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Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms
Department of Electronics Engineering, Federico Santa Maria Technical University, Av. Espana 1680,Valparaíso, 110-V, Chile
Received: 15 October 2012; in revised form: 16 December 2012 / Accepted: 8 January 2013 / Published: 17 January 2013
Abstract: Process modeling by means of Gaussian-based algorithms often suffers from redundant information which usually increases the estimation computational complexity without significantly improving the estimation performance. In this article, a non-arbitrary measurement selection criterion for Gaussian-based algorithms is proposed. The measurement selection criterion is based on the determination of the most significant measurement from both an estimation convergence perspective and the covariance matrix associated with the measurement. The selection criterion is independent from the nature of the measured variable. This criterion is used in conjunction with three Gaussian-based algorithms: the EIF (Extended Information Filter), the EKF (Extended Kalman Filter) and the UKF (Unscented Kalman Filter). Nevertheless, the measurement selection criterion shown herein can also be applied to other Gaussian-based algorithms. Although this work is focused on environment modeling, the results shown herein can be applied to other Gaussian-based algorithm implementations. Mathematical descriptions and implementation results that validate the proposal are also included in this work.
Keywords: mapping; SLAM; features selection
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Cite This Article
MDPI and ACS Style
Cheein, F.A.A. Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms. Entropy 2013, 15, 287-310.
Cheein FAA. Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms. Entropy. 2013; 15(1):287-310.
Cheein, Fernando A.A. 2013. "Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms." Entropy 15, no. 1: 287-310.