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Adam Attarian

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North Carolina State University

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grad student at ncsu. i work on inverse problems, filtering and estimation, and a little bit of control theory.

my webpage is at http://www4.ncsu.edu/~arattari/

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08 Apr 2009 Automatic Differentiation for Matlab This package provides automatic first and second derivatives for vector-functions and matrices. Author: Martin Fink
09 Nov 2008 Learning the Unscented Kalman Filter An implementation of Unscented Kalman Filter for nonlinear state estimation. Author: Yi Cao

Greetings,

While I understand it is no longer necessary to augment the states when you consider additive noise, it is also apparent that you then only have to use the first L weights, and not the 2L+1 weights. Can you comment on this? Is anything lost or gained by using L weights or 2L+1 weights in the additive noise case?

I ask only because I saw degraded performance when I switched from using all 2L+1 weights to using only the first L weights in my program (I am not augmenting the states).

Thanks,
Adam
NC State Univ
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estimation, information matrix, inverse problems, parameter estimation, sensitivity analysis

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