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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2010"

共 976 条记录，以下是131-140 订阅

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A Bootstrap-Bayesian Dynamic Modification Model Based on small Sample Target Features

A Bootstrap-Bayesian Dynamic Modification Model Based on Sma...

引用

Global OCEANS Singapore - U.S. Gulf Coast conference

作者： Han, Peng Yao, Xiling Zhan, Jiayuan Bai, Yuanyuan Northwestern Polytech Univ Sch Marine Sci & Technol Xian Peoples R China

ISBN: (纸本)9781728154466

The classification and recognition of underwater target signal are based on the samples. In the practical field, the amount of available training samples is limited. In this paper, based on analyzing the radiated noise signals of two types of underwater targets, the Bayesian dynamic modification model for underwater acoustic signals is established, which follows the idea of virtual sample generation. According to idea of virtual sample generation, a Bootstrap-Bayesian dynamic modification model for the small-sample-size problem is proposed, based on Bootstrap method and Bayesian parameter estimation. Through the processing of experimental data and comparative analysis of the experimental results, the effectiveness of the feature extraction method and the dynamically modified classification model proposed in this paper are verified. The applicability of the classification model for two types of underwater acoustic signals has also been confirmed.

关键词： dynamic modification model small-sample-size problem feature extraction bootstrap underwater acoustic signal classification

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Character Recognition Based on Multi-scale Feature Fusion Transfer Learning 6th

Character Recognition Based on Multi-scale Feature Fusion Tr...

引用

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

作者： Zhang, Miaohui Pang, Kangning Chen, Yunzhong Zhang, Bo Key Laboratory of Data Analysis and Processing of Henan Province Henan University Kaifeng China

ISBN: (纸本)9789811541629

Video surveillance has been using in daily life widely and has significant impact in security field. Reading time information in video has becoming an essential part. Because of the complex background of timestamp and the high similarity between targets, the features extracted by traditional machine learning algorithm cannot meet the requirements and cannot achieve the required detection speed and accuracy. In this paper, we improved the algorithm with multi-scale features. Features extracted from convolution layers of different scales are used to predict, which could improve the recognition accuracy of small targets. In addition, because the size of the collected dataset is 2860 and the amount of data is not large enough, we use data augmentation and transfer learning to train. Pre-training on the public dataset SVHN firstly, and then trains on the special dataset. Experiments show that the algorithm can recognize timestamps accurately. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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Faint Moving small Target Detection based on Optical Flow Method 7

Faint Moving Small Target Detection based on Optical Flow Me...

引用

7th International conference on Intelligent Computing and signal processing, ICSP 2022

作者： Dong, Yunfei Jieshou Zhongtong Instruments Co. Ltd. Fuyang236500 China

ISBN: (纸本)9781665478571

Moving target detection algorithm plays a vital role in computer vision research. Moving object detection mainly processes video images to identify moving objects differently from the background. Moving target detection algorithm has an excellent application role, such as: used for security and forbidden area security. This paper presents an effective method for detecting moving targets. The authors combine the corner detection method with LK optical flow method. Afterimage preprocessing, image corner detection, finally, we use LK optical flow method to detect the movement of the moving object, and we can judge the movement direction of the moving object only by two frames of pictures. This method can judge the direction of moving objects only by two pictures frames and has an excellent performance in speed detection. In particular, in detecting small moving targets, the results of this method are noticeable. © 2022 IEEE.

关键词： Computer vision

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Human Movement Tracking and Fall Detection Using UWB Impulse Radar

Human Movement Tracking and Fall Detection Using UWB Impulse...

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IEEE International conference on Radar

作者： Bin Rao Ping Hu Jiaqi Liu School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen China Department of Ophthalmology General Hospital of Southern Theatre Command Guangzhou China School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen China

ISBN: (纸本)9781665468893

Non-contact medical monitoring is an emerging challenging field. In this paper, based on the single chip radar development kit of XeThru X4 ultra wideband (UWB) modules, a small radar system is systematically designed for indoor human body tracking, and fall detection. The algorithms are compared, optimized, and integrated in the system. In signal processing, moving target indication (MTI) and clutter map methods are employed to cancel strong fixed clutter; wavelet analysis is used to denoise and reconstruct the echoes of human breathing and heart-movement; center-of-gravity ranging method is used to detect the presence of human targets and to measure the human range. In data processing, using only the range dimensional measurement, the algorithms for single human target track initialization, track filtering, data association, and track management are designed in a systematic way. Since a normal human body is usually stationary or moving uniformly, a constant-velocity (CV) model is adequately to estimate the human state, and the normalized innovation squared (NIS) is sliding averaged to implement fall judgment. The field data show that the system can effectively detect and track a human body, make timely judgments of human falling and drastic moving changes of state, and extract other vital features.

关键词： Target tracking Radar detection signal processing algorithms signal processing Radar tracking Feature extraction data processing

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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...

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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

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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...

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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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