mirror of https://github.com/gnss-sdr/gnss-sdr
149 lines
5.5 KiB
C++
149 lines
5.5 KiB
C++
/*!
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* \file lock_detectors.cc
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* \brief Implementation of a library with a set of code and carrier phase lock detectors.
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*
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* SNV_CN0 is a Carrier-to-Noise (CN0) estimator
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* based on the Signal-to-Noise Variance (SNV) estimator [1].
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* Carrier lock detector using normalised estimate of the cosine
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* of twice the carrier phase error [2].
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*
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* [1] Marco Pini, Emanuela Falletti and Maurizio Fantino, "Performance
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* Evaluation of C/N0 Estimators using a Real Time GNSS Software Receiver,"
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* IEEE 10th International Symposium on Spread Spectrum Techniques and
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* Applications, pp.28-30, August 2008.
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*
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* [2] Van Dierendonck, A.J. (1996), Global Positioning System: Theory and
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* Applications,
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* Volume I, Chapter 8: GPS Receivers, AJ Systems, Los Altos, CA 94024.
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* Inc.: 329-407.
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* \authors <ul>
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* <li> Javier Arribas, 2011. jarribas(at)cttc.es
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* <li> Luis Esteve, 2012. luis(at)epsilon-formacion.com
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* </ul>
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*
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* -----------------------------------------------------------------------------
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*
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* GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
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* This file is part of GNSS-SDR.
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*
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* Copyright (C) 2010-2020 (see AUTHORS file for a list of contributors)
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* SPDX-License-Identifier: GPL-3.0-or-later
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*
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* -----------------------------------------------------------------------------
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*/
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#include "lock_detectors.h"
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#include <cmath>
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/*
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* Signal-to-Noise (SNR) (\f$\rho\f$) estimator using the Signal-to-Noise Variance (SNV) estimator:
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* \f{equation}
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* \hat{\rho}=\frac{\hat{P}_s}{\hat{P}_n}=\frac{\hat{P}_s}{\hat{P}_{tot}-\hat{P}_s},
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* \f}
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* where \f$\hat{P}_s=\left(\frac{1}{N}\sum^{N-1}_{i=0}|Re(Pc(i))|\right)^2\f$ is the estimation of the signal power,
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* \f$\hat{P}_{tot}=\frac{1}{N}\sum^{N-1}_{i=0}|Pc(i)|^2\f$ is the estimator of the total power, \f$|\cdot|\f$ is the absolute value,
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* \f$Re(\cdot)\f$ stands for the real part of the value, and \f$Pc(i)\f$ is the prompt correlator output for the sample index i.
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*
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* The SNR value is converted to CN0 [dB-Hz], taking to account the coherent integration time, using the following formula:
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* \f{equation}
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* CN0_{dB}=10*log(\hat{\rho})-10*log(T_{int}),
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* \f}
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* where \f$T_{int}\f$ is the coherent integration time, in seconds.
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*
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*/
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float cn0_svn_estimator(const gr_complex* Prompt_buffer, int length, float coh_integration_time_s)
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{
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float SNR = 0.0;
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float SNR_dB_Hz = 0.0;
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float Psig = 0.0;
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float Ptot = 0.0;
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for (int i = 0; i < length; i++)
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{
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Psig += std::abs(Prompt_buffer[i].real());
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Ptot += Prompt_buffer[i].imag() * Prompt_buffer[i].imag() + Prompt_buffer[i].real() * Prompt_buffer[i].real();
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}
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Psig /= static_cast<float>(length);
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Psig = Psig * Psig;
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Ptot /= static_cast<float>(length);
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SNR = Psig / (Ptot - Psig);
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SNR_dB_Hz = 10.0F * std::log10(SNR) - 10.0F * std::log10(coh_integration_time_s);
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return SNR_dB_Hz;
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}
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/*
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* Signal-to-Noise (SNR) (\f$\rho\f$) estimator using the Moments Method:
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* \f{equation}
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* \hat{\rho}=\frac{\hat{P}_s}{\hat{P}_n}=\frac{\sqrt{2*\hat{M}_2^2 - \hat{M}_4 }}{\hat{M}_2-\sqrt{2*\hat{M}_2^2 - \hat{M}_4 }},
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* \f}
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* where \f$\hat{P}_s=\left(\frac{1}{N}\sum^{N-1}_{i=0}|Re(Pc(i))|\right)^2\f$ is the estimation of the signal power,
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* \f$ \hat{M}_2=\frac{1}{N}\sum^{N-1}_{i=0}|Pc(i)|^2 \f$, \f$\hat{M}_4 = \frac{1}{N}\sum^{N-1}_{i=0}|Pc(i)|^4 \f$, \f$|\cdot|\f$ is the absolute value,
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* \f$Re(\cdot)\f$ stands for the real part of the value, and \f$Pc(i)\f$ is the prompt correlator output for the sample index i.
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*
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* The SNR value is converted to CN0 [dB-Hz], taking to account the coherent integration time, using the following formula:
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* \f{equation}
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* CN0_{dB}=10*log(\hat{\rho})-10*log(T_{int}),
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* \f}
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* where \f$T_{int}\f$ is the coherent integration time, in seconds.
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*
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*/
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float cn0_m2m4_estimator(const gr_complex* Prompt_buffer, int length, float coh_integration_time_s)
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{
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float SNR_aux = 0.0;
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float SNR_dB_Hz = 0.0;
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float Psig = 0.0;
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float m_2 = 0.0;
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float m_4 = 0.0;
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float aux;
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const auto n = static_cast<float>(length);
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for (int i = 0; i < length; i++)
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{
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Psig += std::abs(Prompt_buffer[i].real());
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aux = Prompt_buffer[i].imag() * Prompt_buffer[i].imag() + Prompt_buffer[i].real() * Prompt_buffer[i].real();
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m_2 += aux;
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m_4 += (aux * aux);
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}
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Psig /= n;
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Psig = Psig * Psig;
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m_2 /= n;
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m_4 /= n;
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aux = std::sqrt(2.0F * m_2 * m_2 - m_4);
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if (std::isnan(aux))
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{
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SNR_aux = Psig / (m_2 - Psig);
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}
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else
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{
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SNR_aux = aux / (m_2 - aux);
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}
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SNR_dB_Hz = 10.0F * std::log10(SNR_aux) - 10.0F * std::log10(coh_integration_time_s);
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return SNR_dB_Hz;
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}
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/*
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* The estimate of the cosine of twice the carrier phase error is given by
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* \f{equation}
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* \cos(2\phi)=\frac{NBD}{NBP},
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* \f}
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* where \f$NBD=(\sum^{N-1}_{i=0}Im(Pc(i)))^2-(\sum^{N-1}_{i=0}Re(Pc(i)))^2\f$,
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* \f$NBP=(\sum^{N-1}_{i=0}Im(Pc(i)))^2+(\sum^{N-1}_{i=0}Re(Pc(i)))^2\f$, and
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* \f$Pc(i)\f$ is the prompt correlator output for the sample index i.
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*/
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float carrier_lock_detector(const gr_complex* Prompt_buffer, int length)
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{
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float tmp_sum_I = 0.0;
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float tmp_sum_Q = 0.0;
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float NBD = 0.0;
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float NBP = 0.0;
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for (int i = 0; i < length; i++)
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{
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tmp_sum_I += Prompt_buffer[i].real();
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tmp_sum_Q += Prompt_buffer[i].imag();
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}
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NBP = tmp_sum_I * tmp_sum_I + tmp_sum_Q * tmp_sum_Q;
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NBD = tmp_sum_I * tmp_sum_I - tmp_sum_Q * tmp_sum_Q;
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return NBD / NBP;
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}
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