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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191-200 订阅

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Improved adaptive co-pulse differential methods for nautical radar small target detection in sea clutter

Improved adaptive co-pulse differential methods for nautical...

引用

CIE International conference on Radar (RADAR)

作者： Bao, Zhonghua Xi, Zemin Lu, Jianbin Naval Univ Engn Elect Engn Coll Wuhan Peoples R China

ISBN: (纸本)9781509048281

Detection of small targets in sea clutter is an important issue for nautical radar signal processing. Firstly two improved adaptive co-pulse differential methods were presented in this paper, based on different modulation effects of radar antenna to small target and azimuth distributed sea clutter. Then the performance of the proposed methods was analyzed and the selection of optimum algorithm parameters was discussed. Considering that sea clutter's characteristic is random and time varying, the auto-relativity of sea clutter which was part of key algorithm parameters was estimated adaptively. Finally, field data acquired by a shore-based X-band non-coherent nautical radar was used to validate the proposed methods.

关键词： sea clutter small target detection co-pulse differential method adaptive processing

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DOMAIN ADAPTATION FOR SPEECH EMOTION RECOGNITION BY SHARING PRIORS BETWEEN RELATED SOURCE AND TARGET CLASSES

DOMAIN ADAPTATION FOR SPEECH EMOTION RECOGNITION BY SHARING ...

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IEEE International conference on Acoustics, Speech and signal processing

作者： Qirong Mao Wentao Xue Qiru Rao Feifei Zhang Yongzhao Zhan Department of Computer Science and Communication Engineering Jiangsu University China

ISBN: (纸本)9781479999897

In speech emotion recognition (SER), speech data is usually captured from different scenarios, which often leads to significant performance degradation due to the inherent mismatch between training and test set. To cope with this problem, we propose a domain adaptation method called Sharing Priors between Related Source and Target classes (SPRST) based on a two-layer neural network. The classifier parameters, namely the weights of the second layer, are imposed the common priors between the related classes, so that the classes with few labeled data in target domain can borrow knowledge from the related classes in source domain. The method is evaluated on the INTERSPEECH 2009 Emotion Challenge two-class task. Experimental results show that our approach significantly improves the performance when only a small number of target labeled instances are available.

关键词： Domain adaptation speech emotion recognition neural network priors Neural network emotion recognition Speech targets degradation of performance Classes Domain

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small and dim target detection by background modeling

Small and dim target detection by background modeling

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conference on signal and data processing of small targets

作者： Hu, Jing Yu, Yi Liu, Fan Huazhong Univ Sci & Technol Sch Automat Sci & Technol Multispectral Informat Proc Lab Wuhan 430074 Peoples R China

ISBN: (纸本)9781628417623

An effective method for small and dim moving target detection in complicated background is proposed. The proposed approach takes advantage of the Non-local means filter, and applies a novel weight calculation model based on circular mask to the original background estimation pattern. By associating similarity of grayscale distribution of the images with temporal information, the extended method realize the complicated background estimation and point target extraction successfully. To compare existing target detection methods and the proposed method, signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF) are employed for spatial performance comparison and receiver operating characteristics (ROC) is used for detection-performance comparison of the target trajectory. Experimental results demonstrate good performance of the proposed method in complicated scenes and low signal to noise ratio images.

关键词： small and dim target detection complicated background background modeling Non-Local means

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Ultrawideband Inverse Scattering Method for Resonance Region Target Recognition: Application to small-Scale Airplane targets with Measured data 9

Ultrawideband Inverse Scattering Method for Resonance Region...

引用

9th European conference on Antennas and Propagation EuCAP

作者： Secmen, Mustafa Yasar Univ Dept Elect & Elect Engn Izmir Turkey

ISBN: (纸本)9788890701856

This paper develops an inverse scattering method to determine the characteristics of resonance region targets by using ultrawideband scattering signals. The method mainly uses multiple time-domain signals for each target, and they are processed to give feature vectors in frequency domain. These vectors contain target-specific information related with characteristics (dimensions of substructures, material properties, etc.) of the known targets. The target recognition is employed by using test (unknown) target's scattering signal. This time-domain signal is processed in an inverse way to get a frequency-domain test vector. Then, the characteristics of test target are assigned as these of one of the known targets with a basic template-matching approach between test vector and feature vectors of these targets. The method is demonstrated for three realistic small-scale airplane. For an ultrawideband system of 0.7-18 GHz, more than 85 percent accuracy has been obtained even under moderate signal-to-noise (SNR) ratio of about 10 dB.

关键词： ultrawideband inverse scattering radar signal processing resonance region

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Detection of infrared dim and small targets based on saliency and grayscale morphological reconstruction 34

Detection of infrared dim and small targets based on salienc...

引用

第三十四届中国控制会议

作者： GU Zhangyuan YANG Shuo LI Jianxun School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467374439

Based on saliency and grayscale morphological reconstruction, a new detection algorithm for infrared dim and small targets is proposed in this paper. The saliency of the original image to obtain the region of interest(ROI) is analyzed, then the spatial domain characteristic of dim and small targets is introduced into the marker image. Grayscale morphological reconstruction is based on the marker image and the mask image(the original image). Because saliency efficiently concentrates the gradient difference of the targets, detection probability is improved also with little false alarms. As the role of recognition, spatial domain characteristic of target reduces false alarm probability with the same detection probability, and background can be well estimated by grayscale morphological reconstruction, after subtraction, dim and small targets are detected. Experiments of real data prove the better detection performance, especially higher signal-to-noise ratio(SNR).

关键词： Infrared dim and small targets,Saliency,Morphological reconstruction,Background estimation

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Compound-cognizant Feature Compression of Gas Chromatographic data to Facilitate Environmental Forensics

Compound-cognizant Feature Compression of Gas Chromatographi...

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data Compression conference (DCC)

作者： Damavandi, Hamidreza Ghasemi Sen Gupta, Ananya Reddy, Christopher Nelson, Robert Univ Iowa Iowa City IA USA Woods Hole Oceanog Inst Woods Hole MA 02543 USA

ISBN: (纸本)9781479984305

We present complementary compound-cognizant data engineering techniques for feature compression and data indexing across two-dimensional gas chromatographic (GC¡ÁGC) datasets with petroleum forensics as the primary application. We propose single-linkage clustering of dominant compounds (targets) along with local interpretation across biomarker peak profiles. Our methods enable high-volume data compression, along with robust querying and forensic distinction between similar sources. We validate our techniques against a diverse dataset of thirty-four crude oil injections collected from nineteen distinct sources across the planet, with emphasis on Macon do well, the source of Deepwater Horizon disaster (Gulf of Mexico, April 2010).

关键词： forensics cluster space-saving big data

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Through-the Wall Imaging (TWI) radar for detection, classification and identification of targets based on ReS

Through-the Wall Imaging (TWI) radar for detection, classifi...

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National conference on Recent Advances in Electronics & Computer Engineering (RAECE)

作者： Chauhan, Prashant Pratap Singh Singh, Dharmendra Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee 247667 Uttar Pradesh India

ISBN: (纸本)9781509021468

With growing need of Through wall imaging technology by both civil and military organization, accentuate the requirement of an effective system which is able to effectively detect the targets location, differentiate it as living or non living and classify it on the basis of how it is posing to the radar in the form of radar cross section. So far, classification of bigger targets was formulated for warship and planes but not for small targets with a very low RCS. The highly directive Horn antenna functioning on stepped frequency continuous wave (SFCW) signal exercises the concepts of Synthetic aperture radar (SAR) for better resolution and less of hardware requirement. A comprehensive research is produced comprising the concept of digital image processing and clutter removal techniques amalgamated with the upcoming modus operandi of classification of data using artificial neural network (ANN). A simple cost effective system is implemented using MATLAB as programming tool and the obtained results proves that the novel concept produces best results taking all the hurdles of the through-wall imaging in consideration.

关键词： Through the wall imaging A-Scan B-scan C-scan Independent Component Analysis (lCA) Artificial Neural Network (ANN) Thresholding Radar Cross Section (RCS) Synthetic Aperture Radar (SAR)

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Object-based detection of vehicles in airborne data 21

Object-based detection of vehicles in airborne data

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conference on Image and signal processing for Remote Sensing XXI

作者： Schilling, Hendrik Bulatov, Dimitri Middelmann, Wolfgang Fraunhofer Inst Optron Syst Technol & Image Explo D-76275 Ettlingen Germany

ISBN: (纸本)9781628418538

Robust detection of vehicles in airborne data is a challenging task since a high variation in the object signatures depending on data resolution - and often a small contrast between objects and background lead to high false classification rates and missed detections. Despite these facts, many applications require reliable results which can be obtained in a short time. In this paper, an object-based approach for vehicle detection in airborne laser scans (ALS) and photogrammetrically reconstructed 2.5D data is described. The focus of this paper lies on a robust object segmentation algorithm as well as the identification of features for a reliable separation between vehicles and background (all none-vehicle objects) on different scenes. The described method is based on three consecutive steps, namely, object segmentation, feature extraction and supervised classification. In the first step, the 2.5D data is segmented and possible targets are identified. The segmentation progress is based on the morphological top-hat filtering, which leaves areas that are smaller than a given filter size and higher (brighter) than their surroundings. The approach is chosen due to the low computational effort of this filter, which allows a fast computation even for large areas. The next step is feature extraction. Based on the initial segmentation, features for every identified object are extracted. In addition to frequently used features like height above ground, object area, or point distribution, more complex features like object planarity, entropy in the intensity image, and lineness measures are used. The last step contains classification of each object. For this purpose, a random forest classifier (RF) using the normalized features extracted in the previous step is chosen. RFs are suitable for high dimensional and nonlinear problems. In contrast to other approaches (e.g. maximum likelihood classifier), RFs achieves good results even with relatively small training samples.

关键词： 3D reconstruction classification detection vehicles

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DETECTION, PARAMETRIC IMAGING AND CLASSIFICATION OF VERY small MARINE targets EMERGED IN HEAVY SEA CLUTTER UTILIZING GPS-BASED FORWARD SCATTERING RADAR

DETECTION, PARAMETRIC IMAGING AND CLASSIFICATION OF VERY SMA...

引用

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

作者： Kabakchiev, C. Behar, V. Garvanov, I. Kabakchieva, D. Rohling, H. SU Sofia Bulgaria IICT BAS Sofia Bulgaria ULSIT Sofia Bulgaria UWNE Sofia Bulgaria TU HH Hamburg Germany

ISBN: (纸本)9781479928934

In this paper, we address a technique and related algorithms for precise detection, parametric imaging and classification of small marine targets in a harsh sensing environment attributed for heavy sea clutter via noncooperative processing of the GPS-based Forward Scatter Radar (FSR) data. In contrary to GPS L5 detection approach, the proposed technique utilizes civil GPS L1 signal formats in FSR exploiting GPS as a non-cooperative transmitter. In our previous studies it is shown that the use of the new power GPS signal L5, and the Forward Scattering effect providing a high SNR, at the detector input allows reliably to detect small air targets in conditions of the intense interference. In this paper we propose another approach, to enhance SNR, at the input of the detector in Forward Scattering Radar (FSR). The use of the effective filter (Local Variance Filter) for suppression of intensive sea clutter allows FSR reliably to detect small marine targets emerged in harsh sea clutter, but with GPS L1 signal, whose SNR is very small. At the classification level, the data mining approach is adopted, in which the target feature parameters are extracted from the preliminary filtered signals by utilizing the modified structure of a processor for target detection and parameter estimation in the time domain. Both, the decision tree-based and the neural network classifiers are featured and adapted for real-time implementation. The efficiency of the proposed technique is verified via analytical performance evaluations and experimental demonstrations.

关键词： FSR GPS detection classification harsh sensing environment

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Through-the wall imaging (TWI) radar for detection, classification and identification of targets based on RCS

Through-the wall imaging (TWI) radar for detection, classifi...

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National conference on Recent Advances in Electronics & Computer Engineering (RAECE)

作者： Prashant Pratap Singh Chauhan Dharmendra Singh Indian Institute of Technology Roorkee Roorkee Uttar Pradesh IN Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9781509021772

关键词： Artificial neural networks Sea measurements Directive antennas Horn antennas Image recognition Dielectrics

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