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检索条件"任意字段=Signal and Data Processing of Small Targets 1995 Conference"

共 783 条记录，以下是1-10 订阅

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Examination of Micro-Doppler Signatures of Drone and Seagulls with X-Band Noise Radar 32

Examination of Micro-Doppler Signatures of Drone and Seagull...

引用

32nd IEEE signal processing and Communications Applications conference (SIU)

作者： Gumen, Kartal Kaan Paker, Selcuk Savci, Kubilay Istanbul Tech Univ Uydu Haberlesmesi & Uzaktan Algilama Istanbul Turkiye Istanbul Tech Univ Elekt & Haberlesme Muhendisligi Istanbul Turkiye Koc Univ Elekt & Elekt Muhendisligi Istanbul Turkiye

ISBN: (纸本)9798350388978;9798350388961

In this study, data obtained from near-range radar responses collected by an X-band noise radar targeting small aerial objects such as drones and seagulls have been analyzed for the purpose of target discrimination. The research aims to identify distinctive features in terms of range, speed, and micro-Doppler signatures of small air targets using a noise radar operating at the same frequency as maritime ship radars. This approach has not only facilitated the detection of small air targets with low Radar Cross Section (RCS) but also contributed additional information for classifying these targets. Considering that the noise radar used during the measurements possesses characteristics typical of a marine radar, this work has effectively demonstrated the extraction of micro-Doppler signatures of drones and seagulls performing various maneuvers in a maritime environment.

关键词： Noise Radar signal processing Target Detection Micro Doppler Signature Time-Frequency Analysis

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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...

引用

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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MWIRSTD: A MWIR small TARGET DETECTION dataSET 31

MWIRSTD: A MWIR SMALL TARGET DETECTION DATASET

引用

2024 International conference on Image processing

作者： Kumar, Nikhil Upadhyay, Avinash Sharma, Shreya Sharma, Manoj Singh, Pravendra IIT Roorkee Roorkee Uttar Pradesh India Bennett Univ Greater Noida Uttar Pradesh India Visual Cognit Lab Pvt Ltd Noida Uttar Pradesh India

ISBN: (纸本)9798350349405;9798350349399

This paper presents a novel mid-wave infrared (MWIR) small target detection dataset (MWIRSTD) comprising 14 video sequences containing approximately 1053 images with annotated targets of three distinct classes of small objects. Captured using cooled MWIR imagers, the dataset offers a unique opportunity for researchers to develop and evaluate state-of-the-art methods for small object detection in realistic MWIR scenes. Unlike existing datasets, which primarily consist of uncooled thermal images or synthetic data with targets super-imposed onto the background or vice versa, MWIRSTD provides authentic MWIR data with diverse targets and environments. Extensive experiments on various traditional methods and deep learning-based techniques for small target detection are performed on the proposed dataset, providing valuable insights into their efficacy. The dataset and code are available at https://***/avinres/MWIRSTD.

关键词： small target detection small and dim target detection point target detection MWIR dataset defence application

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FBI-Net: Frequency Band Integration Network for Infrared small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Xin, Biqiao Li, Qiang Mao, Qianchen Wang, Jinbao Wang, Bingshu School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches. © 2025 IEEE.

关键词： Feature Fusion Infrared small Target Segmentation Semantic Injection Wavelet Transformation

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LOCAL INFORMATION GUIDED GLOBAL INTEGRATION FOR INFRARED small TARGET DETECTION 49

LOCAL INFORMATION GUIDED GLOBAL INTEGRATION FOR INFRARED SMA...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Li, Qiang Mao, Qianchen Liu, Wenjie Wang, Jinbao Wang, Wenmin Wang, Bingshu Northwestern Polytech Univ Sch Software Xian Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Infrared small targets often exhibit small scale and weak semantic features, which makes it a great challenge to their detection. To address this situation, we propose a novel network for infrared small target detection that combines local details information and global contextual information. To preserve the local and high-frequency details present in infrared images, we introduce a High-frequency Aware Encoder. To extract contextual information from multi-scale feature maps, we propose a Multi-scale Context Learning Bottleneck that incorporates contextual information repeatedly and performs cross-level fusion, which enables the recognition of small targets based on their surroundings. Finally, a lightweight Transformer Decoder is employed to restore the feature map, while placing attention on the target pixels. Experimental results on the IRSTD-1k dataset demonstrate that our method outperforms other state-of-the-art approaches.

关键词： infrared small target detection local inductive bias information context and cross-level fusion Swin Transformer

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Improvement of small Target Detection Algorithm Based on SSD 2

Improvement of Small Target Detection Algorithm Based on SSD

引用

2nd International conference on signal processing and Intelligent Computing, SPIC 2024

作者： Li, Yuzhi Li, Fang Zhou, Xiaolin Yin, Xiangyang Li, Shuhui Dai, Jinchi Zhang, Ce Shenyang Ligong University Shenyang China Emergency Management Bureau Rizhao China North China Electric Power University Beijing China

ISBN: (纸本)9798350368888

To address the issues of imbalanced target distribution within the dataset and the deficiency of high-level semantic information in the lower-level feature maps for small target detection, this paper proposes a small target detection algorithm grounded in feature fusion. The algorithm builds upon the SSD framework. To enhance the detection background for small targets, a data augmentation technique is employed, bolstering the model's generalization capacity for small and medium-sized targets. To augment the semantic information in the lower-level feature maps, a feature fusion module is incorporated, injecting high-level semantic data into the low-level feature map. Comparative experiments conducted on the dataset demonstrate the algorithm's effectiveness, showing improved overall detection performance and enhanced accuracy for small and medium-sized targets. © 2024 IEEE.

关键词： data enhancement deep learning feature fusion target detection

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A method for enhancing infrared small target images under complex sky backgrounds 2

A method for enhancing infrared small target images under co...

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

作者： Xu, Yuhan Tang, Linbo Sun, Haotian Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Radar Research Lab School of Information and Electronics Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9798331515669

Infrared target detection is currently widely used in both military and civilian fields. Due to the lower resolution of infrared images compared to visible light images and the lack of texture information in small targets, the quality of infrared small target images is often poor and not suitable for direct detection. Therefore, enhancing low-quality infrared small target images is a necessary step before object detection can be performed. This article presents a method for enhancing infrared images of small targets. The process begins with applying guided filtering to the original image. The resulting detail layer is then used as a guided image for a second guided filtering of the base layer, producing a second detail layer. Additionally, a novel approach to local contrast enhancement is introduced, which applies contrast enhancement specifically to this secondary detail layer. Experimental results has been demonstrated that the enhancement method proposed in this paper significantly improves the visibility of the target area while reducing background interference, without losing information about small targets. Additionally, this method outperforms other common techniques in terms of enhancement effect, as shown in comparative experiments. © 2024 IEEE.

关键词： Wiener filtering

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Sea Surface small Target Tracking Method with Integrated Dynamic Wave Prediction Information 2

Sea Surface Small Target Tracking Method with Integrated Dyn...

引用

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

作者： Li, Zixuan Jin, Biao Sun, Kangsheng Liu, Chao Ocean College Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798331515669

Tracking small targets on the sea surface has always been a challenging issue in the field of maritime target monitoring. Traditional methods typically rely on specific dynamic equations to establish the motion state model of the target (such as CV, CA, and CT models or their combinations), which struggle to accurately represent the motion states of small targets under complex sea conditions. We propose a method that incorporates deep learning to predict the dynamic fluctuations of sea waves, integrating real-time wave state information into the target state model. This method, which fuses sea wave dynamic prediction information, aims to improve the tracking of small sea surface targets. Firstly, we simulate various sea conditions based on the Elfouhaily wave spectrum to establish a database of small target motion trajectories on the sea surface. Then, we employ an LSTM network with a dual-stage attention mechanism to predict wave heights, which are subsequently fed into the state transition model. We utilize the Extended Kalman Filter for tracking. Simulation results demonstrate that with the assistance of sea wave dynamic prediction information, the proposed method enhances target tracking accuracy by approximately 14% compared to traditional methods. © 2024 IEEE.

关键词： Extended Kalman filters

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A Cascade-filtered Adaptive CFAR Detector in Low small Target Detecting 2

A Cascade-filtered Adaptive CFAR Detector in Low Small Targe...

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

作者： Ni, Leibo Xu, Wenwen Wang, Hao Zhang, Fengze Xu, Dalong Cao, Jianyin Nanjing University of Science and Technology Nanjing210094 China Nanhu Laboratory Jiaxing314050 China Changchun University of Science and Technology Changchun130022 China

ISBN: (纸本)9798331515669

In the context of detecting low-flying small targets in urban high-rise environments, the targets are often obscured by large clutter reflectors or non-uniform clutter regions. This paper proposes a cascaded filtering adaptive constant false alarm rate (CFAR) detector. This method consists of two cascaded detectors. Units judged as "target present"by the first stage are filtered out in the second stage. Compared to single-stage detectors, this method effectively integrates the performance advantages of multiple CFAR techniques. This makes it suitable for more complex detection environments. Based on measured data and Monte Carlo simulation results, the cascaded filtering CFAR detector has a high detection rate in multi-target environments and a low false alarm rate in non-uniform clutter edge environments. © 2024 IEEE.

关键词： Radar clutter

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A small Target Detection Method in Sea Clutter Based on Feature Manifold 2

A Small Target Detection Method in Sea Clutter Based on Feat...

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

作者： Guan, Jian Jiang, Xingyu Chen, Baoxin Ding, Hao Dong, Yunlong Liu, Ningbo Naval Aviation University Information Fusion Institute Yantai China

ISBN: (纸本)9798331515669

Target detection methods based on multidimensional features are often applied to the detection of small targets in sea clutter. However, existing algorithms do not fully utilize the correlation information between features, which to some extent limits their performance. To address this issue, this paper proposes a small target detection method in sea clutter using feature manifolds. By establishing a high-dimensional manifold representation of radar echo feature data, the radar target detection problem is transformed into a discrimination problem between two geometric points on a feature matrix manifold. This method integrates the use of multidimensional features and their correlation information, thereby effectively improving target detection performance. Experimental results on the SDRDSP dataset demonstrate that the proposed detection approach outperforms existing feature-based methods in terms of detection accuracy. © 2024 IEEE.

关键词： Clutter (information theory)

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