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共 447 条记录，以下是361-370 订阅

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A randomized heuristic approach for multidimensional association in target tracking

A randomized heuristic approach for multidimensional associa...

引用

16th conference on signal and data processing of small targets

作者： Sinha, A Kirubarajan, T McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON L8S 4K1 Canada

ISBN: (纸本)081945351X

the combinatorial optimization problem of multidimensional assignment has been treated with renewed interest because of its extensive application in target tracking, cooperative control, robotics and image processing. In this work we particularly concentrate on data association in multisensor-multitarget tracking algorithms, in which solving the multidimensional assignment is an essential step. Current algorithms generate good suboptimal solutions (with quantifiable accuracy) to these problems in pseudo polynomial time. However, in dense scenarios these methods can become inefficient because of the resulting dense candidate association tree. Also, in order to generate the top m (or ranked) solutions these algorithms need to solve a number of optimization problems, which increases the computational complexity significantly. In this paper we develop a Randomized Heuristic Approach (RHA), in which, in each step, instead of choosing the best solution indicated by the heuristic, one of the solutions is chosen randomly depending on the "probability" associated with it. the resulting algorithm produces solutions that are as good as or better than those produced by Lagrange relaxation-based algorithms that have much higher computational complexity. this method also produces other ranked best solutions with no further computational requirement.

关键词： multisensor-multitarget tracking data association randomized heuristic approach assignment algorithms

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Trajectory estimation of closely spaced objects (CSO) using infrared focal plane data of an STSS (Space tracking and surveillance system) platform

Trajectory estimation of closely spaced objects (CSO) using ...

引用

16th conference on signal and data processing of small targets

作者： Korn, J Holtz, H Farber, MS Aerosp Corp El Segundo CA 90245 USA

ISBN: (纸本)081945351X

A major challenge in tracking midcourse objects by means of an infrared (IR) sensor is that during a significant part of the trajectory they are closely spaced (Closely Spaced Objects, or CSO). the imprints of C the CSOs on the IR focal plane create blurred unresolved clusters where the number, the coordinates, and the radiant intensities of the objects are not immediately apparent. this paper presents two methods for solving the problem of midcourse CSO resolution using IR focal plane data in the context of the Space Tracking and Surveillance System (STSS). Both approaches are based on dynamics/radiant intensity models of the focal plane objects, and use least squares-based minimization procedures(1). the first and more traditional baseline approach estimates the focal plane coordinates of the objects and their intensities on a frame-by-frame basis. the object tracks are then established by associating and fitting the estimates of all the frames to the postulated models. An alternative, multi-frame approach explored in this paper, uses the focal plane information from an entire sequence of frames, and, using least squares criteria over space and time and matched filtering, estimates the model parameters directly. With this "track-before-detect" approach, the association problem of objects to tracks is embedded in the estimation procedure.

关键词： Closely Space Objects (CSO) infrared (IR) focal plane

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Application of the Kalman-Levy filter for tracking maneuvering targets

Application of the Kalman-Levy filter for tracking maneuveri...

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16th conference on signal and data processing of small targets

作者： Sinha, A Kirubarajan, T Bar-Shalom, Y McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON Canada

ISBN: (纸本)081945351X

In target tracking algorithms using Kalman filtering-like approaches, the standard assumptions are Gaussian process and measurement noise models. Based on these assumptions, the Kalman filter is widely used in single or multiple filter versions (e.g., in an Interacting Multiple Model - IMM - estimator). the over-simplification resulting from the above assumptions can cause severe degradation in tracking performance. Of particular concern is the simplistic white noise or Wiener process acceleration models used to handle maneuvering targets. Presence of heavy-tailed noise in the observation process is another concern. In this paper we explore the application of Kalman-Levy filter to handle maneuvering targets. this filter assumes a heavy tailed noise distribution known as the Levy distribution. Unlike in the case of Gaussian distribution, the existence of the covariance is not guaranteed in this case. Due to the heavy tailed nature of the assumed distribution, the Kalman-Levy filter is more effective in the presence of large errors that can occur, for example, due to the onset of acceleration or deceleration. However, for the same reason, the performance of Kalman-Levy filter in non-maneuvering portion of track is worse than a Kalman filter's. this motivates us to develop an IMM estimator incorporating a Kalman filter and a Kalman-Levy filter. the performance of this filter is compared with an IMM estimator with two standard Kalman filters in a scenario from the 4th Navy tracking benchmark problem. It is found that the IMM estimator with a Kalman-Levy filter performs better than the other IMM estimator in both maneuvering and non-maneuvering portion of target flight.

关键词： maneuvering target tracking interacting multiple model filter Kalman filter Kalman-Levy filter heavy-tailed process noise

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High-speed data acquisition and digital signal processing system for PET imaging techniques applied to mammography

High-speed data acquisition and digital signal processing sy...

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13th IEEE-NPSS Real Time conference

作者： Martínez, JD Benlloch, JM Cerdá, J Lerche, CW Pavón, N Sebastiá, A Univ Politecn Valencia DSD Grp Dept Elect Engn Valencia 46020 Spain CSIC IFIC Nucl Med Phys Grp Valencia 46980 Spain

this paper is framed into the Positron Emission Mammography (PEM) project, whose aim is to develop an innovative gamma ray sensor for early breast cancer diagnosis. Currently, breast cancer is detected using low-energy X-ray screening. However, functional imaging techniques such as PET/FDG could be employed to detect breast cancer and track disease changes with greater sensitivity. Furthermore, a small and less expensive PET camera can be utilized minimizing main problems of whole body PET. To accomplish these objectives, we are developing a new gamma ray sensor based on a newly released photodetector. However, a dedicated PEM detector requires an adequate data acquisition (DAQ) and processing system. the characterization of gamma events needs a free-running analog-to-digital converter (ADC) with sampling rates of more than 50 Ms/s and must achieve event count rates up to 10 MHz. Moreover, comprehensive data processing must be carried out to obtain event parameters necessary for performing the image reconstruction. A new generation digital signal processor (DSP) has been used to comply with these requirements. this device enables us to manage the DAQ system at up to 80 Ms/s and to execute intensive calculi over the detector signals. this paper describes our designed DAQ and processing architecture whose main features are: very high-speed data conversion, multichannel synchronized acquisition with zero dead time, a digital triggering scheme, and high throughput of data with an extensive optimization of the signal processing algorithms.

关键词： data acquisition free-running sampling nuclear pulse processing positron emission mammography (PEM)

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A multiple model probability hypothesis density filter for tracking maneuvering targets

A multiple model probability hypothesis density filter for t...

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16th conference on signal and data processing of small targets

作者： Punithakumar, K Kirubarajan, T Sinha, A McMaster Univ ECE Dept Hamilton ON L8S 4K1 Canada

ISBN: (纸本)081945351X

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. With multiple targets, representing the full posterior distribution over target states is not practical. the problem becomes even more complicated when the target number varies, in which case the dimensionality of the state space itself becomes a discrete random variable. the Probability Hypothesis Density (PHD) filter, which propagates only the first-order statistical moment (or the PHD) of the full target posterior. has been shown to be a computationally efficient solution to multitarget tracking problems with varying number of targets. the integral of PHD in any region of the state space gives the expected number of targets in that re-ion. With maneuvering targets, detecting and tracking the changes in the target motion model also become important;but current PHD implementations do not provide a mechanism for handling this. the target dynamic model uncertainty can be resolved by assuming multiple models for possible motion modes and then combining the mode-conditioned estimates in a manner similar to the one used in the Interacting Multiple Model (IMM) estimator. In this paper a multiple model implementation of the PHD filter, which approximates the PHD by a set of weighted random samples propagated over time using Sequential Monte Carlo methods, is proposed. the resulting filter can handle nonlinear, non-Gaussian dynamics with uncertain model parameters in multisensor-multitarget tracking scenarios. Simulation results are presented to show the effectiveness of the proposed filter over sin-le-model PHD filters.

关键词： maneuvering target tracking nonlinear filtering probability hypothesis density filter sequential Monte Carlo methods multiple model estimation

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Feature aided tracking (FAT)

Feature aided tracking (FAT)

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16th conference on signal and data processing of small targets

作者： Singer, PF Coursey, AL Raytheon Co El Segundo CA 90245 USA

ISBN: (纸本)081945351X

Association of observations and tracks is a fundamental component of most solutions to the tracking problem. Association is frequently formulated as a multiple hypothesis test. Typically, the test statistic, called the track score, is the likelihood or likelihood ratio of the observations conditioned upon the association hypotheses. Assuming that the test is reasonably efficient, further reduction in the association error probability necessitates the introduction of additional information into the track score. this additional information is embodied in quantities called track features which are to be included in the track score. In practice, the necessary conditional probabilities of the track features are unknown. the class of non-parametric hypothesis tests is designed to provide such a test in the absence of any probabilistic information about the data. However, the test statistics used in non-parametric tests cannot be used directly in the track score. the one probabilistic quantity generally available with non-parametric tests is the Type I error probability, the probability of failing to accept a true hypothesis. If the non-parametric test is distribution free then the Type I error probability is independent of the distribution of the track features. this paper presents a distribution free, non-parametric test of the track features that can be used to test the association hypotheses and a quantity that can be included in the track score is derived from the Type I error probability of the test.

关键词： feature aided tracking non-parametric hypothesis test distribution free data association

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Vehicle tracking using a network of small acoustic arrays

Vehicle tracking using a network of small acoustic arrays

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IEEE conference on Aerospace

作者： V. Calloway R. Hodges S. Harman A. Hume D. Beale QinetiQ Limited Malvern Worcestershire UK

Major advances in base technologies of computer processors and low cost communications have paved the way for a resurgence of interest in unattended ground sensors. Networks of sensors offer the potential of low cost persistent surveillance capability in any area that the sensor network can be placed. Key to this is the choice of sensor on each node. If the system is to be randomly deployed then nonline of sight sensor become a necessity. Acoustic sensors potentially offer the greatest level of capability and will be considered here. As a passive sensor, only time of arrival or bearing information can be obtained from an acoustic array, thus the tracking of targets must be done in this domain. this work explores the critical step between array processing and implementation of the tracking algorithm. Specifically, unlike previous implementations of such a system, the bearings from each frequency interval of interest are not averaged but are used as data points within a Kalman filter. thus data is not averaged and then filtered but all data is put into the tracking filter.

关键词： Vehicles Acoustic arrays Acoustic sensors Sensor arrays Costs Target tracking Surveillance Sensor systems Array signal processing Frequency

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Chirp pulse microwave computed tomography(CP-MCT) equipped with a fan beam scanner for high-speed imaging of a biological target

Chirp pulse microwave computed tomography(CP-MCT) equipped w...

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25th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2003

作者： Miyakawa, M. Harada, E. Jing, W. Center for Transdisciplinary Research Niigata University Niigata Japan Graduate School of Natural Science and Technology Niigata University Niigata Japan

ISBN: (纸本)0780377893

Recently, microwave imaging aiming at early-stage-tumor detection has become of major interest because of its relatively high contrast resolution in the image. Chirp pulse microwave computed tomography (CP-MCT) obtains the attenuation and/or phase shift images of biological targets by use of a chirp pulse signal from 1 GHz to 2 GHz and signal processing techniques. the experimental- and computational-studies show that the estimated spatial resolution becomes 10 to 12 mm and very small temperature variation such as 0.3 degree to 0.5 degree is detectable. Usefulness of this imaging modality increases by developing a fan beam scanner that enables fast data acquisition. In fact, it takes only a few minutes for imaging, while the prototype system takes about 90 minutes. © 2003 IEEE.

关键词： data acquisition

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Chirp pulse microwave computed tomography(CP-MCT) equipped with a fan beam scanner for high-speed imaging of a biological target

Chirp pulse microwave computed tomography(CP-MCT) equipped w...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： M. Miyakawa E. Harada W. Jing Center for Transdisciplinary Research Niigata University Niigata Japan Graduate School ofNatural Science and Technology NiiRata University Niigata Japan

关键词： Chirp Biology computing Microwave imaging High-resolution imaging Spatial resolution Image resolution signal resolution Microwave theory and techniques Computed tomography Attenuation

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Detection of small IR objects using wavelets, matched subspace detectors, and registration

Detection of small IR objects using wavelets, matched subspa...

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14th conference on signal and data processing of small targets

作者： Hewer, G Kuo, W Kenney, C Hanson, G Bobinchak, J USN Air Warfare Ctr China Lake CA 93555 USA

ISBN: (纸本)0819444782

Infrared sensors and advanced signal processing are used to detect small (or "point") targets in highly cluttered and noisy environments. In this paper, a wavelet detection algorithm and tracking of small targets in clutter will be discussed. A new registration algorithm based on optical flow estimates with matched subspace detectors against small maneuverable targets is also discussed. Both detectors incorporate adaptive constant false alarm rate (CFAR) detection statistics. Simulation of the detection and tracking algorithms using an unclassified database with a helicopter target and platform for the video cameras is summarized.

关键词： wavelets matched subspace detector registration

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