Share Email Print

Proceedings Paper

Bayesian approach for detection, localization, and estimation of superposed sources in remote sensing
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In many remote sensing techniques the measured signal can be modelled as the result of a convolution operator (with completely or partially known impulse response) on an input signal which is known to be the superposition of a finite number of elementary signals with unknown parameters. The restoration or inversion problem becomes then the estimation of these parameters. In this work we propose a Bayesian estimation framework to solve these inverse problems by introducing some prior knowledge on the unknown parameters via the specified prior probability laws on them. More specifically, we propose to use the maximum a posteriori (MAP) estimation method with some specific choices for the prior laws. The MAP criterion is optimized using a modified Newton-Raphson algorithm. Some simulation results illustrate the performances of the proposed method. In these simulations we considered the input signal to be the superposition of Gaussians with unknown positions, standard deviations and amplitudes.

Paper Details

Date Published: 29 October 1997
PDF: 10 pages
Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); doi: 10.1117/12.279545
Show Author Affiliations
Ali Mohammad-Djafari, Ecole Superieure d'Electricite de Plateau de Moulon (France)

Published in SPIE Proceedings Vol. 3163:
Signal and Data Processing of Small Targets 1997
Oliver E. Drummond, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?