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IEEE signal processing and Communications Applications (SIU)

作者： Kartal Kaan GÜMen Selçuk Paker Kubilay Savci Uydu Haberleşmesi ve Uzaktan Algılama İstanbul Teknik Üniversitesi İstanbul Türkiye Elektronik ve Haberleşme Mühendisliği İstanbul Teknik Üniversitesi İstanbul Türkiye Elektrik ve Elektronik Mühendisliği Koç Üniversitesi İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

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： Radar cross-sections Noise Radar detection Radar Radar signal processing Doppler radar Noise measurement

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

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

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signal, Information and data processing (ICSIDP), IEEE International Conference on

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Monte Carlo methods Fluctuations Filtering Radar measurements Image edge detection Simulation Detectors data processing Clutter

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Design and Implementation Considerations for a 3D Beamformer on State-of-the-Art Multicore Processors

Design and Implementation Considerations for a 3D Beamformer...

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OCEANS Conference

作者： Narayanan, Prakash M. Gopal, Vijay G. Rajesh, R. DRDO Naval Phys & Oceanog Lab Kochi Kerala India CUSAT Kochi Kerala India

ISBN: (纸本)9781665418218

Underwater targets have become increasingly difficult to be detected by sonars. Improvement in ship and submarine design technology have made it less noisy, and hence difficult to detect. And there is a proliferation of small Autonomous Underwater Vehicles (AUVs) for conducting surveillance operations, and recently, also for carrying out offensive missions. AUV are more difficult to detect compared to a submarine. This has resulted in sonar designers to search for options to improve the detection capabilities of sonars. However, there exist little options for sonar designers to improve the detectability of such underwater objects. Sonar designers have resorted to bigger arrays to improve the detection, by improving the directive gain of the array resulting in huge data to be handled by the signal processors. Improvement in processing technology has made available enough resources to handle the multitude of data thus generated. The processor technology in turn brings its own issues to be handled. The signal processor designer must take care the architectural details of the processor as well as its memory architecture to get maximum through put. The paper presents different approaches to implementation for a multicore processor. The approaches are based on OpenMP and POSIX thread. In the thread-based approach different types of stride of data are considered. The results show that the pthread based implementation with stride carefully chosen to avoid the memory conflicts results in high processor utilization. A higher block size enables the memory controller to keep the computational resources from starving for data, there by getting better utilization.

关键词： signal processing Array processing Beamformers Multicore device optimizations

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Research on Spatial Angle Super-Resolution Algorithm Based on Adaptive Layered Sparse

Research on Spatial Angle Super-Resolution Algorithm Based o...

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2023 International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Xue, Min Gao, Yuexin Wu, Zhixin Xidian University National Key Laboratory of Radar Signal Processing Xi'an China School of Aerospace Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798350318012

Compressed Sensing (CS) theory is based on the sparsity of signals and involves compressively sampling high-dimensional data to obtain a small number of linear observations that contain the complete information of the signals. By solving an optimization problem, the original signals can be recovered from these compressed linear observations. This paper combines CS theory with the sparse characteristics of targets in the spatial domain and proposes an adaptive layered sparse spatial angle super-resolution algorithm. This algorithm converts the target spatial angle super-resolution into the problem of using orthogonal basis to reconstruct sparse signals. Then, the target spatial angle super-resolution is carried out by using single snapshot data through optimization solution, and the target spatial angle domain is continuously reduced through layered orthogonal matching pursuit (LOMP) solution to complete the target spatial angle reconstruction. The effectiveness and robustness of the algorithm are verified by using the proposed algorithm to process the measured data. © 2023 IEEE.

关键词： Compressed sensing

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Research on Complex Street View Detection Algorithm Based on YOLOv8

Research on Complex Street View Detection Algorithm Based on...

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signal processing and Intelligent Computing (SPIC), International Conference on

作者： Haiyang Du Jun Liu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The current street scene is becoming more and more complex, with high pedestrian and vehicular traffic and a large number of targets. Aiming at the problems that the accuracy of the current complex street scene-oriented detection algorithms is not satisfactory and the leakage rate of small targets is unstable, the intelligent detection algorithm YOLOv8s- for complex street scenes is proposed, aiming at the intelligent detection of multi-targets in daily street scenes. This study is based on the target detection model of YOLOv8s, proposing a C2f-DCNv2 module that can sample features flexibly; adding a small target detection layer in the head; and using the Shape IoU loss function. The improved algorithm improves the precision, recall, and average accuracy by 2.2%, 3.6%, and 5.0%, respectively, compared with the initial algorithm, and these experimental data validate that this study can have a good performance when targeting the detection task of complex street scenes.

关键词： Performance evaluation Accuracy Pedestrians Shape Urban areas signal processing algorithms Object detection Real-time systems Mobile handsets Detection algorithms

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The high-resolution imaging science experiment (HiRISE) in the MRO extended science phases (2009-2023)

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ICARUS 2024年 419卷

作者： McEwen, A. S. Byrne, S. Hansen, C. Daubar, I. J. Sutton, S. Dundas, C. M. Bardabelias, N. Baugh, N. Bergstrom, J. Beyer, R. Block, K. M. Bray, V. J. Bridges, J. C. Chojnacki, M. Conway, S. J. Delamere, W. A. Ebben, T. Espinosa, A. Fennema, A. Grant, J. Gulick, V. C. Herkenhoff, K. E. Heyd, R. Leis, R. Ojha, L. Papendick, S. Schaller, C. Thomas, N. Tornabene, L. L. Weitz, C. Wilson, S. A. Univ Arizona Lunar & Planetary Lab Tucson AZ 85721 USA Planetary Sci Inst Tucson AZ USA Brown Univ Dept Earth Environm & Planetary Sci Providence RI 02912 USA US Geol Survey Astrogeol Sci Ctr 2255 N Gemini Dr Flagstaff AZ 86001 USA Ball Aerosp & Technol Corp Boulder CO 80301 USA SETI Inst Mountain View CA 94043 USA Univ Leicester Sch Phys & Astron Space Pk Leicester Leicester LE17RH England Le Mans Univ Univ Angers Nantes Univ CNRSUMR 6112Lab Planetol & Geosci Le Mans France Delamere Support Syst Boulder CO 80304 USA Smithsonian Inst CEPS Washington DC 20560 USA NASA Ames Res Ctr Moffett Field CA 94035 USA Univ Bern Sidlerstr 5 CH-3012 Bern Switzerland Western Univ Inst Earth & Space Explorat Dept Earth Sci 1151 Richmond St London ON N6A 5B7 Canada

The Mars Reconnaissance Orbiter has been orbiting Mars since 2006 and has acquired >80,000 HiRISE images with sub-meter resolution, contributing to over 2000 peer-reviewed publications, and has provided the data needed to enable safe surface landings in key locations by several rovers or landers. This paper describes the changes to science planning, data processing, and analysis tools since the initial Primary Science Phase in 2006-2008. These changes affect the data used or requested by the community and how they should interpret the data. There have been a variety of complications to the dataset over the years, such as gaps in monitoring due to spacecraft and instrument issues and special events like the arrival of new landers or rovers on Mars or global dust storms. The HiRISE optics have performed well except for a period when temperature uniformity was perturbed, reducing the resolution of some images. The focal plane system now has 12 rather than 14 operational detectors. The first failure (2011) was a unit at the edge of the swath width, reducing image width by 10% rather than creating a gap. The recent (2023) failure was in the middle of the swath. An unusual problem with the analog-to-digital conversion of the signal (resulting in erroneous data) has worsened over time;mitigation steps so far have preserved full-resolution imaging over all functional detectors. Soon, full-resolution imaging will be narrowed to a subset of the detectors and there will be more 2 x 2 binned data. We describe lessons received for future very high-resolution orbital imaging. We continue to invite all interested people to suggest HiRISE targets on Mars via HiWish, and to explore the easy-to-use publicly available images.

关键词： Mars surface Image processing Instrumentation Geological processes data reduction techniques

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Local Information Guided Global Integration for Infrared small Target Detection

Local Information Guided Global Integration for Infrared Sma...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Qiang Li Qianchen Mao Wenjie Liu Jinbao Wang Wenmin Wang Bingshu Wang School of Software Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Faculty of Innovation Engineering Macau University of Science and Technology Macau China

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.

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Dim and small targets based on the fusion of infrared radiation characteristics and spectral characteristics research on detection methods 8

Dim and small targets based on the fusion of infrared radiat...

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Dong, Liu Yang, Yining Liu, Tongyu Yong, Zhang Hu, Yutong Shuai, Jia Ma, Baoqiang Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

The geometric and motion characteristics of false targets are more similar to those of real targets. The detection accuracy of traditional infrared radiation characteristics methods for infrared targets is obviously low. For small infrared weak targets with small size and low signal-to-noise ratio, it is difficult to effectively extract shape features by using a single band detection method. Therefore, this paper makes full use of the advantages of infrared single band image and multispectral image target detection methods, and combines single band detection technology and multispectral detection technology, which not only avoids the defect of single band detection missing alarm, but also reduces the impact of large amount of multispectral detection data, and has real-time performance and good detection effect. The hybrid detection algorithm proposed in this paper has stronger adaptability, higher recognition rate and faster processing ability. © 2022 SPIE

关键词： Infrared radiation

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

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

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signal, Information and data processing (ICSIDP), IEEE International Conference on

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Sea surface Target tracking Surface waves Dynamics Predictive models Mathematical models Trajectory Kalman filters Vehicle dynamics Long short term memory

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DCUNet: Deformable Convolutional UNet for Multi-frame Infrared small Target Super-resolution

DCUNet: Deformable Convolutional UNet for Multi-frame Infrar...

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signal, Information and data processing (ICSIDP), IEEE International Conference on

作者： Yuanxin Huang Xiyang Zhi Wenbin Chen Xiaoyang Liang Zhipeng Wang Wei Zhang Research Center for Space Optical Engineering Harbin Institute of Technology Harbin China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The objective of infrared multi-frame super-resolution for small targets is to enhance the target's resolution by leveraging complementary information from multiple frames. However, the presence of motion variations and scale changes in infrared small targets introduces discontinuities in information between frames, posing challenges for super-resolution. To address the above challenges, we propose a multi-frame infrared small target super-resolution network based on deformable UNet. Specifically, the network takes multi-frame sequential infrared images as input and divides the input into reference frames and current frames. It utilizes deformable convolutions to align the frames to enhance the target features of the current frame. Additionally, the encoding structure of the UNet network is employed to extract multiscale features from the sequential images. By leveraging skip connections and a decoding structure, the network achieves the enhancement of target detail information and the fusion of multiscale features, ultimately outputting the super-resolved image. We conducted experiments on the IRDST and NUDT-MIRSDT datasets, and the results validate the practicality of our designed network.

关键词： Image segmentation Head Convolution Motion segmentation Superresolution Feature extraction Robustness Decoding Spatial resolution signal resolution

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