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Your search keyword '"quantized data"' showing total 2 results
Start Over Descriptor "quantized data" Topic bayesian inference
2 results on '"quantized data"'

1. Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective.

Author

Piga, Dario, Mejari, Manas, and Forgione, Marco

Subjects
*DYNAMICAL systems, *FINITE impulse response filters, *IMPULSE response, *BAYESIAN field theory, *INSTRUCTIONAL systems
Abstract

Identification of dynamical systems from low-resolution quantized observations presents several challenges because of the limited amount of information available in the data and since proper algorithms have to be designed to handle the error due to quantization. In this article, we consider identification of infinite impulse response models from quantized outputs. Algorithms both for maximum-likelihood estimation and Bayesian inference are developed. Finally, a particle-filter approach is presented for recursive reconstruction of the latent nonquantized outputs from past quantized observations. [ABSTRACT FROM AUTHOR]

Published
2022
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2. A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks.

Author

Nguyen, Thi Le Thu, Septier, Francois, Rajaona, Harizo, Peters, Gareth W., Nevat, Ido, and Delignon, Yves

Subjects
*BAYESIAN analysis, *MULTISENSOR data fusion, *WIRELESS sensor networks, *WIRELESS communications, *MONTE Carlo method
Abstract

In this paper, we address the challenging problem of multiple source localization in wireless sensor networks (WSN). We develop an efficient statistical algorithm, based on the novel application of sequential Monte Carlo (SMC) sampler methodology, that is able to deal with an unknown number of sources given quantized data obtained at the fusion center from different sensors with imperfect wireless channels. We also derive the posterior Cramér–Rao bound (PCRB) of the source location estimate. The PCRB is used to analyze the accuracy of the proposed SMC sampler algorithm and the impact that quantization has on the accuracy of location estimates of the sources. Extensive experiments show the benefits of the proposed scheme in terms of the accuracy of the estimation method that is required for model selection (i.e., the number of sources) and the estimation of the source characteristics compared to the classical importance sampling method. [ABSTRACT FROM AUTHOR]

Published
2016
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