29 October 1997 Bayesian approach for detection, localization, and estimation of superposed sources in remote sensing
Author Affiliations +
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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ali Mohammad-Djafari, Ali Mohammad-Djafari, } "Bayesian approach for detection, localization, and estimation of superposed sources in remote sensing", Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); doi: 10.1117/12.279545; https://doi.org/10.1117/12.279545
PROCEEDINGS
10 PAGES


SHARE
Back to Top