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Proceedings Paper

Performance evaluation of the randomized heuristic approach for multidimensional association
Author(s): A. Sinha; T. Kirubarajan; M. Farooq
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Paper Abstract

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 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) for multidimensional assignment problems with decomposable costs (likelihoods). Unlike many assignment algorithms the RHA does not need the complete candidate assignment tree to start with. Instead, it constructs this tree as required. Results show that the RHA requires only a small fraction of the assignment tree and these results in a considerable reduction of computational cost. Results show that the RHA, on an average, produces better solutions than those produced by Lagrange relaxation-based multidimensional assignment algorithm which has higher computational complexity. Also, using the different solutions obtained in RHA iterations, top m solutions can be constructed with no further computational requirement. These solutions can be utilized in a soft decision based algorithm which performs much better than hard decision based algorithm, as shown in this paper by a ground target tracking example.

Paper Details

Date Published: 25 May 2005
PDF: 11 pages
Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); doi: 10.1117/12.605462
Show Author Affiliations
A. Sinha, McMaster Univ. (Canada)
T. Kirubarajan, McMaster Univ. (Canada)
M. Farooq, Royal Military College of Canada (Canada)

Published in SPIE Proceedings Vol. 5809:
Signal Processing, Sensor Fusion, and Target Recognition XIV
Ivan Kadar, Editor(s)

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