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Proceedings Paper

Bayesian learning of random signal distributions in complex environments
Author(s): D. Keith Wilson; Daniel J. Breton; Chris L. Pettit; Vladimir E. Ostashev
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Paper Abstract

This paper describes the coupling of Bayesian learning methods with realistic statistical models for randomly scattered signals. Such a formulation enables efficient learning of signal properties observed at sensors in urban and other complex environments. It also provides a realistic assessment of the uncertainties in the sensed signal characteristics, which is useful for calculating target class probabilities in automated target recognition. In the Bayesian formulation, the physics-based model for the random signal corresponds to the likelihood function, whereas the distribution for the uncertain signal parameters corresponds to the prior. Single and multivariate distributions for randomly scattered signals (as appropriate to single- and multiple-receiver problems, respectively) are reviewed, and it is suggested that the log-normal and gamma distributions are the most useful due to their physical applicability and the availability of Bayesian conjugate priors, which enable efficient refinement of the signal hyperparameters. Realistic simulations for sound propagation are employed to illustrate the Bayesian processing. The processing is found to be robust to mismatches between the simulated signal distributions and the assumed forms of the likelihood functions.

Paper Details

Date Published: 10 May 2019
PDF: 16 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 1100619 (10 May 2019); doi: 10.1117/12.2523568
Show Author Affiliations
D. Keith Wilson, U.S. Army Engineer Research and Development Ctr. (United States)
Daniel J. Breton, U.S. Army Engineer Research and Development Ctr. (United States)
Chris L. Pettit, U.S. Naval Academy (United States)
Vladimir E. Ostashev, U.S. Army Engineer Research and Development Ctr. (United States)


Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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