, and introduces invariant theory to prove its CFAR property. Computational analysis and simulation results confirm the effectiveness and superiority of the proposed CFAR detector.
DIGITAL SIGNAL PROCESSING
(2021)
Article
Chemistry, Multidisciplinary
Jin Tang, Jialiang Jiang, Ning Wang, Yaodong Wu, Yihao Wang, Junbo Li, Yona Soh, Yimin Xiong, Lingyao Kong, Shouguo Wang, Mingliang Tian, Haifeng Du
Summary: Real-space magnetic imaging and anisotropic magnetoresistance studies on dipolar skyrmions were conducted. The study found that the anisotropic magnetoresistance of skyrmions is proportional to the skyrmion count and independent of the helicity. This research promotes read-out operations in skyrmion-based spintronic devices.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Engineering, Aerospace
Yongsong Li, Zhengzhou Li, Yu Shen, Zhiwei Guo
Summary: A target detection method based on local image block analysis and center-surround gray difference measure (CGDM) is proposed in this article to address the challenges of detecting dim small targets under complex background clutters and noise. Experimental results show that the proposed method outperforms existing algorithms in small target detection and is robust to various target shapes, sizes, and noise types.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Jie Chen, Zhongjin Zhang
Summary: This paper presents anisotropic magnetoresistance (AMR) sensors with a flexible substrate. The sensors are fabricated on polyimide (PI) material using surface micromachining process, achieving a minimum linewidth of 3 μm through process optimization. An orthogonal-arranged Wheatstone bridge structure and series-parallel connection of AMR strips are proposed to improve voltage output and sensitivity. The AMR sensors demonstrate high linearity, sensitivity, and voltage output performance, with a maximum Wheatstone bridge voltage output of 0.07 mV achieved at 0.5 V bias in a 100 Gs magnetic field, and a sensitivity value of 1.5 Gs(-1). Furthermore, the AMR sensors exhibit good robustness upon mechanical bending, achieving a maximum bend radius of 2.3 cm. These research results demonstrate the feasibility of manufacturing high-performance small-sized AMR sensors on flexible substrates and reveal great potential for magnetic field detection in non-planar applications.
JOURNAL OF MATERIALS SCIENCE-MATERIALS IN ELECTRONICS
(2023)
Article
Astronomy & Astrophysics
Kazunori Nakayama, Wen Yin
Summary: The recent observation of anomalous flux in the cosmic optical background suggests the possibility of dark matter decaying into photons, although the measurements from the Hubble Space Telescope have contradicted this theory.
Article
Materials Science, Characterization & Testing
Xin Wu, Xialong Wei, Haojun Xu, Weifeng He, Chao Sun, Lin Zhang, Yiwen Li, Yang Fang
Summary: An improved rapid multiprobe scattering microwave imaging algorithm and a deep learning model based on microwave images are proposed to detect damage in radar-absorbing materials (RAMs) in real time. The proposed method enhances the quality of microwave images and utilizes semantic segmentation to improve target detection accuracy.
NDT & E INTERNATIONAL
(2022)
Article
Environmental Sciences
Wenhao Zhang, Yajun Li, Zhengqi Zheng, Lin Xu, Zhicheng Wang
Summary: High frequency radar has a wide monitoring range and low range resolution, which can contain multiple targets or outlier interference phenomena. The key to multi-target detection in background clutter is determining the attributes of targets or outliers. This paper proposes a new method for multi-target detection based on the ordered statistics constant false alarm detector (OS-CFAR), utilizing the sparse characteristics of the target and introducing regularization processing to eliminate interfering targets. Simulation and measured data demonstrate the effectiveness of the algorithm in counteracting interference and providing reliable target detection.
Article
Environmental Sciences
Jiankang Ma, Haoran Guo, Shenghui Rong, Junjie Feng, Bo He
Summary: This paper proposes a coarse-to-fine deep learning-based method for detecting infrared dim and small targets. The method utilizes a coarse-to-fine detection framework integrating deep learning and background prediction. Experimental results demonstrate that the proposed framework has effective detection capability and robustness for complex surroundings.
Article
Engineering, Electrical & Electronic
Minti Liu, Cao Zeng, Shidong Li, Haihong Tao, Guisheng Liao, Jun Li
Summary: This study proposes a novel solution for estimating multi-object trajectories with unknown object detection profiles in a nonuniform clutter background. The solution utilizes sensors with limited sensing range and combines maximum likelihood estimation and model-based clustering to estimate the unknown clutter intensity and object detection profiles. The proposed solution improves the performance of trajectory estimation by utilizing the TPMB filter and demonstrated excellent tracking accuracy compared to state-of-the-art solutions.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
James Theiler
Summary: The optimal detector for additive signals on Gaussian clutter is a linear matched filter adapted to the known signal and background covariance. This adaptive matched filter is widely used for gas-phase plume detection, and can be extended to absorptive plumes with a quadratic filter. By using an elliptically-contoured multivariate t distribution and deriving a generalized likelihood ratio test detector, the applicability of the quadratic matched filter can be extended to stronger plumes. Furthermore, expressions for estimating plume strength are derived and the performance of these detectors is evaluated using simulated plume implantation in background images with different clutter distributions.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Computer Science, Information Systems
Hang Su, Wei Wang, Shanwen Wang
Summary: In this paper, a method based on dual background and gradient is proposed for abandoned objects detection. The method uses temporal median filter and temporal minimum filter to extract foreground and static objects respectively. A gradient-based image processing algorithm is proposed to eliminate the interference of vehicle lights, enabling accurate detection at night. Experimental results show that the proposed method outperforms widely used abandoned objects detection methods on both ABODA dataset and our video dataset in highway scenes.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Environmental Sciences
Anran Zhou, Weixin Xie, Jihong Pei
Summary: With the rapid development of marine business, the intelligent detection of ship targets has become crucial for marine safety. However, accurately detecting maritime infrared targets is challenging due to severe sea clutter interference in strong wind waves or dim sea scenes. To adapt to diverse marine environments, a dual-mode sea background model is proposed for target detection.
