Proceedings Paper
Statistical efficiency of composite position measurements from passive sensors| Format | Member Price | Non-Member Price |
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Paper Abstract
Combining line-of-sight (LOS) measurements from passive sensors (e.g., satellite-based IR, ground-based cameras,
etc.), assumed to be synchronized, into a single composite Cartesian measurement (full position in 3D) via
maximum likelihood (ML) estimation, can circumvent the need for nonlinear filtering. This ML estimate is
shown to be statistically efficient, and as such, the covariance matrix obtainable from the Cramer-Rao lower
bound provides a consistent measurement noise covariance matrix for use in a target tracking filter.
Paper Details
Date Published: 5 May 2011
PDF: 11 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 805008 (5 May 2011); doi: 10.1117/12.883045
Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)
PDF: 11 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 805008 (5 May 2011); doi: 10.1117/12.883045
Show Author Affiliations
Richard W. Osborne III, Univ. of Connecticut (United States)
Yaakov Bar-Shalom, Univ. of Connecticut (United States)
Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)
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