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Evaluation of an approximate crosscovariance calculation for track-to-track fusion

Evaluation of an approximate crosscovariance calculation for...

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16th conference on signal and data processing of small targets

作者： Sinav, A Balci, AM Bar-Shalom, Y Turkish Navy Res & Dev Ctr Command Istanbul Turkey

ISBN: (纸本)081945351X

the effect of the crosscovariance in track-to-track fusion has been studied in [2] [3] [4] [5]. Ignoring the crosscovariance of the local track estimation errors leads to optimistic covariance for the fused state estimates. However, evaluation of this crosscovariance is too demanding. Consequently, we approximate the crosscovariances between two sensors' track estimation errors in a manner similar to one of the procedures presented in [2], namely, with constant crosscorrelation coefficients. the results of using this in a practical naval surveillance system are discussed by comparing the exact covariances;of the fused tracks, the covariances obtained with the approximation and the covariances obtained by completely ignoring the crosscorrelations.

关键词： Error analysis Sensors Monte Carlo methods Filtering (signal processing) Matrices Radar Surveillance systems Computer engineering Detection and tracking algorithms Sensor fusion

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Tracking manoeuvring targets using a scale mixture of normals

Tracking manoeuvring targets using a scale mixture of normal...

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16th conference on signal and data processing of small targets

作者： Maskell, S Gordon, N Everett, N Robinson, M QinetiQ Ltd Malvern Technol Ctr Malvern Worcs England

ISBN: (纸本)081945351X

the assumption of Gaussian noise in the system and measurement model has been standard practice for target tracking algorithm development for many years. For problems involving manoeuvring targets this is known to be an over-simplification and a potentially poor approximation. In this paper the use of heavy-tailed distributions is suggested as a means of efficiently modelling the behaviour of manoeuvring targets with a single dynamic model. We exploit the fact that all heavy-tailed distributions can be written as scale mixture of Normals to propose a Rao-Blackwellised particle filter (SMNPF) where particles sample the history of the continuous scale parameter and a Kalman filter is used to conduct the associated filtering for each particle. Schemes are proposed for making the proposal of new particles efficient. Performance of a heavy-tailed system model implemented via the SMNPF filter is compared against an IMM for a sample trajectory taken from a benchmark problem.

关键词： tracking filtering manoeuvring target non-Gaussian heavy-tailed distribution particle filter

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A dynamic approach to efficiently clustering and predicting groups for moving targets

A dynamic approach to efficiently clustering and predicting ...

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16th conference on signal and data processing of small targets

作者： Ma, DY Zhang, AD SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

ISBN: (纸本)081945351X

Effective and efficient approaches to monitor and manage maneuvering objects are of great importance in various applications, such as wide battlefields, traffics, and wireless communications. Modern airborne radar sensors can provide wide-area surveillance coverage of ground activities. the huge volume of radar data renders it impractical and inefficient to examine all the activities of individual moving object. Clustering moving objects and predicting motion tendencies of large groups are becoming a crucial issue for optimizing resource distribution and formulating sound decisions. However, most traditional clustering techniques are static-object-oriented and not effective at clustering maneuvering objects. In addition, the radar data intermittence and noise data, which are caused by extraneous objects and stationary clutter background, are major difficulties in clustering and predicting groups. In this paper, we present a dynamic-object-oriented clustering approach to detecting and predicting large group activities over time. We propose a "core member" concept to support dynamic-object-oriented clustering and to mitigate the effects of data intermittence and noise data. In general, some special targets always tend to remain in a constant group and stay near the center of that group. To a large extent, the movement of these targets represents the activity of the entire group. To exploit this characteristic, we consider these special targets to be core members of their own cluster. the movements of the core members can help us detect clusters and predict their future movements. the performance and results of the application of our approach to CASTFOREM data sets are also presented.

关键词： Radar Detection and tracking algorithms Filtering (signal processing) Target detection Electronic filtering Sensors data modeling Surveillance Wireless communications signal detection

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A Bayesian network tracking database

A Bayesian network tracking database

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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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Attributes in tracking and classification with incomplete data

Attributes in tracking and classification with incomplete da...

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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 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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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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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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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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