/*! * \file bayesian_estimation.h * \brief Interface of a library with Bayesian noise statistic estimation * * Bayesian_estimator is a Bayesian estimator which attempts to estimate * the properties of a stochastic process based on a sequence of * discrete samples of the sequence. * * [1]: LaMountain, Gerald, VilĂ -Valls, Jordi, Closas, Pau, "Bayesian * Covariance Estimation for Kalman Filter based Digital Carrier * Synchronization," Proceedings of the 31st International Technical Meeting * of the Satellite Division of The Institute of Navigation * (ION GNSS+ 2018), Miami, Florida, September 2018, pp. 3575-3586. * https://doi.org/10.33012/2018.15911 * * \authors * ------------------------------------------------------------------------- * * Copyright (C) 2010-2019 (see AUTHORS file for a list of contributors) * * GNSS-SDR is a software defined Global Navigation * Satellite Systems receiver * * This file is part of GNSS-SDR. * * GNSS-SDR is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GNSS-SDR is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with GNSS-SDR. If not, see . * * ------------------------------------------------------------------------- */ #ifndef GNSS_SDR_BAYESIAN_ESTIMATION_H_ #define GNSS_SDR_BAYESIAN_ESTIMATION_H_ #if ARMA_NO_BOUND_CHECKING #define ARMA_NO_DEBUG 1 #endif #include #include /*! \brief Bayesian_estimator is an estimator of noise characteristics (i.e. mean, covariance) * * Bayesian_estimator is an estimator which performs estimation of noise characteristics from * a sequence of identically and independently distributed (IID) samples of a stationary * stochastic process by way of Bayesian inference using conjugate priors. The posterior * distribution is assumed to be Gaussian with mean \mathbf{\mu} and covariance \hat{\mathbf{C}}, * which has a conjugate prior given by a normal-inverse-Wishart distribution with paramemters * \mathbf{\mu}_{0}, \kappa_{0}, \nu_{0}, and \mathbf{\Psi}. * * [1] TODO: Ref1 * */ class Bayesian_estimator { public: Bayesian_estimator(); explicit Bayesian_estimator(int ny); Bayesian_estimator(const arma::vec& mu_prior_0, int kappa_prior_0, int nu_prior_0, const arma::mat& Psi_prior_0); ~Bayesian_estimator() = default; void init(const arma::mat& mu_prior_0, int kappa_prior_0, int nu_prior_0, const arma::mat& Psi_prior_0); void update_sequential(const arma::vec& data); 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); arma::mat get_mu_est() const; arma::mat get_Psi_est() const; private: arma::vec mu_est; arma::mat Psi_est; arma::vec mu_prior; int kappa_prior; int nu_prior; arma::mat Psi_prior; }; #endif