Many tracking systems have the requirement to transfer information about a particular tracked object between two systems. The general approach to this involves generation of an object map by the system designating the...
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ISBN:
(纸本)081945351X
Many tracking systems have the requirement to transfer information about a particular tracked object between two systems. The general approach to this involves generation of an object map by the system designating the particular track followed by receipt of the map and correlation to the local track picture of the second system. Correlation performance is in general limited by a number of factors: random track errors added by each system, miss-registration of the two systems' coordinate frames, and miss-match between the numbers of objects tracked by the two systems. Two correlation algorithms are considered for this problem: Global Nearest Neighbor (GNN) and Global Nearest Pattern (GNP). Four basic failure modes are identified for the GNP formulation, and three of these explain failures in the GNN formulation. Analytic expressions are derived for each of these modes, and a comparison of each to Monte-Carlo experiment is provided to demonstrate overall validity.
The assignment algorithm is an old, well-known, widely implemented, fast, combinatorial algorithm for optimal matching in a bipartite graph. This note proposes a method for using the assignment algorithm to solve the ...
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The assignment algorithm is an old, well-known, widely implemented, fast, combinatorial algorithm for optimal matching in a bipartite graph. This note proposes a method for using the assignment algorithm to solve the problem of optimal matching with a variable number of controls, in which there is a choice not only of who to select as a control for each treated subject, but also of how many controls to have for each treated subject. The strategy uses multiple copies of treated subjects and sinks with zero cost to absorb extra controls. Also, it is shown that an optimal matching with variable numbers of controls cannot be obtained by starting with an optimal pair matching and adding the closest additional controls. An example involving mortality after surgery in Pennsylvania hospitals is used to illustrate the method.
In this paper we present the development of a multisensor fusion algorithm using multidimensional data association for multitarget tracking. The work is motivated by a large scale ground target surveillance problem, w...
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ISBN:
(纸本)081942823X
In this paper we present the development of a multisensor fusion algorithm using multidimensional data association for multitarget tracking. The work is motivated by a large scale ground target surveillance problem, where observations from multiple asynchronous sensors with time-varying sampling intervals (e.g., electronically scanned array radars) are used for centralized fusion. The combination of multisensor fusion with multidimensional assignment is done such as to maximize the "time-depth": in addition to "sensor-width" for the number S of lists handled by the assignment algorithm. The time-depth results from the simultaneous use of multiple frames of measurements obtained at different time instants. The sensor-width comes from the geographically distributed nature of the sensors. A procedure, which guarantees maximum effectiveness for an S-dimensional data association (S greater than or equal to 3), i.e., maximum time-depth (S-1) for each sensor without sacrificing the fusion across sensors, is presented. Using a sliding-window technique (of length S), the estimates are updated after each frame of measurements. The algorithm provides a systematic approach to automatic track formation, maintenance and termination for multitarget tracking using multisensor fusion with multidimensional assignment for data association. Estimation results are presented for simulated data.
In this paper we present the development of a Variable Structure Interacting Multiple Model (VS-IMM) estimator for tracking groups of ground targets on constrained paths using Moving Target Indicator (MTI) reports obt...
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ISBN:
(纸本)0819428221
In this paper we present the development of a Variable Structure Interacting Multiple Model (VS-IMM) estimator for tracking groups of ground targets on constrained paths using Moving Target Indicator (MTI) reports obtained from an airborne sensor. The targets are moving along a highway, with varying inter-visibility (obscuration) due to changing terrain conditions. In addition, the roads can branch, merge or cross. Some of the targets may also move in an open field. This constrained motion estimation problem is handled using an IMM estimator with varying mode sets depending on the topography. The number of models in the IMM estimator, their types and their parameters are modified adaptively, in real-time, based on the estimated position of the target and the corresponding road/visibility conditions. This topography-based variable structure mechanism eliminates the need for carrying all the possible models throughout the entire tracking period as in the standard IMM estimator, improving performance and reducing computational load.
In this paper we present the development and implementation of a multisensor-multitarget tracking algorithm for large scale air traffic surveillance based on the IMM state estimator combined with a a-dimensional assig...
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ISBN:
(纸本)0819425850
In this paper we present the development and implementation of a multisensor-multitarget tracking algorithm for large scale air traffic surveillance based on the IMM state estimator combined with a a-dimensional assignment for data association. The algorithm can be used to track a large number of targets from measurements obtained with a large number of radars. The use of the algorithm is illustrated on measurements obtained from 5 FAA radars, which are asynchronous, heterogeneous and geographically distributed over a large area. Both secondary radar data (beacon returns from cooperative targets) as well as primary radar data (skin returns from noncooperative targets) are used. The target IDs from the beacon returns are not used in the data association. The surveillance region includes about 800 targets that exhibit different types of motion. The performance of the IMM estimator is compared with that of the Kalman filter. A number of performance measures that can be used on real data without knowledge of the ground truth are presented for this purpose. It is shown that the IMM estimator performs better than the Kalman filter. The advantage of fusing multisensor data is quantified. It is also shown that the computational requirements in the multisensor case are lower than in single sensor case.
In this paper we present a new technique for data association using multiassignment for tracking a large number of closely spaced (and overlapping) objects. The algorithm is illustrated on a biomedical problem, namely...
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ISBN:
(纸本)0819425850
In this paper we present a new technique for data association using multiassignment for tracking a large number of closely spaced (and overlapping) objects. The algorithm is illustrated on a biomedical problem, namely the tracking of a group of fibroblast (tissue) cells from an image sequence, which motivated this work. The algorithm presents a novel iterated approach to multiassignnent using successive one-to-one assignments of decreasing size with modified costs. The cost functions, which are adjusted depending on the ''depth'' of the current assignment level and on the tracking results, are derived. The resulting assignments are used to form, maintain and terminate tracks with a modified version of the Probabilistic Data Association Filter, which can handle the contention for a single measurement among multiple tracks in addition to the association of multiple measurements to a single track. Estimation results are given and compared with those of the standard a-dimensional one-to-one assignment algorithm. It is shown that iterated multiassignnent results in superior measurement-to-track association.
Two problems in image processing are presented: chromosome classification and the construction of cross-sectional images from non-cross-sectional views. Both problems are formulated as network flow models, a type of m...
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Two problems in image processing are presented: chromosome classification and the construction of cross-sectional images from non-cross-sectional views. Both problems are formulated as network flow models, a type of model generally found in the O.R. setting. This shows the applicability of such O.R. techniques to problems of intelligent information processing.
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