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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2000"

共 865 条记录，以下是121-130 订阅

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Practical Detection using Sparse signal Separation

Practical Detection using Sparse Signal Separation

引用

IEEE International Radar conference (RADAR)

作者： Rosenberg, Luke Duk, Vichet Ng, Brian Def Sci & Technol Grp Canberra ACT Australia Univ Adelaide Adelaide SA Australia Fraunhofer FHR Wachtberg Germany

ISBN: (纸本)9781728168135

Due to the dynamic motion of the sea, radar detection of small maritime targets can be difficult. Airborne maritime surveillance platforms are also increasingly required to operate at higher altitudes where backscatter from the sea becomes stronger. The focus of this paper is to investigate three sparse signal separation formulations using the short time Fourier transform as a dictionary. This approach has been demonstrated as effective in separating both stationary and moving targets from sea clutter, but relies on the tuning of different parameters. The first part of this work looks at the selection of the penalty parameter, which is essential to achieve good separation. Then a number of practical detection schemes are presented that allow control of the false alarm rate. The algorithm performance is demonstrated using Monte-Carlo simulation with synthetic targets injected into the Ingara medium grazing angle sea-clutter data set.

关键词： Sea clutter radar detection compressive sensing

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small Target Detection Method in Sea Clutter Based on Interframe Multi-feature Iteration

Small Target Detection Method in Sea Clutter Based on Interf...

引用

IEEE International conference on signal and Image processing (ICSIP)

作者： Shuwen Xu Yitu Ma Xiaohui Bai National Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781665446006

For marine radar, since sea clutter is complex and dynamic, and small targets are difficult to model, the detection of floating small targets in sea clutter is always the focus and difficulty of radar target detection. The radar cross section of the floating small targets is small and the velocity is slow, so traditional detection methods have some bottlenecks, which belong to the problem of "over-clutter detection". The multi-feature-based detection method is an effective way to solve the detection of small targets on the sea surface, but traditional feature-based detection generally requires long accumulated time. Traditional scanning radar needs to take into account the surveillance space, which makes the number of accumulated pulses at each beam position restricted. It limits the application of feature detection methods to scanning radars. In this paper, aiming at the problem that the radar cannot stare at an angle for a long time in scanning mode, three features of relative average amplitude, relative Doppler peak height, and relative Doppler vector entropy are firstly extract. After that, the idea of multi-feature iteration between frames is used to update the feature values in the feature space, and then the fast convex hull algorithm is used to construct the decision region for detection. The measured data verifies that the proposed method can be well applied to scanning radar and obtains better detection performance than original methods.

关键词： Radar cross-sections Radar clutter Surveillance Radar detection Radar Object detection Feature extraction

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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...

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2021 International conference on Electronics, Circuits and Information Engineering, ECIE 2021

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

ISBN: (纸本)9781665418690

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. © 2021 IEEE.

关键词： Synthetic aperture radar

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Intelligent System for Real time detection and classification of Aerial targets using CNN

Intelligent System for Real time detection and classificatio...

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International conference on ent Computing and Control Systems (ICICCS)

作者： Madhusudan Upadhyay Somanchi Krishna Murthy A A Bazil Raj Defence Institute of Advanced Technology Pune India

The advancements in drone technology has led to increased usage of drones in civil and military which poses significant security related challenges. The small size of drones and birds pose a significant problem for their accurate detection in real time. These aerial targets have reduced RCS and operate at low altitudes with speeds of 40-80kmph making it difficult to accurately detect and classify them. This paper focusses on the design and development of an intelligent system for the detection and classification of these aerial targets by obtaining their micro-Doppler characteristics. The work integrates the signal processing techniques of radar-based detection with neural network classifier model for real time target classification.

关键词： Training Neural networks data acquisition Process control Object detection signal processing Real-time systems

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Tackling the different target dynamics issues in counter drone operations using passive radar

Tackling the different target dynamics issues in counter dro...

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IEEE International Radar conference (RADAR)

作者： Martelli, Tatiana Filippini, Francesca Colone, Fabiola Sapienza Univ Rome Dept Informat Engn Elect & Telecommun DIET Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9781728168135

This paper deals with the detection of small unmanned air vehicles (UAVs) and drones in high-density target scenarios such as airport terminal areas. In fact, in such conditions, strong reflections by high radar cross section (RCS) targets are likely to prevent the detection of very weak target echoes. In this work, we aim at overcoming this issue by taking advantage of long coherent processing intervals (CPIs) and by implementing a CLEAN-like multistage algorithm to remove the strongest target contributions. The effectiveness of the proposed strategy is first demonstrated against simulated data representing a typical scenario. Then, a preliminary experimental result is shown, obtained against data collected by the DVB-T based AULOS (R) passive radar developed by Leonardo S.p.A..

关键词： Passive Radar DVB-T signals drone detection long coherent integration time strong targets cancellation

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Theoretical Model of the signal Reflected from Rotating Blades of Aerial targets 22

Theoretical Model of the Signal Reflected from Rotating Blad...

引用

22nd International conference on Digital signal processing and its Applications, DSPA 2020

作者： Plotnitskaya, Ekaterina Veremyev, Vladimir Vorobev, Evgenii Research Institute 'Prognoz' Saint Petersburg Electrotechnical University 'LETI' Saint Petersburg Russia

ISBN: (纸本)9781728175287

The theoretical model representing the signal reflected from rotating blades of aerial objects with different rotor configurations is described. The proposed model is used concerning the radar operating in UHF-band for simulation of the reflected signals from the propeller-driven airplane, helicopter, and small multi-rotor drone. The simulation results are compared with the real data recorded by a passive radar utilizing signals of second-generation terrestrial digital video broadcasting standard for target illumination. © 2020 IEEE.

关键词： Drones

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PSO-RBF small Target Detection in Sea Clutter Background

PSO-RBF Small Target Detection in Sea Clutter Background

引用

7th International conference on Communications, signal processing, and Systems (CSPS)

作者： Li, ZhuXi Yin, ZhenDong Shi, Jia Harbing Inst Technol Sch Elect & Informat Engn Harbin 150006 Peoples R China

ISBN: (纸本)9789811365041;9789811365034

Target detection in the background of sea clutter is an important part of sea surface radar signal processing. The traditional detection of weak targets in sea clutter is based on the statistical characteristics of sea clutter, which does not reflect the intrinsic dynamics of sea clutter. Therefore, the detection results are not ideal. Based on the chaotic characteristics of sea clutter, this dissertation reconstructs the space structure of the sea clutter and proposes an improved particle swarm optimization (PSO) algorithm based on adaptive time-varying weights and local search operators. This method was applied to the optimization learning of the parameters of the radial basis function (RBF) neural network kernel function. The method was validated by using McIX University in Canada to measure the sea clutter data with the target in the Dartmouth area using IPIX radar. The results showed that the PSO-RBF algorithm in the background of chaotic sea clutter has good predictability. Compared with the general radial basis neural network, the improved algorithm not only has fast convergence speed but also has high error accuracy.

关键词： Particle swarm optimization (PSO) Radial basis function (RBF) Target detection Neural network Sea clutter

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Underwater small Target Recognition Based on Convolutional Neural Network

Underwater Small Target Recognition Based on Convolutional N...

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Global OCEANS Singapore - U.S. Gulf Coast conference

作者： Li, Sichun Jin, Xin Yao, Sibing Yang, Shuyu Harbin Engn Univ Acoust Sci & Technol Lab Harbin 150001 Peoples R China Harbin Engn Univ Key Lab Marine Informat Acquisit & Secur Minist Ind & Informat Technol Harbin 150001 Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Harbin 150001 Peoples R China

ISBN: (纸本)9781728154466

With the increasingly extensive use of diver and unmanned underwater vehicle in military, it has posed a serious threat to the security of the national coastal area. In order to prevent the underwater diver's impact on the safety of water area, it is of great significance to identify underwater small targets in time to make early warning for it In this paper, convolutional neural network is applied to underwater small target recognition. The recognition targets are diver, whale and dolphin. Due to the time-frequency spectrum can reflect the essential features of underwater target, convolutional neural network can learn a variety of features of the acoustic signal through the image processed by the time-frequency spectrum, time-frequency image is input to convolutional neural network to recognize the underwater small targets. According to the study of learning rate and pooling mode, the network parameters and structure suitable for underwater small target recognition in this paper are selected The results of data processing show that the method can identify underwater small targets accurately.

关键词： underwater small target recognition deep learning convolutional neural network diver dolphin whale

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Development of a Computer Simulator of the Visual N2 Event-Related Potential Component for the Study of Cognitive Processes 15th

Development of a Computer Simulator of the Visual N2 Event-R...

引用

15th Mediterranean conference on Medical and Biological Engineering and Computing (MEDICON)

作者： Marturano, Francesca Brigadoi, Sabrina Doro, Mattia Dell'Acqua, Roberto Sparacino, Giovanni Univ Padua I-35131 Padua PD Italy

ISBN: (纸本)9783030316358;9783030316341

The importance of visual N2 Event-Related Potential (ERP) component in the study of cognitive processes lies in its interpretation as a measure of the allocation of visual attention to possible targets. Unfortunately, the N2 component has a small amplitude and the domain of validity of methods used for its estimation is difficult to assess if real data are considered. Here, we develop a computer simulator of ERP measurements emulating the variability of the visual N2 component and use the synthetic data to evaluate the performance of four popular literature ERP estimation methods. Results confirmed that a solid simulation framework could allow identifying a reliable method to detect small-amplitude ERP components and quantifying its accuracy.

关键词： Visual N2 ERP modeling EEG signal processing Gaussian mixture models

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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...

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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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