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Proceedings Paper

Multisensor management optimization for multitarget tracking based on Hausdorff distance minimization
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Paper Abstract

In multi-hypothesis target tracking, given the time-predicted tracks, we consider the sensor management problem of directing the sensors' Field of View (FOV) in such a way that the targets detection rate is improved. Defining a (squared) distance between a sensor and a track as the (squared) Euclidean distance between the centers of their respective Gaussian distributions, weighted by the sum of the covariance matrices, the problem is formulated as the minimization of the Hausdorff distance from the set of tracks to the set of sensors. An analytical solution for the single sensor case is obtained, and is extended to the multiple sensors case. This extension is achieved by performing the following: (1) It is first proved that for an optimal solution, there exists a partition of the set of tracks into subsets, and an association of each subset with a sensor, such that each subset-sensor pair is optimal in the Hausdorff distance sense; (2) a brute force search is then conducted to check all possible subset-partitions of the tracks as well as the permutations of sensors; (3) for each subset-sensor pair, the optimal solution is obtained analytically; and (4) the configuration with the smallest Hausdorff distance is declared as the optimal solution for the given multi-target multi-sensor problem. Some well established loopless algorithms for generating set partitions and permutations are implemented to reduce the computational complexity. A simulation result demonstrating the proposed sensor management algorithm is also presented.

Paper Details

Date Published: 31 July 2002
PDF: 11 pages
Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); doi: 10.1117/12.477611
Show Author Affiliations
Lingji Chen, Scientific Systems Co., Inc. (United States)
Adel I. El-Fallah, Scientific Systems Co., Inc. (United States)
Raman K. Mehra, Scientific Systems Co., Inc. (United States)
Ronald P. S. Mahler, Lockheed Martin Tactical Systems (United States)
John R. Hoffman, Lockheed Martin Tactical Systems (United States)
Chad A. Stelzig, Lockheed Martin Tactical Systems (United States)
Mark G. Alford, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 4729:
Signal Processing, Sensor Fusion, and Target Recognition XI
Ivan Kadar, Editor(s)

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