An Information Theoretic Approach to Optimal Sensor Data Selection for State Estimation (2002)
| Venue: | IEEE Trans. on Pattern Analysis and Machine Intelligence |
| Citations: | 7 - 1 self |
BibTeX
@ARTICLE{Denzler02aninformation,
author = {J. Denzler and C. Brown},
title = {An Information Theoretic Approach to Optimal Sensor Data Selection for State Estimation},
journal = {IEEE Trans. on Pattern Analysis and Machine Intelligence},
year = {2002},
volume = {24}
}
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Abstract
In this paper we introduce a formalism for optimal sensor parameter selection for iterative state estimation in static systems. In contrast to common approaches, where a certain metric --- for example, the mean squared error between true and estimated state --- is optimized during state estimation, in this work the optimality is defined in terms of reduction in uncertainty in the state estimation process. The main assumption is that state estimation becomes more reliable if the uncertainty and ambiguity in the state estimation process can be reduced.
