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small target of sea surface detection algorithm research based on feature classification

Small target of sea surface detection algorithm research bas...

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International conference on signal processing Proceedings (ICSP)

作者： Lianqing Yu Yun Li Ning Wang Jin Jiang Nanjing Research Institute of Electronics Technology Nanjing 210039 China

ISBN: (纸本)9781479921898

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： Abstracts Doppler effect Logic gates

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ISAR imaging with random missing observations based on non-iterative signal reconstruction algorithm

ISAR imaging with random missing observations based on non-i...

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International conference on signal processing Proceedings (ICSP)

作者： Yong Wang Jian Kang Runbin Zhang Research Institute of Electronic Engineering Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9781479921898

Random missing observations in real-world inverse synthetic aperture radar (ISAR) imaging may appear due to the instability of the radar system. Under this circumstance, the signal of one range bin over the slow-time has limited samples. Using traditional range-Doppler algorithm, high-quality ISAR images for random missing observations cannot be obtained. Recently, a new non-iterative algorithm based on the combined robust statistics and compressive sensing (CS) theory has been proposed to efficiently recover a complete signal from a small random set of samples, showing robustness in the presence of noise [16]. It is also important to emphasize that non-iterative method is computationally simpler than the iterative signal reconstruction solutions, and thus more amenable to practical applications. therefore, based on the non-iterative robust signal reconstruction method, a new algorithm for ISAR imaging with random missing observations is proposed in this paper. the efficiency of the proposed approach is demonstrated on the examples with simulated and real data.

关键词： Frequency measurement Abstracts Indexes Imaging Radar Analytical models

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Ground Target Tracking with RCS Estimation Utilizing Probability Hypothesis Density Filters

Ground Target Tracking with RCS Estimation Utilizing Probabi...

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16th International conference on Information Fusion (FUSION)

作者： Mertens, Michael Ulmke, Martin Fraunhofer FKIE Sensor Data & Informat Fus Wachtberg Germany

ISBN: (纸本)9786058631113

the knowledge on the radar cross section (RCS) of a ground target can support classification and identification tasks. In addition, it might also contribute to the resource management of the radar system because in general less energy needs to be emitted towards larger targets in order to obtain a detectable target return compared to small targets. the focus of this work, however, is to distinguish closely-spaced targets by first determining the mean RCS of the individual moving objects and then using this additional target attribute information to improve the track continuity in such a challenging environment. the RCS of a ground moving target can be estimated based on signal strength measurements. For this method to work, the RCS fluctuations are assumed to follow the analytically tractable Swerling-I and Swerling-III cases. the estimation scheme of the target RCS is incorporated into the Gaussian mixture variants of the probability hypothesis density (PHD) and cardinalized probability hypothesis density (CPHD) filters. the performance of these algorithms is analyzed based on a multi-target simulation scenario using a modified version of the optimal subpattern assignment (OSPA) metric that also accounts for labeling errors.

关键词： Target tracking ground moving target indication (GMTI) radar cross section (RCS) signal strength Gaussian mixture cardinalized probability hypothesis density (PHD) optimal subpattern assignment (OSPA) labeling error

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Comparing Multitarget Multisensor ML-PMHT with ML-PDA for VLO targets

Comparing Multitarget Multisensor ML-PMHT with ML-PDA for VL...

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16th International conference on Information Fusion (FUSION)

作者： Schoenecker, Steven Willett, Peter Bar-Shalom, Yaakov NUWC Div Newport Newport RI 02841 USA Univ Connecticut Dept Elect Engn Storrs CT 06269 USA

ISBN: (纸本)9786058631113

the Maximum Likelihood Probabilistic data Association (ML-PDA) tracker and the Maximum Likelihood Probabilistic Multi-Hypothesis (ML-PMHT) tracker are tested in their capacity as algorithms for very low observable targets (VLO, meaning 6 dB post-signal-processing or even less) and are then applied to five synthetic benchmark multistatic active sonar scenarios featuring multiple targets, multiple sources and multiple receivers. Both methods end up performing well in situations where there is a single target or widely-spaced targets. However, ML-PMHT has an inherent advantage over ML-PDA in that its likelihood ratio has a simple multitarget formulation, which allows it to be implemented as a true multitarget tracker. this formulation gives ML-PMHT superior performance for instances where multiple targets are closely spaced with similar motion dynamics.

关键词： ML-PDA ML-PMHT multitarget ML-PMHT maximum likelihood multistatic bistatic sonar tracking multitarget very low observable

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small object detection via fast discrete Curvelet transform

Small object detection via fast discrete Curvelet transform

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25th SPIE conference on signal and data processing of small targets

作者： Valdez, Hector A. Lu, thomas T. Chao, Tien-Hsin Calif State Univ Long Beach Long Beach CA 90840 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91125 USA

ISBN: (纸本)9780819497079

this paper describes the Fast Discrete Curvelet Transform (FDCT) which is integrated into an automated object detection framework capable of fast image processing for the purpose of tracking multiple objects of interest. the distinguishing difference between FDCT and other transforms is that the Curvelet coefficients have three beneficial parameters, which define scale, orientation, and spatial locations. We utilize these parameters to efficiently detect relatively small objects in a variety of image and video data sets. We have implemented the FDCT in a multi-stage automatic target recognition (ATR) architecture. the results of the current algorithm yield a highly sensitive detection of small targets in noisy backgrounds.

关键词： ATR Curvelet object detection ROI sparse representation targeting

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An Examination of the Application of Space Time Adaptive processing for the Detection of Maritime Surface targets from High Altitude Airborne Platforms

An Examination of the Application of Space Time Adaptive Pro...

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25th SPIE conference on signal and data processing of small targets

作者： McDonald, Michael Cerutti-Maori, Delphine Def R&D Canada 3701 Carling Ave Ottawa ON Canada Fraunhofer FHR D-53343 Wachtberg Germany

ISBN: (纸本)9780819497079

An examination of the application of Space Time Adaptive processing (STAP) techniques to real, multi-channel, medium grazing angle, radar sea clutter data is undertaken and the detection performance is quantified against simulated moving maritime surface targets. the application of sub-optimal STAP approaches to the maritime radar detection problem is shown to be complicated by non-stationarity of sea clutter and rapid variations of the sea clutter spectrum due to transient wave activity. Observed performance gains from maritime STAP are much more limited than those observed for Ground Moving Target Indication (GMTI) due to the inherent spectral width of sea clutter and the slow Doppler velocities of maritime targets. three sub-optimal STAP processing architectures are examined and PRI-Staggered Post-Doppler is shown to provide consistently superior detection performance for the data set in question.

关键词： Maritime surveillance STAP Radar GMTI

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Interpolated root-Music for transversal automotive radar traffic monitoring

Interpolated root-Music for transversal automotive radar tra...

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2013 10th European Radar conference, EuRAD 2013 - Held as Part of 16th European Microwave Week, EuMW 2013

作者： Askeland, Stein Arne Ekman, Torbjorn Norwegian University of Science and Technology Dept. of Electronics and Telecommunications 7491 Trondheim Norway

ISBN: (纸本)9782874870330

One of the key requirements for future automotive radar safety systems is to have a wide field of view along with high angular resolution and accuracy. the combination of a conformal array and a root-MUSIC algorithm can provide these properties but root-MUSIC requires an uniform linear array (ULA) structure and high SNR in order to work. In this paper the ULA requirement is handled by transforming the real array data into data from a set of virtual linear arrays by a linear interpolation procedure. High SNR is achieved by first detecting the targets and then use the Fourier transformed signal at the target Doppler frequencies for the angular estimation problem. the system performance is analysed by implementing an advanced simulation model with the radar targets moving in a transversal trajectory testing the system at the extreme angles near ±90°. the tracking performance is finally evaluated by implementing an extended Kalman filter. the results show that by due to the high SNR the angular estimation works properly even though we have a small number of signal snapshots, also target ambiguities are resolved and the estimated trajectories are accurate enough to find which lanes the targets are moving in. © 2013 EMA.

关键词： Multiple signal classification

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Contact Clustering and Fusion for Preprocessing Multistatic Active Sonar data

Contact Clustering and Fusion for Preprocessing Multistatic ...

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16th International conference on Information Fusion (FUSION)

作者： Hanusa, Evan Krout, David W. Gupta, Maya R. Univ Washington Appl Phys Lab Seattle WA 98105 USA Univ Washington Dept Elect Engn Seattle WA 98105 USA

ISBN: (纸本)9786058631113

this paper presents results of a clustering-based preprocessing step for multistatic tracking, evaluated on the PACsim dataset, a simulated multistatic active sonar dataset. the clustering step uses a flexible likelihood-based similarity calculation which allows for the incorporation of any available features. In this work, we present results using target strength (estimated from signal-to-noise ratio) and Doppler measurements. Results show that this approach performs well on dim targets in high clutter environments.

关键词： signal to noise ratio

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Identification of Human Motion Signature using Airborne Radar data

Identification of Human Motion Signature using Airborne Rada...

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25th SPIE conference on signal and data processing of small targets

作者： McDonald, Michael Damini, Anthony Def R&D Canada Ottawa ON Canada

ISBN: (纸本)9780819497079

data containing the radar signature of amoving person on the ground were collected at ranges of up to 30 km from a moving airborne platform using the DRDC Ottawa X-band Wideband Experimental Airborne Radar (XWEAR). the human target radar echo returns were found to possess a characteristic amplitude modulated (AM) and frequency modulated (FM) signature which could be usefully characterized in terms of conventional AM and FM modulation parameters. Human detection performance after space time adaptive processing is frequently limited by false alarms arising from incomplete cancellation of large radar cross-section discretes during the whitening step. However, the clutter discretes possess different modulation characteristics from the human targets discussed above. the ability of pattern classification techniques to use this parameter measurement space to distinguish between human targets and clutter discretes is explored and preliminary results presented.

关键词： STAP Radar GMTI

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Detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets

Detection of unusual trajectories using multi-objective evol...

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25th SPIE conference on signal and data processing of small targets

作者： Smolinski, Tomasz G. Newell, Trevor McDaniel, Samantha Pokrajac, David Delaware State Univ Dept Comp & Informat Sci Dover DE 19901 USA

ISBN: (纸本)9780819497079

Detection of unusual trajectories of moving objects (e.g., people, automobiles, etc.) is an important problem in many civilian and military surveillance applications. In this work, we propose a multi-objective evolutionary algorithms and rough sets-based approach that breaks down 2-dimensional trajectories into a set of additive components, which then can be used to build a classifier capable of recognizing typical, but yet unseen trajectories, and identifying those that seem suspicious.

关键词： Detection of Unusual Trajectories data Mining Evolutionary Algorithms Rough Sets

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