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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201-210 订阅

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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...

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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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INFORMATION EXTRACTION FROM VERY HIGH RESOLUTION SATELLITE SAR data TIME SERIES USING GRAPH BASED CONNECTED FEATURES

INFORMATION EXTRACTION FROM VERY HIGH RESOLUTION SATELLITE S...

引用

IEEE International conference on Image processing (ICIP)

作者： Gleich, Dusan Univ Maribor Fac Elect Engn & Comp Sci Smetanova 17 SLO-2000 Maribor Slovenia

ISBN: (纸本)9781479983391

This paper presents information extraction from very high resolution SAR data. small patches of SAR data were used for graph based regularized sparse coding. The auto regressive model was used to extract sparse graphs from the data. The sparse graph features were used for dictionary learning using Graph-regularized Non-negative Matric Factorization. The algorithm was used categorization of database consisted of 20 classes and each class contained 100 patches. The algorithm was not superior to the supervised categorization using spectral features.

关键词： Synthetic Aperture Radar Feature extraction Compresses sensing

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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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CMOS Image sensor characterization experimental setup

CMOS Image sensor characterization experimental setup

引用

conference on Education and Training in Optics and Photonics (ETOP)

作者： Avignon, Thierry Perrin, Baptiste Augereau, Jean Moreau, Julien Jacubowiez, Lionel Inst Opt Grad Sch Lab Enseignement Expt LEnsE F-91127 Palaiseau France

ISBN: (纸本)9781510600287

We demonstrate a very simple experimental setup that allows our students to study and fully characterize an industrial CMOS Image Sensor. In 2014, 95% of produced cameras are CMOS image sensors (Complementary Metal Oxyd Semiconductor) and only 5% are still CCD sensors (Charge-Coupled Device). The main difference between CMOS and CCD is that each CMOS sensor pixel has its own readout circuit (voltage-photoelectron conversion and amplification) directly adjacent to the photosensitive area. CMOS image sensors are not only cheaper, because simpler to manufacture, they have a lower power consumption than CCD sensors. They also allow image processing at the pixel level (zones of interest (ROI), Binning, filtering, etc ...). However, compare to CCD sensor, CMOS sensors often demonstrate a lower dynamic, a larger read-out noise and a larger non uniformity of the spatial response. In overall, it is very important to understand every characteristic of an image sensor and be able to measure it in a simple way. Our system consist in a small integrating sphere illuminated by a white LED, a standard calibrated photodiode (or a light power meter) and a small monochromator (or several colored LED). Control of the camera parameters, Image acquisition and data processing are achieved with a single Matlab homemade software.

关键词： CCD and CMOS image sensors characterization dark signal readout noise photon noise pattern response non-uniformity quantization binning ROI

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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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Sparse Reconstruction Of Equivalence Classes Of Moving targets Using Single-Channel Synthetic Aperture Radar

Sparse Reconstruction Of Equivalence Classes Of Moving Targe...

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

作者： Gunther, Jake Hunsaker, Josh Anderson, Hyrum Moon, Todd Utah State Univ Elect & Comp Engn Logan UT 84322 USA Sandia Natl Labs Data Anal & Data Exploitat Albuquerque NM 87185 USA

ISBN: (纸本)9781479928934

Simultaneously estimating position and velocity of moving targets using only phase information from single-channel SAR data is impossible. This paper defines classes of equivalent target motion and solves the GMTI problem up to membership in an equivalence class using single-channel SAR phase data. We present a definitions for endo- and exo-clutter that is consistent with the equivalence classes, and show that most target motion can be detected, i.e. the set of endo-clutter targets is very small. We exploit the sparsity of moving targets in the scene to develop an algorithm to resolve target motion up to membership in an equivalence class, and demonstrate the effectiveness of the proposed technique using simulated data.

关键词： Radar signal processing synthetic aperture radar motion detection sparse signal recovery

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Detection of small targets and their Characterization Based on their Formation using an Image Feature Network-Based Object Recognition Algorithm

Detection of Small Targets and their Characterization Based ...

引用

conference on signal and data processing of small targets

作者： Straub, Jeremy Univ N Dakota Dept Comp Sci Grand Forks ND 58202 USA

ISBN: (纸本)9781628410297

small target detection and classification is problematic. For targets that operate as part of a cluster, classification can be performed based on the characteristics of the cluster's operations, instead of trying to identify an individual cluster-member directly. This paper presents an algorithm for object identification based on comparing networks of point-to-point distances between features identified by an image feature detection algorithm. It discusses the alterations required to make the algorithm suitable for performing cluster-formation based characterization of small targets from point or near-point source data. An analysis of the algorithm's performance is presented and it efficacy for this application assessed.

关键词： small target detection and classification point targets classification decision cluster object identification cluster-formation based characterization of small targets

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small TARGET OF SEA SURFACE DETECTION ALGORITHM RESEARCH BASED ON FEATURE CLASSIFICATION 12

SMALL TARGET OF SEA SURFACE DETECTION ALGORITHM RESEARCH BAS...

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12th IEEE International conference on signal processing (ICSP)

作者： Yu, Lianqing Li, Yun Wang, Ning Jiang, Jin Nanjing Res Inst Elect Technol Nanjing 210039 Jiangsu Peoples R China

ISBN: (纸本)9781479921867

This paper focuses on small target of sea surface detection in sea clutter. Firstly, it analyses the statistic of sea clutter. Secondly, three feature vectors (namely, RAA, RPH and RVE) are extracted, which is utilized to discriminate the sea clutter and targets, and then the time-Doppler curve is used to distinguish the multi-targets in order to finish the small target of sea surface detection. Finally, it analyses the trial data of some radar and shows that this algorithm can effectively distinguish the sea clutter and small target of sea surface. This paper provides a new approach for the ship-borne surveillance radar or marine radar to detect the small target of sea surface and antisubmarine warfare patrol.

关键词： Sea Cluttert Target Detection Feature Detection Feature Classification Time-Doppler Curve

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