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ADD: matlab VTL models with d_clk_drift
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miguekf
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33bd8d4937
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238
src/utils/matlab/vtl/kf_prototype_clk_d.m
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238
src/utils/matlab/vtl/kf_prototype_clk_d.m
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%% vtl KF
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%%
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sat_number=5;
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%% ################## Kalman filter initialization ######################################
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st_nmbr=9;
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% covariances (static)
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kf_P_x = eye(st_nmbr); %TODO: use a real value.
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kf_x = zeros(st_nmbr, 1);
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kf_R = zeros(2*sat_number, 2*sat_number);
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kf_dt=0.1;
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% kf_F = eye(st_nmbr, st_nmbr);
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% kf_F(1, 4) = kf_dt;
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% kf_F(2, 5) = kf_dt;
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% kf_F(3, 6) = kf_dt;
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% kf_F(7, 8) = kf_dt;
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kf_F=[ 1 0 0 kf_dt 0 0 0 0 0
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0 1 0 0 kf_dt 0 0 0 0
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0 0 1 0 0 kf_dt 0 0 0
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0 0 0 1 0 0 0 0 0
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0 0 0 0 1 0 0 0 0
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0 0 0 0 0 1 0 0 0
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0 0 0 0 0 0 1 kf_dt kf_dt^2/2
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0 0 0 0 0 0 0 1 kf_dt
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0 0 0 0 0 0 0 0 1];
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% kf_F=[ 1 0 0 kf_dt 0 0 0 0
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% 0 1 0 0 kf_dt 0 0 0
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% 0 0 1 0 0 kf_dt 0 0
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% 0 0 0 1 0 0 0 0
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% 0 0 0 0 1 0 0 0
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% 0 0 0 0 0 1 0 0
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% 0 0 0 0 0 0 1 kf_dt
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% 0 0 0 0 0 0 0 1];
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kf_H = zeros(2*sat_number,st_nmbr);
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kf_y = zeros(2*sat_number, 1);
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kf_yerr = zeros(2*sat_number, 1);
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% kf_xerr = zeros(8, 1);
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kf_S = zeros(2*sat_number, 2*sat_number); % kf_P_y innovation covariance matrix
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%% pre-allocate for speed
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d = zeros(sat_number, 1);
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rho_pri = zeros(sat_number, 1);
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rhoDot_pri = zeros(sat_number, 1);
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a_x = zeros(sat_number, 1);
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a_y = zeros(sat_number, 1);
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a_z = zeros(sat_number, 1);
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c_pr_m=zeros(sat_number,length(navSolution.RX_time));
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pr_m_filt=zeros(sat_number,length(navSolution.RX_time));
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rhoDot_pri_filt=zeros(sat_number,length(navSolution.RX_time));
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sat_dopp_hz_filt=zeros(sat_number,length(navSolution.RX_time));
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%% ################## Kalman Tracking ######################################
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% receiver solution from rtklib_solver
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for t=2:length(navSolution.RX_time)
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%% State error Covariance Matrix Q (PVT) and R (MEASUREMENTS)
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if (t<3)
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%% State error Covariance Matrix Q (PVT)
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kf_Q = eye(st_nmbr);%new_data.rx_pvt_var(i); %careful, values for V and T could not be adecuate.
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%%
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% Measurement error Covariance Matrix R assembling
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kf_R = blkdiag(eye(sat_number)*40,eye(sat_number)*10);
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else
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kf_Q = blkdiag(eye(3)*pos_var,eye(3)*vel_var,clk_bias_var,clk_drift_var,clk_d_drift_var);
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kf_R = blkdiag(eye(sat_number)*pr_var,eye(sat_number)*pr_dot_var);
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end
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clear x_u y_u z_u xDot_u yDot_u zDot_u cdeltatDot_u cdeltatDot_u_g cdeltat_u_g
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if (t<3)
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kf_x(1,t) = navSolution.X(t);
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kf_x(2,t) = navSolution.Y(t);
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kf_x(3,t) = navSolution.Z(t);
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kf_x(4,t) = navSolution.vX(t);
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kf_x(5,t) = navSolution.vY(t);
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kf_x(6,t) = navSolution.vZ(t);
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kf_x(7,t) = clk_bias_s(t)*SPEED_OF_LIGHT_M_S;
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kf_x(8,t) = clk_drift(t)*SPEED_OF_LIGHT_M_S;%new_data.rx_dts(1);
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kf_x(9,t) = 0;%new_data.rx_dts(1);
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x_u=kf_x(1,t);
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y_u=kf_x(2,t);
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z_u=kf_x(3,t);
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xDot_u=kf_x(4,t);
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yDot_u=kf_x(5,t);
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zDot_u=kf_x(6,t);
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cdeltat_u(t)=kf_x(7,t);
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cdeltatDot_u(t)=kf_x(8,t);
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d_cdeltatDot_u(t)=kf_x(9,t);
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% Kalman state prediction (time update)
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kf_P_x = eye(st_nmbr)*100; %TODO: use a real value.
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kf_xpri(:,t) = kf_F * (kf_x(:,t)-kf_x(:,t-1));% state prediction
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kf_P_x= kf_F * kf_P_x * kf_F' + kf_Q;% state error covariance prediction
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else
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x_u=corr_kf_state(1,t-1);
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y_u=corr_kf_state(2,t-1);
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z_u=corr_kf_state(3,t-1);
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xDot_u=corr_kf_state(4,t-1);
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yDot_u=corr_kf_state(5,t-1);
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zDot_u=corr_kf_state(6,t-1);
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cdeltat_u(t)=corr_kf_state(7,t-1);
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cdeltatDot_u(t)=corr_kf_state(8,t-1);
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d_cdeltatDot_u(t)=corr_kf_state(9,t-1);
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% Kalman state prediction (time update)
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kf_P_x= kf_F * kf_P_x * kf_F' + kf_Q;% state error covariance prediction
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end
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for chan=1:5 %neccesary quantities
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d(chan)=(sat_posX_m(chan,t)-x_u)^2;
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d(chan)=d(chan)+(sat_posY_m(chan,t)-y_u)^2;
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d(chan)=d(chan)+(sat_posZ_m(chan,t)-z_u)^2;
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d(chan)=sqrt(d(chan));
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c_pr_m(chan,t)=d(chan)+cdeltat_u(t);
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a_x(chan,t)=-(sat_posX_m(chan,t)-x_u)/d(chan);
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a_y(chan,t)=-(sat_posY_m(chan,t)-y_u)/d(chan);
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a_z(chan,t)=-(sat_posZ_m(chan,t)-z_u)/d(chan);
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rhoDot_pri(chan,t)=(sat_velX(chan,t)-xDot_u)*a_x(chan,t)...
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+(sat_velY(chan,t)-yDot_u)*a_y(chan,t)...
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+(sat_velZ(chan,t)-zDot_u)*a_z(chan,t);
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end
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for chan=1:5 % Measurement matrix H assembling
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% It has st_nmbr columns (st_nmbr states) and 2*NSat rows (NSat psudorange error;NSat pseudo range rate error)
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kf_H(chan, 1) = a_x(chan,t); kf_H(chan, 2) = a_y(chan,t); kf_H(chan, 3) = a_z(chan,t); kf_H(chan, 7) = 1.0;
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kf_H(chan+sat_number, 4) = a_x(chan,t); kf_H(chan+sat_number, 5) = a_y(chan,t); kf_H(chan+sat_number, 6) = a_z(chan,t); kf_H(chan+sat_number, 8) = 1.0;
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kf_H(chan+sat_number, 9) = kf_dt;
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end
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% unobsv(t) = length(kf_F) - rank(obsv(kf_F,kf_H));
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% !!!! Limitaciones
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% obsv no se recomienda para el diseño de control, ya que calcular el rango de la matriz de observabilidad
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% no se recomienda para las pruebas de observabilidad. Ob será numéricamente singular para la mayoría de los
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% sistemas con más de unos cuantos estados. Este hecho está bien documentado en la sección III de [1].
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% Kalman estimation (measurement update)
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for chan=1:5 % Measurement matrix H assembling
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kf_yerr(chan,t)=c_pr_m(chan,t)-sat_prg_m(chan,t);
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kf_yerr(chan+sat_number,t)=(sat_dopp_hz(chan,t)*Lambda_GPS_L1+cdeltatDot_u(t)+kf_dt*d_cdeltatDot_u(t))-rhoDot_pri(chan,t);
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end
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% DOUBLES DIFFERENCES
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% kf_yerr = zeros(2*sat_number, 1);
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% for (int32_t i = 1; i < sat_number; i++) % Measurement vector
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% {
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% kf_y(i)=new_data.pr_m(i)-new_data.pr_m(i-1);
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% kf_yerr(i)=kf_y(i)-(rho_pri(i)+rho_pri(i-1));
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% kf_y(i+sat_number)=(rhoDot_pri(i)-rhoDot_pri(i-1))/Lambda_GPS_L1;
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% kf_yerr(i+sat_number)=kf_y(i+sat_number)-(new_data.doppler_hz(i)-new_data.doppler_hz(i-1));
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% }
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% kf_yerr.print("DOUBLES DIFFERENCES");
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% Kalman filter update step
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kf_S = kf_H * kf_P_x* kf_H' + kf_R; % innovation covariance matrix (S)
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kf_K = (kf_P_x * kf_H') * inv(kf_S); % Kalman gain
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kf_xerr(:,t) = kf_K * (kf_yerr(:,t)); % Error state estimation
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kf_P_x = (eye(length(kf_P_x)) - kf_K * kf_H) * kf_P_x; % update state estimation error covariance matrix
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if (t<3)
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% kf_xerr(9,t)=15;
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corr_kf_state(:,t)=kf_xpri(:,t)-kf_xerr(:,t)+kf_x(:,t); %updated state estimation
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else
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corr_kf_state(:,t)=corr_kf_state(:,t-1)-kf_xerr(:,t); %updated state estimation
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end
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%% ################## Geometric Transformation ######################################
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x_u=corr_kf_state(1,t);
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y_u=corr_kf_state(2,t);
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z_u=corr_kf_state(3,t);
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xDot_u=corr_kf_state(4,t);
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yDot_u=corr_kf_state(5,t);
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zDot_u=corr_kf_state(6,t);
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cdeltat_u_g=corr_kf_state(7,t);
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cdeltatDot_u_g=corr_kf_state(8,t);
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d_cdeltatDot_u_g=corr_kf_state(9,t);
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% for chan=1:5 %neccesary quantities
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% d(chan)=(sat_posX_m(chan,t)-x_u)^2;
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% d(chan)=d(chan)+(sat_posY_m(chan,t)-y_u)^2;
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% d(chan)=d(chan)+(sat_posZ_m(chan,t)-z_u)^2;
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% d(chan)=sqrt(d(chan));
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%
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% c_pr_m(chan,t)=d(chan)+cdeltat_u_g;
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%
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% a_x(chan,t)=-(sat_posX_m(chan,t)-x_u)/d(chan);
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% a_y(chan,t)=-(sat_posY_m(chan,t)-y_u)/d(chan);
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% a_z(chan,t)=-(sat_posZ_m(chan,t)-z_u)/d(chan);
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%
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% rhoDot_pri(chan,t)=(sat_velX(chan,t)-xDot_u)*a_x(chan,t)...
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% +(sat_velY(chan,t)-yDot_u)*a_y(chan,t)...
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% +(sat_velZ(chan,t)-zDot_u)*a_z(chan,t);
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% end
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kf_H = zeros(2*sat_number,st_nmbr);
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for chan=1:5 % Measurement matrix H assembling
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% It has st_nmbr columns (st_nmbr states) and 2*NSat rows (NSat psudorange error;NSat pseudo range rate error)
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kf_H(chan, 1) = a_x(chan,t); kf_H(chan, 2) = a_y(chan,t); kf_H(chan, 3) = a_z(chan,t); kf_H(chan, 7) = 1.0;
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kf_H(chan+sat_number, 4) = a_x(chan,t); kf_H(chan+sat_number, 5) = a_y(chan,t); kf_H(chan+sat_number, 6) = a_z(chan,t); kf_H(chan+sat_number, 8) = 1.0;
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kf_H(chan+sat_number, 9) = kf_dt;
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end
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% Re-calculate error measurement vector with the most recent data available: kf_delta_y=kf_H*kf_delta_x
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kf_yerr_g=kf_H*kf_xerr;
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% Filtered pseudorange error measurement (in m) AND Filtered Doppler shift measurements (in Hz):
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for chan=1:5 % Measurement vector
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pr_m_filt(chan,t)=sat_prg_m(chan,t)+kf_yerr_g(chan,t);% now filtered
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rhoDot_pri_filt(chan,t)=(sat_dopp_hz(chan,t)*Lambda_GPS_L1+corr_kf_state(8,t)+kf_dt*corr_kf_state(9,t))-kf_yerr_g(chan+sat_number,t);
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%convert rhoDot_pri to doppler shift!
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% d_dt_clk_drift=(corr_kf_state(8,t)-corr_kf_state(8,t-1));
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if (t<3)
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sat_dopp_hz_filt(chan,t)=(rhoDot_pri_filt(chan,t)-corr_kf_state(8,t)-corr_kf_state(9,t)*kf_dt)/Lambda_GPS_L1;
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else
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sat_dopp_hz_filt(chan,t)=(rhoDot_pri_filt(chan,t)-corr_kf_state(8,t)-corr_kf_state(9,t)*kf_dt)/Lambda_GPS_L1;
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end
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% carrier_phase_rads = 0;
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carrier_freq_hz =GPS_L1_freq_hz+sat_dopp_hz_filt(chan,t);
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% carrier_freq_rate_hz_s = 0;
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% code_phase_chips = 0;
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end
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% carrier_phase_rads = 0;
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% carrier_freq_hz = 0;
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% carrier_freq_rate_hz_s = 0;
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% code_phase_chips = 0;
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end
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102
src/utils/matlab/vtl/vtl_prototype_clk_d_drift.m
Normal file
102
src/utils/matlab/vtl/vtl_prototype_clk_d_drift.m
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% VTL prototype
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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) 2010-2019 (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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%%
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clc
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close all
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clearvars
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% if ~exist('gps_l1_ca_read_pvt_raw_dump.m', 'file')
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% addpath('./libs')
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% end
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%
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% if ~exist('cat2geo.m', 'file')
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% addpath('./libs/geoFunctions')
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% end
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SPEED_OF_LIGHT_M_S=299792458.0;
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Lambda_GPS_L1=0.1902937;
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GPS_L1_freq_hz=1575.42e6;
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% point_of_closure=4118;%4060;
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%% ==================== VARIANCES =============================
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pos_var=100;%m^2
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vel_var=10;%m^2/s^2
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clk_bias_var=100;%m^2
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clk_drift_var=40;%m^2/s^2
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clk_d_drift_var=1;
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pr_var=20;%m^2
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pr_dot_var=2;%m^2/s^2
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% CARLES PAPER LTE GNSS VTL
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% pos_var=2;%m^2
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% vel_var=0.2;%m^2/s^2
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% clk_bias_var=1e-7;%m^2
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% clk_drift_var=1e-4;%m^2/s^2
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% pr_var=20;%m^2
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% pr_dot_var=3;%m^2/s^2
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%% ============================================================
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samplingFreq=5000000;
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channels=6;
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TTFF_sec=41.48;
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spirent_index_TTFF=416;
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plot_skyplot=0;
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plot_reference=1;
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load_observables=1;
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%% ============================ PARSER TO STRUCT ============================
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navSolution = GnssSDR2struct('PVT_raw.mat');
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refSolution = SpirentMotion2struct('..\log_spirent\motion_V1_SPF_LD_05.csv');
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%
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load observables\observables_raw.mat
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% refSatData = SpirentSatData2struct('..\log_spirent\sat_data_V1A1_SPF_LD_05.csv');
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rx_PRN=[28 4 17 15 27 9]; % for SPF_LD_05.
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load('PVT_raw.mat','sat_posX_m','sat_posY_m','sat_posZ_m','sat_velX','sat_velY'...
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,'sat_velZ','sat_prg_m','clk_bias_s','clk_drift','sat_dopp_hz','user_clk_offset')
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if(load_observables)
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load observables\observables_raw.mat
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refSatData = SpirentSatData2struct('..\log_spirent\sat_data_V1A1_SPF_LD_05.csv');
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end
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%%
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% vtlSolution = Vtl2struct('dump_vtl_file.csv');
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%%
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kf_prototype_clk_d
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%% ====== FILTERING =======================================================
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% moving_avg_factor= 500;
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% LAT_FILT = movmean(navSolution.latitude,moving_avg_factor);
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% LON_FILT = movmean(navSolution.longitude,moving_avg_factor);
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% HEIGH_FILT = movmean(navSolution.height,moving_avg_factor);
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%
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% X_FILT = movmean(navSolution.X,moving_avg_factor);
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% Y_FILT = movmean(navSolution.Y,moving_avg_factor);
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% Z_FILT = movmean(navSolution.Z,moving_avg_factor);
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%
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% vX_FILT = movmean(navSolution.vX,moving_avg_factor);
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% vY_FILT = movmean(navSolution.vY,moving_avg_factor);
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% vZ_FILT = movmean(navSolution.vZ,moving_avg_factor);
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%
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%%
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%general_raw_plot
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vtl_general_plot
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%% ============================================== ==============================================
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%%
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figure;plot(navSolution.RX_time-navSolution.RX_time(1),kf_yerr(1:5,:)');title('c pr m-error');xlabel('t U.A');ylabel('pr m [m]');grid minor
|
||||
legend('PRN 28','PRN 4','PRN 17','PRN 15','PRN 27','Location','eastoutside')
|
||||
figure;plot(navSolution.RX_time-navSolution.RX_time(1),kf_yerr(6:10,:)');title('c pr m DOT-error');xlabel('t U.A');ylabel('pr m dot [m/s]');grid minor
|
||||
legend('PRN 28','PRN 4','PRN 17','PRN 15','PRN 27','Location','eastoutside')
|
||||
|
||||
%%
|
||||
figure;plot(navSolution.RX_time-navSolution.RX_time(1),kf_xerr(9,:)');title('kf 9 xerr');xlabel('t U.A');ylabel('kf 9 xerr');grid minor
|
||||
Loading…
Reference in New Issue
Block a user