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Attributes in tracking and classification with incomplete data

Attributes in tracking and classification with incomplete da...

引用

16th conference on signal and data processing of small targets

作者： Drummond, OE CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)081945351X

An obvious use for feature and attribute data is for classification and in combat identification. the term classification is used broadly here to include discrimination, detection, target typing, identification, and pattern recognition. An additional use is in the data (or track) association process to reduce the misassociations, often called feature aided tracking. Previous papers discussed the integration of features and attributes into target track processing in addition to their use in multiple target classification. the distinction is made between feature and attribute data because they are processed differently. the term features applies to data from continuous sample space and attributes applies to data from discrete sample space. the primary concern of this paper is to address processing of attributes when there is incomplete data. For example in fusion of sensor data from distributed sensors, a sensor processor might make a hard decision on whether a target exhibits a specific target class (or attribute value) or not (without providing additional information such as likelihoods or probabilities). In another example, a sensor might distribute the likelihood of the most likely target class (or attribute property) and no information on the other possibilities. Both classification and attribute aided tracking is addressed for these examples of incomplete data. the purpose of this paper is to show the feasibility of a simple approach to dealing with incomplete data.

关键词： multiple target tracking feature aided tracking features attributes categorical features multiple models Bayesian methods and Kalman filter

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A randomized heuristic approach for multidimensional association in target tracking

A randomized heuristic approach for multidimensional associa...

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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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PCRLB based multisensor array management for multitarget tracking

PCRLB based multisensor array management for multitarget tra...

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16th conference on signal and data processing of small targets

作者： tharmarasa, R Kirubarajan, T Hernandez, ML McMaster Univ ECE Dept ETF Lab Hamilton ON L8S 4K1 Canada

ISBN: (纸本)081945351X

In this paper we consider the general problem of managing an array of sensors in order to track multiple targets in the presence of measurement origin uncertainty. there are two complicating factors: the first is that because of physical limitations (e.g., communication bandwidth) only a small number of sensors can be utilized at any one time. the second complication is that the associations of measurements to targets/clutter are unknown. It is this second factor that extends our previous work [14]. Hence sensors must be utilized in an efficient manner to alleviate association ambiguities and allow accurate target state estimation. Our sensor management technique is then based on controlling the Posterior Cramer-Rao Lower Bound (PCRLB), which provides a measure of the optimal achievable accuracy of target state estimation. Only recently have expressions for multitarget PCRLBs been determined [7], and the necessary simulation techniques are computationally expensive. However, in this paper we propose some approximations that reduce the computational load and we present two sensor selection strategies for closely spaced (but, resolved) targets. Simulation results show the ability of the PCRLB based sensor management technique to allow efficient utilization of the sensor resources, allowing accurate target state estimation.

关键词： sensor management multisensor-multitarget tracking Posterior Cramer-Rao Lower Bound non-linear filtering data association

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data association combined with the probability hypothesis density filter for multitarget tracking

Data association combined with the probability hypothesis de...

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16th conference on signal and data processing of small targets

作者： Lin, L Bar-Shalom, Y Kirubarajan, T Univ Connecticut Dept ECE Storrs CT 06269 USA

ISBN: (纸本)081945351X

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approach (MHT/assignment) may not give satisfactory results. this is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter, which propagates the PHD or the first moment instead of the full multitarget posterior density, was proposed in. (6-10) In this approach, the integral of the PHD over a region in the state space is the expected number of targets within this region and the peaks in the PHD can be regarded as the estimated locations of the targets at a given time step. In this approach the data association problem is not considered, i.e., the PHD is obtained only for a frame at a time. In our paper, a data association method combined with the PHD approach is proposed for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD filter to get the estimated number and locations of the targets at each time step, and then perform the "peak-to-track" association, whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard PHD filter. Using our approach, the multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging and termination. Simulation results demonstrate that our approach is computationally feasible and effective.

关键词： data association probability hypothesis density multitarget tracking peak extraction group target tracking 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 ...

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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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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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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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A multiple model probability hypothesis density filter for tracking maneuvering targets

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

引用

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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Millimeter-wave and mixed-signal integrated circuits based on advanced metamorphic HEMT technology

Millimeter-wave and mixed-signal integrated circuits based o...

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2004 International conference on Indium Phosphide and Related Materials, 16th IPRM

作者： Schlechtweg, M. Leuther, A. Tessmann, A. Schwölrer, C. Massler, H. Reinert, W. Lang, M. Nowotny, U. Kappeier, O. Walther, M. Loösch, R. Fraunhofer Institute of Applied Solid-State Physics Tullastrasse 72 79108 Freiburg Germany

Integrated circuits based on metamorphic HEMT (MHEMT) technologies on 4″ GaAs substrates for both millimeter-wave and mixed-signal applications are discussed in this paper. Extrinsic cut-off frequencies of f t=293GHz and fmax = 337GHz were achieved for the 70 nm gate length depletion type MHEMT technology. the MMIC process features high yield on transistor and circuit levels. Single-stage low-noise amplifiers demonstrate a small signal gain of 12 dB and a noise figure of 2.2 dB at 94 GHz. An amplifier MMIC developed for D-band operation exhibits a gain of 15 dB from 155 to 160 GHz. the achieved results are equivalent to state-of-the-art InP-based HEMT technologies. In order to realize 80 Gbit/s mixed-signal circuits, a 100 nm gate length enhancement type HEMT process with a transit frequency of 200 GHz is applied. three metalization layers are available for interconnects. the parasitic capacitance of the interconnects is kept low by using BCB and plated air bridge technology. Based on this process, static and dynamic frequency dividers are realized which achieve a maximum toggle frequency of 70 GHz and 108 GHz, respectively. Furthermore, 2:1 multiplexer and 1:2 demultiplexer ICs were developed and successfully tested at 80 Gbit/s data rate. © 2004 IEEE.

关键词： High electron mobility transistors

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Extraction of the average collector velocity in high-speed NpN InP/GaAsSb/InP DHBTs

Extraction of the average collector velocity in high-speed N...

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Indium Phosphide and Related Materials conference

作者： H.G. Liu N. Tao S.P. Watkins C.R. Bolognesi Department of Physics Simon Fraser University Burnaby BC Canada

In "type-II" NpN InP/GaAsSb/InP double heterostructure bipolar transistors (DHBTs), the p/sup +/ GaAsSb base conduction band edge lies /spl Delta/E/sub C/ above the InP collector conduction band: a small ballistic energy /spl Delta/E/sub C/ is imparted to collected electrons as they are launched into the collector. the high initial velocity should in principle reduce the collector signal delay time. In the present work, we extract the average collector electron velocity in high-speed InP/GaAsSb/InP DHBTs, and find a peak average velocity approaching 4 /spl times/ 10/sup 7/ cm/s across a 2000 /spl Aring/ InP collector. this finding provides the first evidence of the performance advantage afforded by abrupt type-II B/C junctions for collector transport when compared to the conventional graded 'launchers' required when a GaInAs base layer is used.

关键词： Indium phosphide Double heterojunction bipolar transistors Electrons Delay effects Wet etching data mining Semiconductor devices Laboratories Power engineering and energy Physics

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