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Removal of bias due to propagation of estimates through nonlinear mappingsFormat | Member Price | Non-Member Price |
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Paper Abstract
Bias introduced due to noisy point estimates being propagated through deterministic nonlinear mappings is a reoccurring
problem in high-fidelity tracking and classification systems. This paper proves that it is a misconception that such bias
is reduced when computing the expected value of the nonlinear output that follows when treating the input as a random
vector with expectation equal to the provided estimate. Instead, this doubles the bias. An approximately unbiased
estimator and an estimate of its covariance matrix are provided. The estimator can be calculated also in the case where
the Hessian matrices associated with the nonlinear mapping are unavailable.
Paper Details
Date Published: 17 April 2008
PDF: 9 pages
Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 69690G (17 April 2008); doi: 10.1117/12.777456
Published in SPIE Proceedings Vol. 6969:
Signal and Data Processing of Small Targets 2008
Oliver E. Drummond, Editor(s)
PDF: 9 pages
Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 69690G (17 April 2008); doi: 10.1117/12.777456
Show Author Affiliations
Trond Jorgensen, SPARTA, Inc. (United States)
Ron Rothrock, SPARTA, Inc. (United States)
Published in SPIE Proceedings Vol. 6969:
Signal and Data Processing of Small Targets 2008
Oliver E. Drummond, Editor(s)
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