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Proceedings Paper

Heuristic task assignment algorithms applied to multisensor-multitarget tracking
Author(s): Robert L. Popp; Krishna R. Pattipati; Richard R. Gassner
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Paper Abstract

In this paper, we are concerned with the problem of assigning track tasks, with uncertain processing costs and negligible communication costs, across a set of homogeneous processors within a distributed computing system to minimize workload imbalances. Since the task processing cost is uncertain at the time of task assignment, we propose several fast heuristic solutions that are extensible, incur very little overhead, and typically react well to changes in the state of the workload. The primary differences between the task assignment algorithms proposed are: (i) the definition of a task assignment cost as a function of past, present, and predicted workload distribution, (ii) whether or not information sharing concerning the state of the workload occurs among processors, and (iii) if workload state information is shared, the reactiveness of the algorithm to such information (i.e., high-pass, moderate, low-pass information filtering). We show, in the context of a multisensor-multitarget tracking problem, that using the heuristic task assignment algorithms proposed can yield excellent results and offer great promise in practice.

Paper Details

Date Published: 31 May 1996
PDF: 12 pages
Proc. SPIE 2759, Signal and Data Processing of Small Targets 1996, (31 May 1996); doi: 10.1117/12.241190
Show Author Affiliations
Robert L. Popp, Univ. of Connecticut (United States)
Krishna R. Pattipati, Univ. of Connecticut (United States)
Richard R. Gassner, Rome Lab. (United States)

Published in SPIE Proceedings Vol. 2759:
Signal and Data Processing of Small Targets 1996
Oliver E. Drummond, Editor(s)

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