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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 1999"

共 764 条记录，以下是131-140 订阅

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Position Monitoring of Multiple Moving targets Based on ZigBee and GPS 4

Position Monitoring of Multiple Moving Targets Based on ZigB...

引用

4th Annual International conference on data Science and Business Analytics, ICDSBA 2020

作者： Dai, Yu Wang, Xuan Long, Wenjia Yu, Peng Ma, Junyu Zhixing College of Hubei University Wuhan China

ISBN: (纸本)9781728181646

Moving target monitoring is widely used in many fields such as industrial control, military, and security. Traditional moving target monitoring generally uses infrared, ultra-sonic and other technologies, which have the disadvantages of complex implementation technology, high hardware cost and high environmental requirements. This paper proposes a low-cost, easy-to-deploy, and small-scale environment with little environmental impact by collecting GPS monitoring signals of multi-target speed and location and signal transmission and processing methods based on ZigBee wireless ad hoc network. signal acquisition and real-time data storage, transmission and processing methods have high application value and promotion significance. © 2020 IEEE.

关键词： Global positioning system

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Image Electronic Evidence Screening Based on Improved SSD

Image Electronic Evidence Screening Based on Improved SSD

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Yafei Liu Liehui Jiang Tieming Liu Youwei Zhang Zhongyuan Network Security Research institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

ISBN: (纸本)9781665447300

With the development of information technology, electronic evidence plays an increasingly important role in the judicial trial. In Judicial Forensics, it is difficult to extract effective electronic evidence accurately and quickly from a large number of electronic data. Traditional means generally take artificial identification to collect, which takes a long time and is not efficient. Using SSD target detection and recognition algorithm instead of traditional means can effectively reduce the time of screening evidence, but the basic SSD neural network is prone to miss detection for small targets. To solve the above problems, an improved SSD-based image electronic evidence screening method is proposed. This method optimizes the SSD neural network adaptively, introduces the attention mechanism module in the shallow convolution layer of the network to improve the representation ability of the feature map, and fuses the image features obtained from different convolution layers with multi-scale features to increase the shallow feature information. The experimental data are used to test the improved algorithm and analyze the experimental results. It is found that compared with the original SSD neural network algorithm, the detection mean average precision of the improved algorithm is increased by 4.7%, reaching 84.3%, which shows the feasibility and effectiveness of the improved algorithm.

关键词： Image recognition Convolution Target recognition Fuses Neural networks Stacking signal processing algorithms

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Recognition of Pest Based on Faster RCNN 6th

Recognition of Pest Based on Faster RCNN

引用

6th conference on signal and Information processing, Networking and Computers, ICSINC 2019

作者： Zhang, Miaohui Chen, Yunzhong Zhang, Bo Pang, Kangning Lv, Bingxue Key Laboratory of Data Analysis and Processing of Henan Province Henan University Kaifeng China Postdoctoral Research Station of Geography Henan University Kaifeng China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China

ISBN: (纸本)9789811541629

Effective prediction of crop pest species and quantities can provide a priori knowledge for pest control. However, there are many kinds of pests and the color and texture of different pests are similar, traditional image processing and recognition methods based on time or frequency domain features and classifiers are hard to meet the needs of high-precision recognition. In order to improve the recognition result of agricultural pests, based on the target detection algorithm Faster RCNN, we put forward a method for the classification and identification of pests on the basis of online hard sample mining and residual network. The main work is as follows: (1) According to the different size of pests, the detection ability of the network for small targets is enhanced by changing the size and number of anchors;(2) In the feature extraction stage, we use ResNet-50 with stronger feature extraction ability rather than the original VGG16 network;(3) In order to solve the imbalance of samples, online hard Sample Mining Strategy is introduced in the training stage. The experimental outcome demonstrate that our method is effective, the improved algorithm achieves about 5% improvement in accuracy, and the network has a stronger ability to detect small targets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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A double-threshold target detection method in detecting low slow small target 8

A double-threshold target detection method in detecting low ...

引用

8th International conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019

作者： Yu, Jie Liu, Yulan Bai, Yang Liu, Feiliang Science and Technology on Electromagnetic Scattering Laboratory Beijing1100854 China Chaohu University Hefei230000 China

With the rapid development of Unmanned Aerial Vehicle (UAV), stealth warship and stealth tank, the mission of radar has become more difficult. The modern radar system must detect and track target through strong clutter from many aspect, such as ground and sea. Radar detection for low slow small （LSS）targets has gradually become a new focus. LSS target has a small Radar Cross Section (RCS) because of its small size, making the SNR of the echo signal very low. At the same time, the Doppler frequency of slow target is very low, making it difficult for conventional signal processing methods to separate the Doppler information from the clutter spectrum. Thus, it does make sense to study how to detect the LSS target in strong clutter efficiently. In this paper, the distribution characteristics of radar clutter and coherent accumulation algorithm for LSS target detection in complex electromagnetic environment are studied. On the base of that, a double-threshold target detection method based on coherent accumulation is proposed. To check out the efficiency of the detection method, an experiment has been conducted, and the data collected was used to test the detection algorithm, and the result has been shown in this paper. © 2020 The Authors. Published by Elsevier B.V.

关键词： Antennas

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Design of a Hyperspectral Imager Using COTS Optics for small Satellite Applications

Design of a Hyperspectral Imager Using COTS Optics for Small...

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2020 International conference on Space Optics, ICSO 2020

作者： Prentice, Elizabeth Frances Grøtte, Mariusz Eivind Sigernes, Fred Johansen, Tor Arne Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim7491 Norway University Centre in Svalbard Svalbard Science Centre LongyearbyenN-9171 Norway

This paper describes the design of a pushbroom hyperspectral imager built for small satellite applications. Its design allows for low weight, low cost, and flexible configuration making it accessible to both smaller projects and supplemental to larger ones. The imager is constructed from commercial off-the-shelf (COTS) optical and sensor components, along with a machined mechanical structure to enable rapid development times. The presented configuration was selected based on a 6U CubeSat ocean color mission developed at the Norwegian University of Science and Technology. The optical design includes three 50 mm lens objectives, a precision-cut 50 μm slit, a blazed transmission grating, and a detecting CMOS sensor. It has the ability to record wavelengths in the spectral range of 300−1000 nm, e.g. in the visible and near infrared spectrum. The calculated bandpass of typically about 5 nm can be configured or binned for the specific application needs. Since targets, such as the ocean surface, are dark and non-Lambertian, it is challenging to reach the necessary sensitivity that ensures a high signal-to-noise ratio (SNR) in the image. The imaging system presented in this paper is built for obtaining that sufficient SNR for a given resolution, both in the spatial and spectral domains. The satellite will be launched into a 500 km altitude sun-synchronous orbit. While in orbit, the imager utilizes the pushbroom concept of sequentially gathering lines of pixels, of all wavelengths in range, as it passes over its target. The pushbroom concept, combined with the optical design, yields a swath width of up to 70 km per scan line with a sampling distance of 49 × 60 m on ground. A final consideration must be made due to the large size of raw hyperspectral data cubes and the constraints this sets on satellite power consumption for downlink. This can be significantly improved through onboard image processing (e.g. correction, classification, anomaly detection, feature extraction, an

关键词： Orbits

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Maritime small targets Detection Method for Passive Bistatic Radar Using Non-Cooperative Radar as Illuminator of Opportunity

Maritime Small Targets Detection Method for Passive Bistatic...

引用

2019 IEEE International conference on signal, Information and data processing, ICSIDP 2019

作者： Jie, Song Guoqing, Wang Caisheng, Zhang Research Institute of Information Naval Aviation University YanTai China

ISBN: (纸本)9781728123455

This paper proposes the maritime small targets detection method of passive bistatic radar (PBR) using a noncooperative pulse radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to a higher range resolution, but there is a problem that radar transmitting signal parameters are unknown, and radar signal processing, such as pulse compression, non-coherent integration, and CFAR processing cannot be conducted accurately. The proposed method estimates radar signal parameters from the direct-path signal, and uses estimated parameters to radar signal processing of the scattered echo. The experiments use the shore-based maritime surveillance radar as a transmitter and ships in the harbor as the targets, which show that the proposed method can be applied to maritime small targets detection successfully. © 2019 IEEE.

关键词： Radar signal processing

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Research on recognition algorithm of LSS based on video in airport clearance area

Research on recognition algorithm of LSS based on video in a...

引用

Big data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), International conference on

作者： Jingyi Qu Xinjie Bi Shanliang Liu Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9781665415408

ISBN: (纸本)9781665447065

LSS (Low, small and Slow) targets have characteristics of low cost, simple operation, easy to carry, low take-off requirements, strong sudden takeoff, and difficult to find and handle. With the development of aviation industry, the cases of LSS flying illegally and used in terrorist attacks are increasing day by day. It will not only cause great negative effects on economy, but also have serious influence on the national security and the normal development of the national economy. In order to eliminate the threat of illegal flight of UAV in the airport clearance area to the safe operation of airport, a LSS recognition algorithm based on the deep learning was proposed in the paper. Compared with other detection types, the video detection was used in the algorithm, which has many advantages such as strong visualization, low cost and fast detection speed. By using the Yolov3 object detection model, one of the most popular deep learning methods, the problem of low accuracy of small objects such as LSS can be solved. The experiments show that the Yolov3 model can improve the accuracy for small objects and can effectively detect the LSS. Thus, the necessary precondition for the subsequent LSS countermeasures can be provided, and the safe operation of the airport can be ensured.

关键词： Deep learning Atmospheric modeling Training data Object detection Airports Internet of Things National security

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A Deep Learning-based Super-resolution Model for Bistatic SAR Image

A Deep Learning-based Super-resolution Model for Bistatic SA...

引用

Electronics, Circuits and Information Engineering (ECIE), International conference on

作者： Xi Cen Xuan Song Yachao Li Chunfeng Wu School of Life Science and Technology Xidian University Xi’An China Key Laboratory for Radar Signal Processing Xidian University Xi’An China China Sanjiang Space Group Wu Han China

ISBN: (纸本)9781665448109

Target detection and recognition of bistatic SAR image has been widely studied in recently. However, how to accurately detect and recognize targets with low-resolution and small size in the image is still a problem. Image super-resolution reconstruction technology can increase image resolution and expand target size to improve detection and recognition performance. Thus, we in this paper bring deep learning onto the topic of bistatic SAR image super-resolution reconstruction and propose a novel super-resolution reconstruction model (named FSRCNN) for bistatic SAR images. The proposed model is characterized by a feature extractor with different structures, a feedback feature enhancement block, and a feature fusion module. During the experiments of bistatic SAR measured data, our method is proved to perform clearer visual effects than other image super-resolution reconstruction method. Moreover, our model achieves the best value on peak signal to noise ratio and structural similarity, which indicates that our model can effectively super-resolution reconstruct bistatic SAR images.

关键词： Image recognition Target recognition Superresolution Feature extraction Radar polarimetry Integrated circuit modeling Image reconstruction

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Space Debris Tracking Via Generalized Labeled Multi-Bernoulli Random Finite Sets

Space Debris Tracking Via Generalized Labeled Multi-Bernoull...

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IEEE International conference on signal processing, Communications and Computing (ICSPCC)

作者： Sun Quan Ding Ding Niu Zhaodong Natl Univ Def Technol Coll Elect Sci & Technol Natl Key Lab Sci & Technol ATR Changsha 410073 Peoples R China Natl Univ Def Technol Ctr Teaching & Res Serv Changsha 410073 Peoples R China

ISBN: (纸本)9781728117089

Y In this paper we present an algorithm for detection and tracking space debris under complex space background via delta-generalized labeled multi-Bernoulli (delta-GLMB) random finite sets (RFS). It includes two parts, position extractor for suspected targets and tracking filter. The core strategy of this position extractor is morphological method. The filter is implemented by Sequential Monte Carlo (SMC) method and each iteration of delta-GLMB multi-target tracking filter includes two operations: update and prediction. The processing results of measured data show that the algorithm has strong detection and tracking ability for dim and small targets under complex space background. To the best of our knowledge, this is the first time that the delta-GLMB multi-target tracking filter has been successfully applied in the field of space debris target detection and tracking.

关键词： space debris detection GLMB tracking filter complex space background dim and small targets

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small targets recognition in SAR ship image based on improved SSD

Small targets recognition in SAR ship image based on improve...

引用

2019 IEEE International conference on signal, Information and data processing, ICSIDP 2019

作者： Li, Yong Chen, Jing Ke, Meng Li, Linghao DIng, Zegang Wang, Yan Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728123455

Synthetic Aperture Radar(SAR) ship recognition is significant in marine applications and plays an important role in maritime traffic management, fisheries management, and maritime rescue, etc. A major difficulty in SAR ship recognition is that the SAR ships have a small size in images, which results in low recognition accuracy of the SSD. This paper first analyzes the reasons for the low recognition accuracy for small targets in SSD. First, the poor matching of the default boxes leads to a small number of positive samples. Second, the representation ability of low-level feature map for recognizing small targets is weak. Then two strategies are proposed to improve the SSD. Firstly, a default box optimization design method based on Kmeans clustering is proposed, which improves the matching performance of the default boxes. Secondly, a feature fusion method based on deconvolution is proposed, which effectively improves the representation ability of low-level feature maps. The experimental results show that the proposed method can greatly improve the recognition accuracy of SSD for small targets in SAR ship images. © 2019 IEEE.

关键词： Synthetic aperture radar

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