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Proceedings Paper

State estimation using the reduced sufficient statistics algorithm
Author(s): Ronald A. Iltis
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Paper Abstract

The reduced sufficient statistics (RSS) algorithm was originally developed by Kulhavy for parameter estimation only. Here, we present a modified form of the RSS algorithm which recursively computes a model posterior probability density function for the state vector. The model density is chosen to be a multidimensional Haar basis representation with dyadic scale, such that the basis functions are disjoint hypercubes. It is then shown that the model density coefficients can be obtained in closed form for this choice of basis functions. A critical part of the modified RSS algorithm is the approximation of the one-step predicted density for the state vector. It is shown that when the transition density for the state vector also has dyadic scale, that a closed-form recursion is ultimately obtained for both predicted and filtered approximating densities. Finally, an application of the algorithm to target tracking using bearings-only measurements is given.

Paper Details

Date Published: 29 October 1997
PDF: 11 pages
Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); doi: 10.1117/12.279525
Show Author Affiliations
Ronald A. Iltis, Univ. of California/Santa Barbara (United States)

Published in SPIE Proceedings Vol. 3163:
Signal and Data Processing of Small Targets 1997
Oliver E. Drummond, Editor(s)

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