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https://github.com/gnss-sdr/gnss-sdr
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140 lines
3.6 KiB
C++
140 lines
3.6 KiB
C++
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/*!
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* \file exponential_smoother.cc
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* \brief Class that implements an exponential smoother
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* \authors Carles Fernandez, 2019 cfernandez@cttc.es
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*
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* Class that implements a first-order exponential smoother.
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*
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* -------------------------------------------------------------------------
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*
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* Copyright (C) 2010-2019 (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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#include "exponential_smoother.h"
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#include <iostream> ///////////////
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#include <iterator>
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#include <numeric>
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Exponential_Smoother::Exponential_Smoother()
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{
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alpha_ = 0.001;
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old_value_ = 0.0;
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one_minus_alpha_ = 1.0 - alpha_;
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samples_for_initialization_ = 500;
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initializing_ = true;
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init_counter_ = 0;
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min_value_ = 25;
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offset_ = 9.0;
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init_buffer_.reserve(samples_for_initialization_);
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}
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Exponential_Smoother::~Exponential_Smoother() = default;
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void Exponential_Smoother::set_alpha(float alpha)
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{
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alpha_ = alpha;
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if (alpha_ < 0)
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{
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alpha_ = 0;
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}
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if (alpha_ > 1)
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{
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alpha_ = 1;
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}
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one_minus_alpha_ = 1.0 - alpha_;
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}
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void Exponential_Smoother::set_offset(float offset)
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{
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offset_ = offset;
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}
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void Exponential_Smoother::set_samples_for_initialization(int num_samples)
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{
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int ns = num_samples;
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if (ns <= 0)
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{
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ns = 1;
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}
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samples_for_initialization_ = num_samples;
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init_buffer_.reserve(samples_for_initialization_);
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}
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void Exponential_Smoother::reset()
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{
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initializing_ = true;
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init_counter_ = 0;
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init_buffer_.clear();
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}
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void Exponential_Smoother::set_min_value(float value)
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{
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min_value_ = value;
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}
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double Exponential_Smoother::smooth(double raw)
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{
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float raw_f = static_cast<float>(raw);
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double smooth_d = static_cast<double>((this)->smooth(raw_f));
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return smooth_d;
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}
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float Exponential_Smoother::smooth(float raw)
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{
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float smoothed_value;
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if (initializing_ == true)
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{
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init_counter_++;
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smoothed_value = raw;
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init_buffer_.push_back(smoothed_value);
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if (init_counter_ == samples_for_initialization_)
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{
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old_value_ = std::accumulate(std::begin(init_buffer_), std::end(init_buffer_), 0.0) / static_cast<float>(init_buffer_.size());
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if (old_value_ < (min_value_ + offset_))
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{
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// flush buffer and start again
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init_counter_ = 0;
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init_buffer_.clear();
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}
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else
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{
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initializing_ = false;
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}
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}
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}
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else
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{
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smoothed_value = alpha_ * raw + one_minus_alpha_ * old_value_;
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old_value_ = smoothed_value;
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}
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return smoothed_value;
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}
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