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检索条件"任意字段=18th Conference on Signal and Data Processing of Small Targets"

共 390 条记录，以下是251-260 订阅

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Exploiting target amplitude information to improve multi-target tracking - art. no. 623618

Exploiting target amplitude information to improve multi-tar...

引用

18th conference on signal and data processing of small targets

作者： Ehrman, Lisa M. Blair, W. Dale Georgia Inst Technol Georgia Tech Res Inst Sensors & Electromagnet Applicat Lab Atlanta GA 30332 USA

ISBN: (纸本)0819462926

Closely-spaced (but resolved) targets pose a challenge for measurement-to-track data association algorithms. Since the Mahalanobis distances between measurements collected on closely-spaced targets and tracks are similar, several elements of the corresponding kinematic measurement-to-track cost matrix are also similar. Lacking any other information on which to base assignments, it is not surprising that data association algorithms make mistakes. One ad hoc approach for mitigating this problem is to multiply the kinematic measurement-to-track likelihoods by amplitude likelihoods. However, this can actually be detrimental to the measurement-to-track association process. With that in mind, this paper pursues a rigorous treatment of the hypothesis probabilities for kinematic measurements and features. three simple scenarios are used to demonstrate the impact of basing data association decisions on these hypothesis probabilities for Rayleigh, fixed-amplitude, and Rician targets. the first scenario assumes that the tracker carries two tracks but only one measurement is collected. this provides insight into more complex scenarios in which there are fewer measurements than tracks. the second scenario includes two measurements and one track. this extends naturally to the case with more measurements than tracks. Two measurements and two tracks are present in the third scenario, which provides insight into the performance of this method when the number of measurements equals the number of tracks. In all cases, basing data association decisions on the hypothesis probabilities leads to good results.

关键词： signal to noise ratio Kinematics Mahalanobis distance Distance measurement Detection and tracking algorithms Radar Target detection Time metrology Antennas Bessel functions

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Nonlinear estimation techniques for impact point prediction of ballistic targets - art. no. 62360C

Nonlinear estimation techniques for impact point prediction ...

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18th conference on signal and data processing of small targets

作者： Hardiman, David F. Kerce, J. Clayton Brown, George C. USA Redstone Arsenal AL 35898 USA

ISBN: (纸本)0819462926

this paper considers three nonlinear estimation algorithms for impact point prediction (IPP) of ballistic targets. the paper assumes measurements are available from a 3D surveillance radar or phased array radar over some portion of the ballistic trajectory. the ballistic target (BT) is tracked using an extended Kalman filter (EKF), an unscented Kalman filter (UKF), and a particle filter (PF). With the track estimate as an initial condition, the equations of motion for the BT are integrated to obtain a prediction of the impact point. this paper compares the performance of the EKF, UKF, and a particular choice of PF for impact point prediction. the traditional Extended Kalman Filter equations are based on a first-order Taylor, series approximation of the nonlinear transformations (expanded about the latest state estimate). Both the Unscented Kalman Filter and the Particle Filter allow nonlinear systems to be modeled without prior linearizion. the primary focus of the analysis presented in this paper is comparing the performance and accuracy of the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the chosen Particle Filter implementation for impact point prediction. the three filtering techniques are compared to the theoretical Cramer-Rao lower bounds (CRLB) of estimation error.

关键词： estimation non-linear dynamic system Cramer-Rao lower bound (CRLB) Kalman filter

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Track management in a multisensor MHT for targets with aspect-dependent SNR - art. no. 623613

Track management in a multisensor MHT for targets with aspec...

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18th conference on signal and data processing of small targets

作者： Blanding, Wayne R. Willett, Peter K. Bar-Shalom, Yaakov Coraluppi, Stefano Univ Connecticut Dept ECE Storrs CT 06269 USA

ISBN: (纸本)0819462926

In many active sonar tracking applications, targets frequently undergo fading detection performance in which the target's detection probability can shift suddenly from a high to a low value. this characteristic is a function of the undersea environment. Using a multistatic active sonar problem, we examine the performance of track management (initiation and termination) routines where the target detection probability is based on an underlying Hidden Markov Model (HMM) with high and low detection states. Using a likelihood ratio test, we develop the optimum track initiation performance as measured by a System Operating Characteristic, similar to a Receiver Operating Characteristic, which plots the probability of initiating a true track versus the probability of initiating a false track. We show that near-optimal performance can be attained using track initiation logic that differentiates the measurements as to receiver source in an "M detections of N scans from C sensors" type of rule. Performance can further be improved by using a composite track initiation test that combines two or three such rules in a logical OR operation. We next show that the use of a Shiryaev-Roberts test for track termination yields the quickest detection of false tracks for a given duration of true target tracks when compared to a Page test and rule-based tests of the form "M or fewer detections from K scans".

关键词： target tracking multistatic sonar track initiation

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3D and infrared face reconstruction from RGB data using Canonical Correlation Analysis

3D and infrared face reconstruction from RGB data using Cano...

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18th International conference on Pattern Recognition (ICPR 2006)

作者： Reiter, Michael Donner, Rene Langs, Georg Bischof, Horst Vienna Univ Technol Pattern Recognit & Image Proc Grp A-1060 Vienna Austria Graz Univ Inst Comp Graph & Vision A-8010 Graz Austria

ISBN: (纸本)0769525210

In this paper, we apply a multiple regression method based on Canonical Correlation Analysis (CCA) to face data modelling. CCA is a factor analysis method which exploits the correlation between two high dimensional signals. We first use CCA to perform 3D face reconstruction and in a separate application we predict near-infrared (NIR) face texture. In both cases, the input data are color (RGB) face images. Experiments show, that due to the correlation between input and output signal, only a small number of canonical factors are needed to describe the functional relation of RGB images to the respective output (NIR images and 3D depth maps) with reasonable accuracy.

关键词： Image recognition

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Nonparametric complex background prediction algorithm using FCM clustering for dim point infrared targets detection

Nonparametric complex background prediction algorithm using ...

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4th International conference on Communications, Circuits and Systems (ICCCAS 2006)

作者： Wu, Honggang Li, Xiaofeng Chen, Yuebin Li, Zaiming Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 610054 Peoples R China

ISBN: (纸本)0780395840

A nonparametric background prediction algorithm using fuzzy c-means (FCM) clustering is proposed to enhance the detection of dim small infrared targets. in image data. the target of interest is assumed to have a very small spatial spread, and is obscured by heavy background clutter. the input image data is firstly segmented using FCM clustering, and then the nonparametric regressive method is applied to predict background in each cluster respectively. Subsequently the background is subtracted from the input data, leaving components of the target signal in the residual noise. Experiment results show better detecting performance for the output data by the algorithm of this paper than by other traditional methods.

关键词： Image analysis

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PetaOp/second FPGA signal processing for SETI and radio astronomy

PetaOp/second FPGA signal processing for SETI and radio astr...

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40th Asilomar conference on signals, Systems, and Computers, ACSSC '06

作者： Parsons, Aaron Backer, Donald Chang, Chen Chapman, Daniel Chen, Henry Crescini, Patrick De Jesus, Christina Dick, Chris Droz, Pierre MacMahon, David Meder, Kirsten Mock, Jeff Nagpal, Vinayak Nikolic, Borivoje Parsa, Arash Richards, Brian Siemion, Andrew Wawrzynek, John Werthimer, Dan Wright, Melvyn University of California Berkeley Berkeley CA 94720 United States Xilinx Corporation

ISBN: (纸本)1424407850

Our group, the Center for Astronomy signal processing and Electronics Research (CASPER), seeks to speed the development of radio astronomy signal processing instrumentation by designing and demonstrating a scalable, upgradeable, FPGA-based computing platform and software design methodology that targets a range of real-time radio telescope signal processing applications. this project relies on a small number of modular, connectible hardware components and open-source signal processing libraries which can be reused and scaled as hardware capabilities expand. We have demonstrated the use of 10 Gb Ethernet packetization and switches to manage high-bandwidth inter-board communication. Using these tools, we have built spectrometers, correlators, beamformers, VLBI data recorders, and many other applications. Future directions for the development include a fully packetized scalable correlator, additional library and toolflow development, and a next generation of modular FPGA-based hardware.

关键词： Radio astronomy

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Neural network for polarimetric radar target classification

Neural network for polarimetric radar target classification

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14th European signal processing conference, EUSIPCO 2006

作者： Soleti, R. Cantini, L. Berizzi, F. Capria, A. Calugi, D. Dept. of Information Engineering University of Pisa Via Caruso 16 56122 Pisa Italy Italy

In this paper, the Artificial Neural Network (ANN) paradigm is applied to radar target classification. Radar returns are simulated via an e.m code and time-domain polarimetric target features are extracted by means of Prony's algorithm. Two different type of feedforward neural network has been adopted in order to classify the target echo, namely the Multi Layer Perceptron (MLP) and the Self Organizing Maps (SOM). the above-mentioned network have been tested on two type of simulated targets: a small tonnage ship with a low level of detail and medium tonnage ship with higher details. Each network has been trained on a wide range of signal-to-noise ratio, and with different data records number in order to assess the training invariant properties of each network. Finally, in the validation phase a fixed number of records has been considered to evaluate networks performances, which are given in terms of classification error.

关键词： signal to noise ratio

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A slope-based technique for motion estimation and optimum time selection for ISAR imaging of ship targets

A slope-based technique for motion estimation and optimum ti...

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14th European signal processing conference, EUSIPCO 2006

作者： Pastina, Debora Spina, Chiara Aprile, Angelo INFOCOM Dpt. University of Rome la Sapienza Via Eudossiana 18 00184 Rome Italy Italy Selex Airborne and Sensor Systems Galileo Avionica Spa Via G.B. Grassi 93 20157 Milan Italy

the focus of this paper is on optimum time selection and angular motion estimation for ship ISAR imaging. the aim is to select proper imaging intervals and to estimate ship angular motion in order to obtain high quality top-view or side-view ship images suitable for processing by classifica-tion/identification procedures. To this purpose a slope-based ISAR algorithm is proposed, able to estimate the time instants better suited for top or side-view image formation and the rotation motion vertical/horizontal components for image scaling. the performance of the proposed ISAR tech-nique is investigated against simulated data under different ship model, ship motion, acquisition geometry and back-ground conditions. Results obtained by applying the pro-posed technique to live ISAR data proves the effectiveness of the proposed approach.

关键词： Motion estimation

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Study on theory of multi-target tracking and data association algorithms in phased array radar

Study on theory of multi-target tracking and data associatio...

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6th International conference on ITS Telecommunications Proceedings

作者： Wang, Lan-yun Zhao, Yong-jun Wang, Wei-xiang Inst Informat Sci & Technol Zhengzhou 1001 Mail Box835 Zhengzhou 450002 Henan Peoples R China

ISBN: (纸本)0780395867

this paper firstly introduced the theory of target tracking and studied the process of data association for multi-target tracking of multifunctional phased array radar. then, a new approach is proposed to deal with the uncertainty of measurement origin and the disadvantage of probabilistic data association filter(PDAF)and joint probabilistic data association filter(JPDAF), and the method directly gets posterior probability by less computational load the modified method mainly analyzed the influence of common measurements in different gates, and neither like that probabilistic data association(PDA) is to use all of the validated measurement with different weights, nor like that the JPDA algorithm is used to track multiple targets by evaluating the measurement-to-track association probabilities and combining them to find the state estimation, Monte-Carlo simulation results show that the algorithm has a small computation and a better real-time tracking performance, so it is applicable to the data processing of phased array radar.

关键词： Target tracking

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Algorithm for Detection with Localization of Multi-targets in Wireless Acoustic Sensor Networks

Algorithm for Detection with Localization of Multi-targets i...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Jaechan Lim Jinseok Lee Sangjin Hong Peom Park Department of Electrical and Computer Engineering Stony Brook University NY USA Department of Industrial and Information Engineering Ajou University/Humintec Company Limited Suwon South Korea

In most multitarget tracking approaches based on joint probabilistic data association (JPDA), it is difficult to apply the solutions to problems (due to the dimensionality curse of heavy complexity) where the number of target varies dramatically. In this paper, we introduce an algorithm for detection of multitargets in wireless acoustic sensor networks (ADMAN); we localize detected targets by particle filtering after ADMAN. the purpose of ADMAN is detecting any number of targets (We know the approximate locations of targets during the detection algorithm.) in the field of interest. the advantage of ADMAN is its ability to cope with varying number of targets in time. ADMAN does not have any restrictions on the varying pattern of the target number

关键词： Acoustic signal detection Wireless sensor networks Acoustic sensors Target tracking signal processing algorithms Frequency estimation Filtering algorithms State estimation Frequency measurement Position measurement

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