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8th International Conference on signal and Image processing, ICSIP 2023

作者： Chen, Yaqing Shen, Shijian Lei, Zhiyong Nanjing Research Institute of Electronics Technology Nanjing China

ISBN: (纸本)9798350397932

Because the sea clutter is greatly affected by the complex and changeable environment, it is difficult to describe its characteristics through a single amplitude statistical model. In addition, the great variety and the small radar cross section of the target on the sea surface increase the detection difficulty. This paper proposes a feature-weighted SVM algorithm, which regards the detection of small targets on the sea surface as a binary classification problem. The algorithm extracts six features from different domains of the sea clutter, calculates the weight of the features according to the contribution of the features and constructs the feature space for detection with weighted feature vectors. Experiments on IPIX data sets show that the detection method improves the detection accuracy compared with the traditional algorithm and it is helpful for sea surface monitoring. © 2023 IEEE.

关键词： Radar cross section

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A new high-resolution seismic wavelet definition and vibroseis sweep signal design 4

A new high-resolution seismic wavelet definition and vibrose...

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4th International Meeting for Applied Geoscience and Energy, IMAGE 2024

作者： Nie, Zhenbo Wang, Huazhong Sheng, Shen School of Ocean and Earth Science Tongji University Shanghai200092 China

Seismic exploration has increasingly focused on specific geological bodies, such as thin layers, small-scale anomalous bodies, fracture-cavities, and small faults. As the burial depth of these targets increases, the requirements of imaging resolution become higher. Seismic data acquisition is the first step in seismic exploration and consequently determines the accuracy of subsequent processing, imaging and interpretation. The seismic wavelet serves as the basic unit of the acquired pre-stack seismic data. Generating a high-resolution wavelet in the field is essential for subsequent high-resolution processing. In this abstract, we introduce a novel method for defining high-resolution seismic wavelets, termed "customized wavelets". The time domain waveform of customized wavelet is determined by three parameters: main lobe width, amplitude ratio of main lobe to side-lobe, and width ratio of main lobe to side-lobe. These key parameters of the customized wavelet can be adjusted to meet varying resolution requirements for different geological bodies. Once the high-resolution customized wavelet is generated, the vibroseis sweep signal can be designed accordingly. By using the designed sweep signal as the excitation source for vibroseis vehicle, the high-resolution processing and imaging of the target geological bodies can be achieved. The corresponding experiments verify the correctness and effectiveness of the proposed method. © 2024 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.

关键词： Seismic prospecting

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Minimizing the Maximum Length of Flight Paths for UAVs Providing Location Service to Ground targets

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IEEE INTERNET OF THINGS JOURNAL 2022年第12期9卷 9904-9917页

作者： Zhu, Xiaojun Wang, Youpeng Xu, Lijie Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210023 Peoples R China

When a UAV broadcasts its location periodically during flight, a target will observe the strongest signal when the UAV is at the closest location;thus, the target can infer its location based on the observed signals and the UAV's trajectory. We consider how to design trajectories for UAVs such that all targets can locate themselves, and the maximum length of trajectories is minimized. To make the problem tractable, we impose constraints on the set of possible trajectories such that they only contain the edges of small squares. We propose an integer-linear-programming-based solution, which runs in exponential time, and two polynomial-time constant-factor approximation algorithms. We implement a prototype system to verify the feasibility of the localization method. Simulations show that our approach outperforms the existing ones in terms of flight length and localization error.

关键词： Trajectory Location awareness Hardware Global Positioning System Internet of Things Distance measurement Security Localization service relative signal strength trajectory optimization unmanned aerial vehicle

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MTKD-Det: An Efficient SAR Target Detector Based on Multi-Teacher Knowledge Distillation 2

MTKD-Det: An Efficient SAR Target Detector Based on Multi-Te...

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2nd IEEE International Conference on signal, Information and data processing, ICSIDP 2024

作者： Zhang, Guang Wang, Zhaocheng Wang, Ruonan Song, Hualiang Kang, Hailong Hebei University of Technology School of Electronics and Information Engineering Tianjin China Xidian University Hangzhou Institute of Technology Hangzhou China Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798331515669

As a critical branch of synthetic aperture radar (SAR) interpretation, SAR target detection has many applications in the military and civil fields. Although traditional deep learning methods achieve some satisfactory performance in target detection tasks, they inherently require significant computational resources and interpretation time. Hence, traditional deep learning methods frequently struggle to maintain a balance between high efficiency and precision. It is undoubtedly a huge challenge for target detection tasks. To address this problem, an efficient SAR target detector based on multi-teacher knowledge distillation (MTKD-Det) is proposed in this paper, which can effectively enhance target detection accuracy while reducing inference time. Specifically, a multiple-teacher knowledge distillation strategy is used in the proposed method, which integrates the experiences and methodologies of multiple teachers to collaboratively guide the training and learning of the student network, ultimately enhancing the robustness of the student network. In this work, a novel teacher network is developed to enhance the detection performance of the proposed method specifically for small targets. Furthermore, an attention mechanism is incorporated into the student network to enable it to focus on more critical information, thereby improving overall detection efficacy. Experimental results obtained from a measured SAR target detection dataset convincingly validate the benefits of the proposed approach. © 2024 IEEE.

关键词： Synthetic aperture radar

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A training method to improve the effect of small target detection

A training method to improve the effect of small target dete...

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2022 International Conference on Image, signal processing, and Pattern Recognition, ISPP 2022

作者： Chen, Boxu Yu, Lixin Meng, Xiao Hua, Yang Beijing Microelectronic Technology Institute Beijing100076 China

ISBN: (纸本)9781510654846

Deep learning target detection has always been a major research direction in the field of artificial intelligence. Its research results are widely used in the fields of automatic driving, security system and medical treatment. This paper proposes a method to improve the detection effect of small targets, which realizes the detection of objects of different scales in the input image, especially to improve the detection effect of small-scale targets. Before the collected data set is sent to the neural network for training, it is first divided into three different scales according to the size of the target to be detected in the image of the data set. Then one or several images in the large target data set are stitched, the images in the small target data set are enlarged, and the above two types of images are used to form a new data set. Finally, the new data set and the original data set are sent to the neural network for training. In this paper, the YOLOX target detection network is used for verification. The results show that the detection effect of the network obtained by this method on small targets has been improved, and the missed detection rate has been reduced from 31.2% to 27.5%. At the same time, the detection effect of large and medium-sized targets has not been sacrificed. © 2022 SPIE.

关键词： Image enhancement

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Light Gradient Boosting Machine-Based Low-Slow-small Target Detection Algorithm for Airborne Radar

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REMOTE SENSING 2024年第10期16卷 1737-1737页

作者： Liu, Jing Huang, Pengcheng Zeng, Cao Liao, Guisheng Xu, Jingwei Tao, Haihong Juwono, Filbert H. Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China

For airborne radar, detecting a low-slow-small (LSS) target is a hot and challenging topic, which results from the rapidly increasing number of non-cooperative flying LSS targets becoming of widespread concern, and the low signal-to-clutter ratio (SCR) of LSS targets results in the targets being particularly easily overwhelmed by the clutter. In this paper, a novel light gradient boosting machine (LightGBM)-based LSS target detection algorithm for airborne radar is proposed. The proposed method, based on the current real-time clutter environment of the range cell to be detected, firstly designs a specific real-time space-time LSS target signal repository with special dimensions and structures. Then, the proposed method creatively designs a new fast-built real-time training feature dataset specifically for the LSS target and the current clutter, together with a series of unique data transformations, sample selection, data restructuring, feature extraction, and feature processing. Finally, the proposed method develops a unique machine learning-based LSS target detection classifier model for the designed training dataset, by fully excavating and utilizing the advantages of the ensemble decision trees-based LightGBM. Consequently, the pre-processed data in the range cell of interest are classified using the proposed algorithm, which achieves LSS target detection by evaluating the output results of the designed classifier. Compared with the traditional classical target detection methods, the proposed algorithm is capable of providing markedly superior performance for LSS target detection. With an appropriate computational time, the proposed algorithm attains the highest probability of detecting LSS targets under the low SCR. The simulation outcomes and detection results with the experimental data are employed to validate the effectiveness and merits of the proposed algorithm.

关键词： clutter suppression airborne radar target detection low-slow-small target light gradient boosting machine

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Design of Human-computer Interaction System for Infrared small Target Detection 24

Design of Human-computer Interaction System for Infrared Sma...

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7th International Conference on Image and Graphics processing, ICIGP 2024

作者： Kou, Renke Wang, Chunping Fu, Qiang Li, Wei Yu, Ying Feng, Changfeng Army Engineering University Shijiazhuang Campus Hebei Shijiazhuang050003 China University of Sanya School of Information & Intelligence Engineering Hainan Sanya572022 China School of Information and Electronics Beijing University of Technology Beijing10081 China

ISBN: (纸本)9798400716720

Infrared (IR) passive detection technology is widely utilized for identifying small targets over long distances, owing to its robust anti-interference capabilities and all-weather functionality. As of now, numerous IR small target detection algorithms have been publicly published. To promote research in this field, this paper first briefly introduces single IR small target detection algorithms, which are primarily categorized as model-driven and data-driven. Subsequently, the paper presents human-computer interaction (HCI) systems tailored to these two algorithm categories, incorporating a total of 20 existing model-driven algorithms and 15 data-driven algorithms, respectively. Ultimately, the paper delves into potential avenues for optimizing HCI system design. The proposed IR small target detection HCI systems not only improve user-friendliness, but also provide a benchmark platform for experimental comparison. The introduction of relevant HCI systems can be found on kourenke/GUI-system-for-IR-small-target-detection (***). © 2024 ACM.

关键词： signal detection

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Interpretable Deep Probabilistic Model for HRR Radar signal and its Application to Target Recognition

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2022年第4期16卷 775-790页

作者： Liao, Leiyao Du, Lan Chen, Jian Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

With the advent of neural network, unprecedented advancements have been achieved in many tasks, including radar signal processing. However, a key disadvantage of the current methods is the black-box structure, which makes it hard to interpret or assess the hidden representations of data. In this paper, by combining the physical generative mechanism of the high range resolution (HRR) radar signal with neural network, we develop an interpretable deep probabilistic model to learn the latent features from HRR radar signals that can characterize the physical structure of targets. In detail, based on the radar target's scattering center model which describes the HRR radar signal as the summation of echoes from the scattering centers, a deep probabilistic model is constructed to depict the generative process from the scattering centers to observations, where the latent features comprise the locations and amplitudes of scattering centers. Considering that the locations of scattering centers are nearly invariable and the amplitudes of them are fluctuant for the signals within a small angular range, our model defines the shared locations and independent amplitudes of scattering centers for the signals in a small angular range, aiming to improve the robustness of interpretable feature extraction model. In addition, our model performs probabilistic inference on the posterior distribution of latent features with high preciseness. With the proposed model, we further design a recognition scheme based on the minimum reconstruction error criterion. Experiments on the measured HRR radar dataset validate the effectiveness of our model on learning interpretable features and superior recognition performance.

关键词： Radar Scattering Feature extraction Target recognition Radar scattering Probabilistic logic Neural networks Radar automatic target recognition (RATR) interpretability neural network variational inference minimum reconstruction error

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Imaging Ultrasound Scattering targets using Density-Enhanced Chaotic Compressive Sampling

Imaging Ultrasound Scattering Targets using Density-Enhanced...

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Asia-Pacific-signal-and-Information-processing-Association Annual Summit and Conference (APSIPA ASC)

作者： Luong Thi Theu Tran Quang Huy Tran Thi Thuy Quynh Duc-Tan Tran Hoa Binh Univ Hanoi Vietnam Hanoi Pedag Univ 2 Fac Phys Hanoi Vietnam VNU Univ Engn & Technol Fac Elect & Telecommun Hanoi Vietnam Phenikaa Univ Fac Elect & Elect Engn Hanoi 12116 Vietnam

ISBN: (纸本)9798350300673

The ultrasound imaging method is a highly promising technique used in various fields. The utilization of ultrasound technique's backscattering could also allow humans to observe objects as small as the size of the incident wavelength. However, the employing backscattering approach requires high computational calculations and poses a significant obstacle to the production of equipment using the ultrasonic method. In this work, we utilized the pseudo-random compression sampling method in the extended DBIM model, which takes tissue density factor. This is necessary because tissue density provides quantitative information about soft tissue and serves as an image contrast source. This method combines pseudo-random measurement and compressed sensing data recovery technique. The simulation results have proved the effectiveness of the proposed CCS-DBIM method. In terms of imaging quality, at compression ratios as low as 0.5, the large difference in imaging quality is evident. After 5 iterations, the normalization error of the DBIM and CCS-DBIM methods was 0.8828 and 0.5281 respectively, i.e. a 40% reduction in normalization errors when the number of measurements was only half of the variables. The disadvantage of the proposed CCS-DBIM method is that the imaging time is significantly greater than that of the DBIM method. However, with today's parallel computing technology, large amounts of data and computational time will no longer be a big problem.

关键词： Ultrasonic imaging distorted Born iterative method pseudo-random scattered data Born approximation

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Sea Surface small Target Detection using Tri-median Normalized Doppler Spectrum Feature 16

Sea Surface Small Target Detection using Tri-median Normaliz...

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16th International Conference on signal processing Systems, ICSPS 2024

作者： Guo, Zi-Xun Wu, Kang-Hui Bai, Xiao-Hui Su, Jia Fan, Yi-Fei Gong, Yan-Yun Wang, Ling Shui, Peng-Lang School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China School of Information Xi’an University of Finance and Economics Shaanxi Xi’an710100 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

ISBN: (纸本)9781510689251

This letter aims to develop a detector for detecting small targets on the sea surface based on feature utilization median normalization. At the long observation time, sea clutter and returns with target show differences of Doppler amplitude spectra (DAS). The median normalized DAS (MNDAS) is used to enhance the Doppler characteristics of returns with target, which is computed from the DAS of the cell under test and its neighboring reference cells. From the MNDAS, three salient features can be extracted for the detection, including the non-zero interval length, vector entropy and global accumulation in the MNDAS. The three features are combined into 3D feature vectors. Considering that the returns with target and their feature vectors cannot be used in the training, one semi-supervised classifier based on the convexhull learning algorithm is built by the feature vectors of sea clutter only. Finally, a new detector based on feature utilization median normalization which can control the false alarm rate precisely is constructed. Through the experiments on the IPIX and CSIR radar databases and one offshore experimental data using an unmanned aerial vehicle with a corner reflector, the proposed detector attains competitive performance and lower computation cost with the TF-tri-feature-based detector. © 2025 SPIE.

关键词： Vectors

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