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Proceedings Paper

Monitoring of sensor covariance consistency
Author(s): S. S. Krigman; M. L. Smith; B. E. Tipton
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Paper Abstract

This paper discusses the meaning of filter and covariance consistency and metrics for quantifying covariance consistency. Methodologies for testing and verifying (monitoring) covariance consistency will be explained and contrasted. Possible methodologies with simulated data sets representing hypothetical sensors tracking simulated targets will be demonstrated. One key methodology relies on statistical hypothesis testing of Mahalanobis distances computed for innovation vectors and state vectors. The focus will be on two important contributors to filter inconsistency: sensor bias and a "scaling factor," which can be an important source of inconsistency in a well-behaved unbiased filter. Using these simulated data sets the problems encountered with testing the innovation vectors in the presence of sensor biases will be demonstrated, underscoring the need to focus the tests for sensor biases on the state vectors instead. It will also be shown that tests of innovations can be reliable in determining the scaling factor. A way to remove bias effects in consistency tests applied to tracker state vectors will be demonstrated as well.

Paper Details

Date Published: 21 September 2007
PDF: 14 pages
Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 66990D (21 September 2007); doi: 10.1117/12.741507
Show Author Affiliations
S. S. Krigman, MIT Lincoln Lab. (United States)
M. L. Smith, MIT Lincoln Lab. (United States)
B. E. Tipton, MIT Lincoln Lab. (United States)

Published in SPIE Proceedings Vol. 6699:
Signal and Data Processing of Small Targets 2007
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)

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