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检索条件"任意字段=13th Conference on Signal and Data Processing of Small Targets"

共 447 条记录，以下是141-150 订阅

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A multiple model SNR/RCS likelihood ratio score for radar-based feature-aided tracking

A multiple model SNR/RCS likelihood ratio score for radar-ba...

引用

17th conference on signal and data processing of small targets

作者： Slocumb, BJ Klusman, ME Numerica Corp Ft Collins CO 80527 USA

ISBN: (纸本)0819459186

Most approaches to data association in target tracking use a likelihood-ratio based score for measurement-to-track and track-to-track matching. the classical approach uses a likelihood ratio based on kinematic data. Feature-aided tracking uses non-kinematic data to produce an "auxiliary score" that augments the kinematic score. this paper develops a non-kinematic likelihood ratio score based on statistical models for the signal-to-noise (SNR) and radar cross section (RCS) for use in narrowband radar tracking. the formulation requires an estimate of the target mean RCS, and a key challenge is the trackina of the mean RCS through significant "jumps" due to aspect dependencies. A novel multiple model approach is used track through the RCS jumps. three solution are developed: one based on an a-filter, a second based on the median filter, and the third based on an IMM filter with a median pre-filter. Simulation results are presented that show the effectiveness of the multiple model approach for tracking through RCS transitions due to aspect-angle changes.

关键词： feature-aided tracking RCS tracking multiple model signal score swerling fluctuation

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Track fusion with feedback for local trackers using MHT

Track fusion with feedback for local trackers using MHT

引用

20th conference on signal and data processing of small targets

作者： Danu, Daniel Lang, Tom Kirubarajan, T. McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Gen Dynam Canada Ottawa ON Canada

ISBN: (纸本)9780819471604

With current processing power, Multiple Hypothesis Tracking (MHT) becomes a feasible and powerful solution;however a good hypothesis pruning method is mandatory for efficient implementation. the availability of a continuously increasing number of tracking systems raises interest in combining information from these systems. the purpose of this paper is to propose a method of information fusion for such trackers that use MHT locally with local information sent in the form of sensor global hypotheses and the fusion center combining them into fused global hypotheses. the information extracted from the best fused global hypotheses, in the form of ranking of received sensor global hypotheses, is sent back to local trackers, for optimized pruning. Details of the method, in terms of sensor global hypotheses generation, evaluation, pruning at local sensors, association and fusion of sensor global hypotheses at fusion center, and usage of the information received as feedback from the fusion center are presented.

关键词： target tracking Multiple Hypothesis Tracking data fusion

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On Covariance Structure in Noisy, Big data

On Covariance Structure in Noisy, Big Data

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

作者： Paffenroth, Randy C. Nong, Ryan Du Toit, Philip C. Numerica Corp Loveland CO 80538 USA

ISBN: (纸本)9780819497079

Herein we describe theory and algorithms for detecting covariance structures in large, noisy data sets. Our work uses ideas from matrix completion and robust principal component analysis to detect the presence of low-rank covariance matrices, even when the data is noisy, distorted by large corruptions, and only partially observed. In fact, the ability to handle partial observations combined with ideas from randomized algorithms for matrix decomposition enables us to produce asymptotically fast algorithms. Herein we will provide numerical demonstrations of the methods and their convergence properties. While such methods have applicability to many problems, including mathematical finance, crime analysis, and other large- scale sensor fusion problems, our inspiration arises from applying these methods in the context of cyber network intrusion detection.

关键词： Sensors Matrices Computer networks Algorithms Computer intrusion detection Optimization (mathematics) Principal component analysis Sensor fusion data fusion Veins

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data Hiding for Text Document in PDF File 13th

Data Hiding for Text Document in PDF File

引用

13th International conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP)

作者： Kuribayashi, Minoru Fukushima, Takuya Funabiki, Nobuo Grad Sch Nat Sci & Technol Kita Ku 3-1-1 Tsushima Naka Okayama 7008530 Japan

ISBN: (纸本)9783319638560;9783319638553

Among data hiding methods for documents, the spaces between words and paragraphs are popular features for embedding information. However, in order to avoid the visible distortions, the embedding capacity is limited to be small in conventional methods. In this paper, we regard a collection of space lengths as one dimensional feature vector and apply signal processing approaches for the vector to embed more information with less distortions. We also focus on the Portable Document Format (PDF) files and propose a new data hiding method for PDF files with large capacity. Considering the secrecy of the embedded information, a random permutation and dither modulation are introduced in the operation.

关键词： Portable Document Format QIM Dither modulation Permutation

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Approximate Calculation of Marginal Association Probabilities using a Hybrid data Association Model

Approximate Calculation of Marginal Association Probabilitie...

引用

conference on signal and data processing of small targets

作者： Baum, Marcus Willett, Peter Bar-Shalom, Yaakov Hanebeck, Uwe D. Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA Karlsruhe Inst Technol Intelligent Sensor Actuator Syst Lab ISAS Karlsruhe Germany

ISBN: (纸本)9781628410297

the calculation of marginal association probabilities is the major computational bottleneck in the Joint Probabilistic data Association Filter (JPDAF). In this paper, we investigate approximations for the marginal associations that simplify the (computational complex) original association model in order to obtain efficient algorithms. In this context, we first discuss the Bakhtiar-Alavi algorithm and the Linear Multitarget Integrated Probabilistic data Association (LMIPDA) algorithm. Second, we propose a fast novel approximation that exploits systematic combinations of the JPDAF measurement model with the Probabilistic Multi-Hypothesis Tracker (PMHT) measurement model. the discussed methods are evaluated by means of a tracking scenario with a high number of closely-spaced targets.

关键词： Joint Probabilistic data Association Filter (JPDAF) marginal association probabilities Probabilistic Multi-Hypothesis Tracker (PMHT)

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Expected likelihood for tracking in clutter with particle filters

Expected likelihood for tracking in clutter with particle fi...

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14th conference on signal and data processing of small targets

作者： Marrs, A Maskell, S Bar-Shalom, Y QinetiQ Ltd Malvern Technol Ctr Malvern Worcs England

ISBN: (纸本)0819444782

the standard approach to tracking a single target in clutter, using the Kalman filter or extended Kalman filter, is to gate the measurements using the predicted measurement covariance and then to update the predicted state using probabilistic data association. When tracking with a particle filter, an analog to the predicted measurement covariance is not directly available and could only be constructed as an approximation to the current particle cloud. A common alternative is to use a form of soft gating, based upon a Student's-t likelihood, that is motivated by the concept of score functions in classical statistical hypothesis testing. In this paper, we combine the score function and probabilistic data association approaches to develop a new method for tracking in clutter using a particle filter. this is done by deriving an expected likelihood from known measurement and clutter statistics. the performance of this new approach is assessed on a series of bearings-only tracking scenarios with uncertain sensor location and non-Gaussian clutter.

关键词： tracking probabilistic data association particle filters

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Hazardous material localization and person tracking

Hazardous material localization and person tracking

引用

20th conference on signal and data processing of small targets

作者： Wieneke, Monika Safenreiter, Konstantin Koch, Wolfgang FGAN FKIE D-53343 Wachtberg Germany

ISBN: (纸本)9780819471604

Timely recognition of threats can be significantly supported by security assistance systems that work continuously in time and call the attention of the security personnel in case of anomalies. We describe the concept and the realization of an indoor security assistance system for real-time decision support. data for the classification of persons are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from laser-range-scanners. Both, tracking and fusion of tracks with chemical attributes can be processed within one single framework called Probabilistic Multiple Hypothesis Tracking (PMHT). An extension of PMHT for dealing with classification measurements (PMHT-c) already exists. We show how PMHT-c can be applied to associate chemical attributes to person tracks. this affords the localization of threads and a timely notification of the security personnel.

关键词： person tracking Probabilistic Multi-Hypothesis Tracking (PMHT) classification attributes data fusion security assistance systems

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Feasibility Study on Service-based data Acquisition for Human signal Molecule Profiling database

Feasibility Study on Service-based Data Acquisition for Huma...

引用

IEEE 13th International conference on Bioinformatics and Bioengineering (BIBE)

作者： Zhao, Xinyan Dong, Tao Vestfold Univ Coll Dept Micro Nano Syst Technol IMST TekMar Borre Norway

ISBN: (纸本)9781479931637

A service-based model of human signal-molecule-profiling database (HSMPD) was proposed to prompt data acquisition of medical information from blood tests in traditional healthcare systems and to make the data be naturally converted into Health IT (HIT) services through the translational bioinformatics (TBI) community. A self-motivated mechanism to minimize the project investment was designed. the low-cost tool, called 'SMP chip', will be employed in blood tests inside hospitals, which results could be decoded and stored in HSMPD automatically. Successively, HIT services derived from HSMPD will provide a stable income to support the data acquisition processes. To explore the feasibility of the model, a small-scale survey study was performed in a common hospital. the positive results indicated that the cooperation of IT, engineering, healthcare systems and TBI community could provide a self-motivated solution to build the HSMPD platform.

关键词： bioinformatics data acquisition database management systems health care hospitals

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An MDL approach for multiple low observable track initiation

An MDL approach for multiple low observable track initiation

引用

14th conference on signal and data processing of small targets

作者： Chen, H Kirubarajan, T Bar-Shalom, Y Pattipati, KR Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)0819444782

In this paper the track initiation problem is formulated as multiple composite hypothesis testing using maximum likelihood estimation with probabilistic data association (ML-PDA), an algorithm known to work under very low SNR conditions. this algorithm does not have to make a decision as to which measurement is target originated. the hypothesis testing is based on the minimum description length (MDL) criterion. We first review some well-known approaches for statistical model selection and the advantage of the MDL criterion. then we present an approximate penalty in accounting for the model complexity to simplify the calculation of MDL. Finally, we apply the. MDL approach for the detection and initiation of tracks of incoming tactical ballistic missiles in the exo-atmospheric phase using a surface based electronically scanned array (ESA) radar. the targets are characterized by low SNR, which leads to low detection probability and high false alarm rate. the target acquisition problem is formulated using a batch of radar scans to detect the presence of up to two targets. the ML-PDA estimator is used to initiate the tracks assuming the target trajectories follow a deterministic state propagation. the approximate MDL criterion is used to determine the number of valid tracks in a surveillance region. the detector and estimator are shown to be effective even at 4.4 dB average SNR in a resolution cell (at the end of the signal processing chain).

关键词： track initiation minimum description length maximum likelihood estimator probabilistic data association model selection

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Performance evaluation of time-weighted backvalues least squares vs. NOGA track estimators via sensor data fusion and track fusion for small target detection applications - art. no. 59130Z

Performance evaluation of time-weighted backvalues least squ...

引用

17th conference on signal and data processing of small targets

作者： Imam, N Barhen, J Glover, CW Oak Ridge Natl Lab Comp Sci & Math Div Ctr Engn Sci Adv Res Oak Ridge TN 37831 USA

ISBN: (纸本)0819459186

the rapid growth and increasing sophistication of airborne surveillance technology have spurred intense research efforts in the development and implementation of tracking algorithms capable of processing a large number of targets using multi-sensor data. In this paper, a, novel tracking algorithm, the NOGA tracker, is presented and compared with the more conventional Time-Weighted Backvalues Least Squares (TWBLS) estimator for accuracy (numerical and phenomenological), ease of implementation, and time performance. the NOGA tracker combines model predictions and sensor measurements to produce best estimates for quantities of interest. the state estimator model used for NOGA is a simple second order auto-regression which is combined with an uncertainty reduction scheme involving nonlinear Lagrange optimization process in which the inverse of a global covariance matrix is used as the natural metric for the Bayesian inference that underlies the combining process. the NOGA tracker explicitly incorporates sensor and model uncertainties in the estimation process, and uses model sensitivities to propagate the associated covariance matrices accurately and in a systematic way.

关键词： Uncertainty reduction track fusion sensor fusion multi-sensor tracking auto-regressive

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