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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2000"

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A Bayesian network tracking database

A Bayesian network tracking database

引用

16th conference on signal and data processing of small targets

作者： Obermeyer, FH Poore, AB Numer Corp Ft Collins CO USA

ISBN: (纸本)081945351X

Most maximum likelihood (ML) trackers based on measurement fusion (measurement-to-measurement or measurement-to-track) or track-to-track fusion produce a single data association hypothesis together with kinematic track state estimates. Uncertainty in the track states due to process and measurement noise is represented by covariance matrices, however uncertainty in the data association is either entirely neglected or representative of only limited types of association uncertainty. This paper presents a Bayesian-Network uncertainty management system for use in conjunction with maximum-likelihood trackers. The system, termed the Bayesian Network Tracking database (BNTD) comprises algorithms for interactive access, whereby expectation values of arbitrary track properties can be calculated over all association hypotheses, and algorithms and data-structures for long-term storage, whereby the complete set of association hypotheses can be efficiently approximated, even over long time intervals. A conjoined MLE/BNTD system is thus capable of supporting target identification (ID), feature-aided tracking, and long-term track maintenance (LTTM).

关键词： Bayesian network tracking database data association multiple hypothesis tracking uncertainty management

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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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A convex minimization approach to data association with prior constraints

A convex minimization approach to data association with prio...

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16th conference on signal and data processing of small targets

作者： Chen, HM Kirubarajan, T Univ New Orleans Dept Elect Engn New Orleans LA 70148 USA

ISBN: (纸本)081945351X

In this paper we propose a new formulation for reliably solving the measurement-to-track association problem with a priori constraints. Those constraints are incorporated into the scalar objective function in a general formula. This is a key step in most target tracking problems when one has to handle the measurement origin uncertainty. Our methodology is able to formulate the measurement-to-track correspondence problem with most of the commonly used assumptions and considers target feature measurements and possibly unresolved measurements as well. The resulting constrained optimization problem deals with the whole combinatorial space of possible feature selections and measurement-to-track correspondences. To find the global optimal solution, we build a convex objective function and relax the integer constraint. The special structure of this extended problem assures its equivalence to the original one, but it can be solved optimally by efficient algorithms to avoid the combinatorial search. This approach works for any cost function with continuous second derivatives. We use a track formation example and a multisensor tracking scenario to illustrate the effectiveness of the convex programming approach.

关键词： data association multitarget tracking convex programming

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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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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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Feature aided tracking (FAT)

Feature aided tracking (FAT)

引用

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...

引用

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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Real-time multi-sensor data fusion for target detection, classification, tracking, counting, and range estimates

Real-time multi-sensor data fusion for target detection, cla...

引用

conference on Detection and Remediation Technologies for Mines and Minelike targets IX

作者： Tsui, EK Thomas, R U.S. Army Tank-Automotive and Armaments Command/Armament Research Development and Engineering Ctr. (United States) McQ Associates Inc. (United States)

ISBN: (纸本)0819453382

As part of the Commanding General of Army Material Command's Research, Development & Engineering Command (RDECOM), the U.S. Army Research Development and Engineering Center (ARDEC), Picatinny funded a joint development effort with McQ Associates, Inc. to develop an Advanced Minefield Sensor (AMS) as a technology evaluation prototype for the Anti-Personnel Landmine Alternatives (APLA) Track III program. This effort laid the fundamental groundwork of smart sensors for detection and classification of targets, identification of combatant or noncombatant, target location and tracking at and between sensors, fusion of information across targets and sensors, and automatic situation awareness to the 1(st) responder. The efforts have culminated in developing a performance oriented architecture meeting the requirements of size, weight, and power (SWAP). The integrated digital signal processor (DSP) paradigm is capable of computing signals from sensor modalities to extract needed information within either a 360degrees or fixed field of view with acceptable false alarm rate. This paper discusses the challenges in the developments of such a sensor, focusing on achieving reasonable operating ranges, achieving low power, small size and low cost, and applications for extensions of this technology.

关键词： Sensors Cameras Target detection Imaging systems Infrared cameras Image processing Infrared imaging Image compression Digital signal processing Image resolution

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Vehicle tracking using a network of small acoustic arrays

Vehicle tracking using a network of small acoustic arrays

引用

conference on signal processing, Sensor Fusion, and Target Recognition XIII

作者： Calloway, V Hodges, R Harman, S Hume, A Beale, D QinetiQ Malvern WR14 3PS Worcs England

ISBN: (纸本)0819453528

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 non line of sight sensor become a necessity. Acoustic sensors potentially offer the greatest level of capability and will be considered here. In addition, there is a trade off between sensor density and tracking technique that will impact on cost. 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 paper 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： networks acoustic tracking AOA MUSIC vehicle unattended ground sensors UGS

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Identification of weak faint point sources by using principal component analysis.

Identification of weak faint point sources by using principa...

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conference on Image and signal processing for Remote Sensing IX

作者： López-Alonso, JM Alda, J Univ Complutense Madrid Dept Opt Sch Opt Madrid 28037 Spain

ISBN: (纸本)0819451215

The Principal Component Analysis (PCA) has been successfully applied to the characterization of noise in a sequence of frames and to the identification of bad pixels in an imaging array. Remote sensing scenery includes visualization through atmospheric turbulence and sea surfaces. These conditions produce spatial-temporal patterns that can be properly treated with the PCA method. A faint or weak source may be masked by the spatial features of the scene, or even by a fluctuating structure embedded on it. The PCA method is able to filter out these contributions related with global correlations of the set of data. In this sense the identification of the sources with the PCA is not based on the values of signal-to-Noise Ratio (SN-R). It pays more attention to the spatial-temporal structure of the signals. Therefore, it is possible to identify sources below the classical SNR threshold. Another advantage is that the method corresponds with linear transformations, therefore, it is easily implemented requiring a low computational effort. The approach used in this contribution is based on the same reasoning applied to the identification and classification of bad pixels. When the source is a point source, its image will fall on a small cluster of pixels (in the limit it will be only one pixel). This cluster is identified because the spatial-temporal evolution is different from the rest of the array. The method is applied to simulated sceneries as those found in images through atmospheric turbulence and detection of targets in sea images.

关键词： Principal component analysis signal to noise ratio Sensors Image filtering Interference (communication) Remote sensing Atmospheric turbulence Infrared radiation Staring arrays Target detection

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