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A Nonconvex Cost Optimization Approach to Tracking Multiple Targets by a Parallel Computational Network

Rose, Kenneth and Gurewitz, Eitan and Fox, Geoffrey (1990) A Nonconvex Cost Optimization Approach to Tracking Multiple Targets by a Parallel Computational Network. In: Proceedings of the Fifth Distributed Memory Computing Conference, 1990. IEEE , Piscataway, NJ, pp. 78-84. ISBN 0-8186-2113-3. https://resolver.caltech.edu/CaltechAUTHORS:20170628-173206820

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Abstract

The problem of tracking multiple targets in the presence of displacement noise and clutter is formulated as a nonconvex optimization problem. The form of the suggested cost function is shown to be suitable for the Graduated Non-Convexity algorithm, which can be viewed as deterministic annealing. The method is first derived for the two-dimensional (spatial/ temporal) case, and then generalized to the multi-dimensional case. The complexity grows linearly with the number of targets. Computer simulations show the performance with crossing trajectories.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/DMCC.1990.555365DOIArticle
http://ieeexplore.ieee.org/document/555365/PublisherArticle
Additional Information:© 1990 IEEE.
Record Number:CaltechAUTHORS:20170628-173206820
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170628-173206820
Official Citation:K. Rose, E. Gurewitz and G. Fox, "A Nonconvex Cost Optimization Approach to Tracking Multiple Targets by a Parallel Computational Network," Proceedings of the Fifth Distributed Memory Computing Conference, 1990., 1990, pp. 78-84. doi: 10.1109/DMCC.1990.555365
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:78681
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:29 Jun 2017 01:41
Last Modified:03 Oct 2019 18:10

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