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29 October 1997 Bayesian approach for detection, localization, and estimation of superposed sources in remote sensing
Ali Mohammad-Djafari
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Proceedings Volume 3163, Signal and Data Processing of Small Targets 1997; (1997) https://doi.org/10.1117/12.279545
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States
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.
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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); https://doi.org/10.1117/12.279545
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KEYWORDS
Remote sensing
Superposition
Convolution
Inverse problems
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