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Proceedings Paper

Nonlinear stochastic filtering technique for radar/lidar inversion
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Paper Abstract

This paper addresses the joint estimation of backscatter and extinction coefficients from range/time noisy data under a nonlinear stochastic filtering setup. This problem is representative of many remote sensing applications such as weather radar and elastic-backscatter lidar. A Bayesian perspective is adopted. Thus, in addition to the observation mechanism, relating in a probabilistic sense the observed data with the parameters to be estimated, a prior probability density function has to be specified. We adopt as prior a causal first order auto-regressive Gauss-Markov random field. By using a reduced order state-space representation of the prior, we derive a nonlinear stochastic filter that recursively computes the backscatter and extinction coefficients at each site. A set of experiments based on simulated data illustrates the potential of the proposed approach.

Paper Details

Date Published: 4 October 1999
PDF: 12 pages
Proc. SPIE 3809, Signal and Data Processing of Small Targets 1999, (4 October 1999); doi: 10.1117/12.364048
Show Author Affiliations
Jose M. B. Dias, Instituto Superior Tecnico (Portugal)
Elsa S. R. Fonseca, Univ. de Beira Interior (Portugal)


Published in SPIE Proceedings Vol. 3809:
Signal and Data Processing of Small Targets 1999
Oliver E. Drummond, Editor(s)

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