/*!
* \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
* - Gerald LaMountain, 2018. gerald(at)ece.neu.edu
*
- Jordi Vila-Valls 2018. jvila(at)cttc.es
*
* -------------------------------------------------------------------------
*
* 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.
*
* SPDX-License-Identifier: GPL-3.0-or-later
*
* -------------------------------------------------------------------------
*/
#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