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https://github.com/gnss-sdr/gnss-sdr
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91 lines
3.3 KiB
C++
91 lines
3.3 KiB
C++
/*!
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* \file bayesian_estimation.h
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* \brief Interface of a library with Bayesian noise statistic estimation
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*
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* Bayesian_estimator is a Bayesian estimator which attempts to estimate
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* the properties of a stochastic process based on a sequence of
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* discrete samples of the sequence.
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*
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* [1]: LaMountain, Gerald, Vilà-Valls, Jordi, Closas, Pau, "Bayesian
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* Covariance Estimation for Kalman Filter based Digital Carrier
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* Synchronization," Proceedings of the 31st International Technical Meeting
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* of the Satellite Division of The Institute of Navigation
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* (ION GNSS+ 2018), Miami, Florida, September 2018, pp. 3575-3586.
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* https://doi.org/10.33012/2018.15911
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*
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* \authors <ul>
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* <li> Gerald LaMountain, 2018. gerald(at)ece.neu.edu
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* <li> Jordi Vila-Valls 2018. jvila(at)cttc.es
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* </ul>
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* -------------------------------------------------------------------------
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*
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* Copyright (C) 2010-2018 (see AUTHORS file for a list of contributors)
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*
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* GNSS-SDR is a software defined Global Navigation
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* Satellite Systems receiver
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*
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* This file is part of GNSS-SDR.
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*
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* GNSS-SDR is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* GNSS-SDR is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with GNSS-SDR. If not, see <https://www.gnu.org/licenses/>.
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*
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* -------------------------------------------------------------------------
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*/
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#ifndef GNSS_SDR_BAYESIAN_ESTIMATION_H_
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#define GNSS_SDR_BAYESIAN_ESTIMATION_H_
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#include <armadillo>
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#include <gnuradio/gr_complex.h>
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/*! \brief Bayesian_estimator is an estimator of noise characteristics (i.e. mean, covariance)
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*
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* Bayesian_estimator is an estimator which performs estimation of noise characteristics from
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* a sequence of identically and independently distributed (IID) samples of a stationary
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* stochastic process by way of Bayesian inference using conjugate priors. The posterior
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* distribution is assumed to be Gaussian with mean \mathbf{\mu} and covariance \hat{\mathbf{C}},
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* which has a conjugate prior given by a normal-inverse-Wishart distribution with paramemters
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* \mathbf{\mu}_{0}, \kappa_{0}, \nu_{0}, and \mathbf{\Psi}.
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*
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* [1] TODO: Ref1
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*
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*/
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class Bayesian_estimator
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{
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public:
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Bayesian_estimator();
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Bayesian_estimator(int ny);
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Bayesian_estimator(const arma::vec& mu_prior_0, int kappa_prior_0, int nu_prior_0, const arma::mat& Psi_prior_0);
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~Bayesian_estimator();
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void init(const arma::mat& mu_prior_0, int kappa_prior_0, int nu_prior_0, const arma::mat& Psi_prior_0);
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void update_sequential(const arma::vec& data);
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void update_sequential(const arma::vec& data, const arma::vec& mu_prior_0, int kappa_prior_0, int nu_prior_0, const arma::mat& Psi_prior_0);
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arma::mat get_mu_est() const;
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arma::mat get_Psi_est() const;
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private:
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arma::vec mu_est;
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arma::mat Psi_est;
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arma::vec mu_prior;
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int kappa_prior;
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int nu_prior;
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arma::mat Psi_prior;
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};
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#endif
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