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mod: cleaning and formating

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This commit is contained in:
M.A.Gomez 2022-10-25 19:15:28 +00:00
parent d03d94116f
commit 86ad760015
3 changed files with 25 additions and 24 deletions
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4
src/algorithms/PVT/libs/rtklib_solver.cc
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@ -1117,11 +1117,11 @@ bool Rtklib_Solver::get_PVT(const std::map<int, Gnss_Synchro> &gnss_observables_
new_vtl_data.sat_health_flag(n) = svh.at(n);
new_vtl_data.sat_CN0_dB_hz(n) = d_obs_data.at(n).SNR[0];
// TODO: first version of VTL works only with ONE frequency band (band #0 is L1)
//new_vtl_data.pr_m(n) = d_obs_data.at(n).P[0]; //RAW pseudoranges
new_vtl_data.pr_m(n) = d_obs_data.at(n).P[0]; //RAW pseudoranges
//To.Do: check it VTL uses all the information as in rtklib rescode function: v[nv] = P - (r + dtr - SPEED_OF_LIGHT_M_S * dts[i * 2] + dion + dtrp);
//corrected pr with code bias, iono and tropo. Still needs the dtr(rx clock bias) and satellite clock bias (dts)
//cout<<"dtr "<<rx_position_and_time[3]*SPEED_OF_LIGHT_M_S<<"m";
new_vtl_data.pr_m(n) = pr_corrected_code_bias_vec[n] - tropo_vec[n] - iono_vec[n]+SPEED_OF_LIGHT_M_S * dts[n * 2];
//new_vtl_data.pr_m(n) = pr_corrected_code_bias_vec[n] - tropo_vec[n] - iono_vec[n]+SPEED_OF_LIGHT_M_S * dts[n * 2];
new_vtl_data.doppler_hz(n) = d_obs_data.at(n).D[0];
new_vtl_data.carrier_phase_rads(n) = d_obs_data.at(n).L[0];
}

2
src/algorithms/PVT/libs/vtl_data.h
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@ -16,6 +16,8 @@
#ifndef GNSS_SDR_VTL_DATA_H
#define GNSS_SDR_VTL_DATA_H
// constants definition
#define Lambda_GPS_L1 0.1902937
#include <armadillo>
#include <cstdint>

39
src/algorithms/PVT/libs/vtl_engine.cc
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@ -73,14 +73,12 @@ bool Vtl_Engine::vtl_loop(Vtl_Data new_data)
kf_Q(i, i) = new_data.rx_pvt_var(i); //careful, values for V and T could not be adecuate.
}
// // Kalman state prediction (time update)
// Kalman state prediction (time update)
kf_x.print(" KF RTKlib STATE");
new_data.kf_state=kf_x;
kf_x = kf_F * kf_x; // state prediction
kf_P_x= kf_F * kf_P_x * kf_F.t() + kf_Q; // state error covariance prediction
// cout << " KF priori STATE diference" << kf_x-new_data.kf_state;
//from error state variables to variables
kf_xerr=kf_x-new_data.kf_state;
// From state variables definition
// TODO: cast to type properly
x_u=kf_x(0);
@ -133,37 +131,38 @@ bool Vtl_Engine::vtl_loop(Vtl_Data new_data)
// Kalman estimation (measurement update)
for (int32_t i = 0; i < new_data.sat_number; i++) // Measurement vector
{
//kf_y(i) = delta_rho(i); // i-Satellite
//kf_y(i+new_data.sat_number) = delta_rhoDot(i); // i-Satellite
kf_y(i)=new_data.pr_m(i);
kf_yerr(i)=kf_y(i)-rho_pri(i);//-0.000157*SPEED_OF_LIGHT_M_S;
float Lambda_GPS_L1=0.1902937;
kf_y(i+new_data.sat_number)=(rhoDot_pri(i))/Lambda_GPS_L1;
kf_yerr(i+new_data.sat_number)=kf_y(i+new_data.sat_number)-new_data.doppler_hz(i);
//rhoDot_pri(i)=(rhoDot_pri(i))/Lambda_GPS_L1;
//kf_y(i) = new_data.pr_m(i); // i-Satellite
//kf_y(i+new_data.sat_number) = rhoDot_pri(i)/Lambda_GPS_L1; // i-Satellite
kf_yerr(i)=new_data.pr_m(i)-rho_pri(i);//-0.000157*SPEED_OF_LIGHT_M_S;
kf_yerr(i+new_data.sat_number)=rhoDot_pri(i)/Lambda_GPS_L1-new_data.doppler_hz(i);
}
//cout << " KF measurement vector difference" << kf_yerr;
//rhoDot_pri.print("DOPPLER stimated [Hz]");
kf_yerr.print("KF measurement vector difference");
// DOUBLES DIFFERENCES
// kf_yerr = arma::zeros(2*new_data.sat_number, 1);
// for (int32_t i = 1; i < new_data.sat_number; i++) // Measurement vector
// {
// kf_y(i)=new_data.pr_m(i)-new_data.pr_m(i-1);
// kf_yerr(i)=kf_y(i)-(rho_pri(i)+rho_pri(i-1));
// kf_y(i+new_data.sat_number)=(rhoDot_pri(i)-rhoDot_pri(i-1))/Lambda_GPS_L1;
// kf_yerr(i+new_data.sat_number)=kf_y(i+new_data.sat_number)-(new_data.doppler_hz(i)-new_data.doppler_hz(i-1));
// }
// kf_yerr.print("DOUBLES DIFFERENCES");
for (int32_t i = 0; i < new_data.sat_number; i++) // Measurement error Covariance Matrix R assembling
{
// It is diagonal 2*NSatellite x 2*NSatellite (NSat psudorange error;NSat pseudo range rate error)
kf_R(i, i) = 0.1; //TODO: use a valid value.
kf_R(i, i) = 0.1; //TODO: fill with real values.
kf_R(i+new_data.sat_number, i+new_data.sat_number) = 1.0;
}
// Kalman filter update step
kf_S = kf_H * kf_P_x* kf_H.t() + kf_R; // innovation covariance matrix (S)
kf_K = (kf_P_x * kf_H.t()) * arma::inv(kf_S); // Kalman gain
//cout << " KF measures ERROR" << kf_yerr;
kf_x = kf_x + kf_K * (kf_yerr); // updated state estimation
kf_P_x = (arma::eye(size(kf_P_x)) - kf_K * kf_H) * kf_P_x; // update state estimation error covariance matrix
//cout << " KF posteriori STATE" << kf_x;
//cout << " KF posteriori STATE diference" << kf_x-new_data.kf_state;
cout << " KF posteriori STATE diference" << kf_x-new_data.kf_state;
// // ################## Geometric Transformation ######################################