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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681-690 订阅

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Fixed-lag sequential Monte Carlo data association

Fixed-lag sequential Monte Carlo data association

引用

18th conference on signal and data processing of small targets

作者： Briers, Mark Doucet, Arnaud Maskell, Simon R. Horridge, Paul R. QinetiQ Ltd. (United Kingdom) Cambridge Univ. (United Kingdom) The Univ. of British Columbia (Canada)

ISBN: (纸本)0819462926

The use of multiple scans of data to improve ones ability to improve target tracking performance is widespread in the tracking literature. In this paper, we introduce a novel application of a recent innovation in the SMC literature that uses multiple scans of data to improve the stochastic approximation (and so the data association ability) of a multiple target Sequential Monte Carlo based tracking system. Such an improvement is achieved by resimulating sampled variates over a fixed-lag time window by artificially extending the space of the target distribution. In doing so, the stochastic approximation is improved and so the data association ambiguity is more readily resolved.

关键词： particle filter Sequential Monte Carlo Multi-target tracking fixed-lag data association out-of-sequence measurements

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Radar measurement noise variance estimation with several targets of opportunity - art. no. 62360M

Radar measurement noise variance estimation with several tar...

引用

18th conference on signal and data processing of small targets

作者： Osborne, Richard W., III Bar-Shalom, Yaakov Univ Connecticut Storrs CT USA

ISBN: (纸本)0819462926

A number of methods exist to track a target's uncertain motion through space using inherently inaccurate sensor measurements. A powerful method of adaptive estimation is the interacting multiple model (IMM) estimator. In order to carry out state estimation from the noisy measurements of a sensor, however, the filter should have knowledge of the statistical characteristics of the noise associated with that sensor. The statistical characteristics (accuracies) of real sensors, however, are not always available, in particular for legacy sensors. This paper presents a method of determining the measurement noise variances of a sensor by using multiple IMM estimators while tracking targets whose motion is not known - targets of opportunity. Combining techniques outlined in [1] and [3], the likelihood functions are obtained for a number of IMM estimators, each with different assumptions on the measurement noise variances. Then a search is carried out to bracket the variances of the sensor measurement noises. The end result consists of estimates of the measurement noise variances of the sensor in question.

关键词： tracking noise estimation

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Effects of multiple targets on the mean level STAP detector

Effects of multiple targets on the mean level STAP detector

引用

CIE International conference on Radar

作者： Van Cao, Tri-Tan Def Sci & Technol Org Edinburgh SA 5111 Australia

ISBN: (纸本)9780780395824

Robustness of the constant false alarm rate (CFAR) mean level adaptive detector for a phased array radar equipped with a space-time adaptive processing (STAP) algorithm :is analysed when the secondary data is contaminated by multiple interfering targets at different ranges. Closed-form analysis is first given based on the assumption of Gaussian interference and Swerling I target models. The deleterious effect on target detection is then experimentally examined using the multi channel airborne radar measurements (MCARM) database. It is found that both the probability of detection and the false alarm rate decrease in accordance with the sum of the signal power of the interfering targets. In addition, the false alarm rate is very sensitive to the presence of interfering targets, in the sense that a small sum (below the noise floor) of their signal power can lower the false alarm rate.

关键词： constant false alarm rate (CFAR) phased array radar space-time adaptive processing (STAP) Gaussian interference swerling I targets

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

Nonparametric complex background prediction algorithm using ...

引用

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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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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Comparison of two MDA algorithms for a problem in missile defense

Comparison of two MDA algorithms for a problem in missile de...

引用

18th conference on signal and data processing of small targets

作者： Areta, J. Bar-Shalom, Y. Levedahl, M. Univ Connecticut Storrs CT USA

ISBN: (纸本)0819462926

In this paper we compare the performance of two Multidimensional Assignment Algorithms (MDA), the Lagrangean Relaxation based S-D algorithm and the Sequential m-best 2-D algorithm, applied to a realistic problem in missile defense surveillance. The benchmark problem consists of a set of sources that provide "event" (track) estimates of multiple launches, via a number of communication networks to a Fusion Center (FC) which has to perform data association prior to fusion. The network model used "loses" the information tag that distinguishes reports from the same source transmitted through different networks, i.e., the track identity (ID) assigned by the source is not passed on. Only a track ID assigned by the network, and the source ID accompany the track. Thus detection and elimination of track duplications at the FC is needed. The proposed hierarchical approach to the problem requires the solution of several MDA problems before calculating the fused estimate, so accuracy of the solution of each is crucial. Examples with several launches, sources and networks are presented to compare the performance of the two assignment algorithms.

关键词： multidimensional assignment data association benchmark

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

Nonlinear estimation techniques for impact point prediction ...

引用

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...

引用

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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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

引用

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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