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Update the altitude KF to use the updated measurements appropriately …
…and check in the relevant matlab code to check it and genererate it. Produces quite smooth traces.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,108 @@ | ||
| baro = fixTime(BaroAltitude); | ||
| accel = fixTime(Accels); | ||
| attitude = fixTime(AttitudeActual); | ||
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| Gamma = diag([1e-5 1e-5 0.00001 1e-20]); % state noise | ||
| accel_sigma = 1; | ||
| baro_sigma = 1; | ||
| Nu = diag([10 10 10 10]); | ||
| z = zeros(length(accel.z),4); | ||
| Nu_n = zeros([4 4 length(accel.z)]); | ||
| Nu_n(:,:,1) = Nu; | ||
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| t = max(accel.timestamp(1), baro.timestamp(1)); | ||
| last_t = t-1; | ||
| last_accel_idx = 1; | ||
| last_baro_idx = 1; | ||
| i = 1; | ||
| timestamp = []; | ||
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| z(1) = baro.Altitude(1); | ||
| z(1:5,4) = 0.4; | ||
| timestamp(1) = t; | ||
| log_accel = 0; | ||
| while (last_accel_idx + 1) <= length(accel.z) && (last_baro_idx + 1) <= length(baro.Altitude) | ||
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| update_baro = baro.timestamp(last_baro_idx + 1) < t; | ||
| update_accel = accel.timestamp(last_accel_idx + 1) < t; | ||
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| if 0 && update_accel | ||
| [~,idx] = min(abs(attitude.timestamp - accel.timestamp(last_accel_idx+1))); | ||
| Rbe = Quat2Rbe([attitude.q1(idx), attitude.q2(idx), attitude.q3(idx), attitude.q4(idx)]); | ||
| idx = last_accel_idx + 1; | ||
| accel_ned = Rbe * [accel.x(idx); accel.y(idx); accel.z(idx)]; | ||
| accel_ned = accel_ned(3); | ||
| % if(abs(accel_ned) < 1e-1) | ||
| % keyboard | ||
| % end | ||
| else | ||
| accel_ned = accel.z(last_accel_idx + 1); | ||
| end | ||
| log_accel(i) = accel_ned; | ||
| if update_baro && update_accel; | ||
| x = [baro.Altitude(last_baro_idx + 1); -accel_ned-9.81]; | ||
| last_baro_idx = last_baro_idx + 1; | ||
| last_accel_idx = last_accel_idx + 1; | ||
| C = [1 0 0 0; 0 0 1 -1]; | ||
| Sigma = diag([baro_sigma; accel_sigma]); | ||
| elseif update_accel | ||
| x = -accel_ned - 9.81; | ||
| last_accel_idx = last_accel_idx + 1; | ||
| C = [0 0 1 -1]; | ||
| Sigma = [accel_sigma]; | ||
| elseif update_baro | ||
| x = [baro.Altitude(last_baro_idx + 1)]; | ||
| last_baro_idx = last_baro_idx + 1; | ||
| C = [1 0 0 0]; | ||
| Sigma = [baro_sigma]; | ||
| else | ||
| % Take a timestep and look for advance | ||
| t = t + 0.1; | ||
| continue; | ||
| end | ||
| %[last_baro_idx last_accel_idx] | ||
| t = max(baro.timestamp(last_baro_idx), accel.timestamp(last_accel_idx)); | ||
| dT = (t - last_t) / 1000; | ||
| if(dT == 0) | ||
| dT = 1.5 / 1000; | ||
| end | ||
| assert(dT ~= 0,'WTF'); | ||
| last_t = t; | ||
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| i = i + 1; | ||
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| % Zero out non-diag covariance | ||
| Nu = diag(diag(Nu)); | ||
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| A = [1 dT 0 0; 0 1 dT 0; 0 0 1 0; 0 0 0 1]; | ||
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| P = A * Nu * A' + Gamma; | ||
| K = P*C'*(C*P*C'+Sigma)^-1; | ||
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| z(i,:) = A * z(i-1,:)' + K * (x - C * A * z(i-1,:)'); | ||
| timestamp(i) = t; | ||
| Nu = (eye(4) - K * C) * P; | ||
| Nu_n(:,:,i) = Nu; | ||
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| z(i,4) = 0; | ||
| if mod(i,10000) == 0 | ||
| subplot(311) | ||
| plot(baro.timestamp, baro.Altitude, '.', timestamp(1:i),z(1:i,1),'r','LineWidth',5) | ||
| subplot(312) | ||
| plot(timestamp(1:i),z(1:i,2),'k') | ||
| subplot(313) | ||
| plot(timestamp(1:i),z(1:i,3),'k'); | ||
| xlim(timestamp([1,i])) | ||
| drawnow | ||
| end | ||
| end | ||
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| subplot(311) | ||
| plot(baro.timestamp, baro.Altitude, '.', timestamp(1:i),z(1:i,1),'r','LineWidth',5) | ||
| subplot(312) | ||
| plot(timestamp(1:i),z(1:i,2),'k') | ||
| subplot(313) | ||
| plot(timestamp(1:i),z(1:i,3),'k'); | ||
| xlim(timestamp([1,i])) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,61 @@ | ||
| % Generate the symbolic code for the kalman filter on altitude | ||
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| dT = sym('dT','real'); | ||
| A = [1 dT 0; 0 1 dT; 0 0 1]; | ||
| Nu = diag([sym('V[1]') sym('V[2]') sym('V[3]')]); | ||
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| Gamma = diag([sym('G[1]') sym('G[2]') sym('G[3]')]); | ||
| Sigma = diag([sym('S[1]') sym('S[2]')]); | ||
| C = [1 0 0; 0 0 1]; | ||
| state = [sym('z[1]'); sym('z[2]'); sym('z[3]')]; | ||
| measure = [sym('x[1]'); sym('x[2]')]; | ||
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| P = simplify(A * Nu * A' + Gamma); | ||
| K = simplify(P*C'*(C*P*C'+Sigma)^-1); | ||
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| P_mat = [sym('P[1][1]') sym('P[1][2]') sym('P[1][3]'); ... | ||
| sym('P[2][1]') sym('P[2][2]') sym('P[2][3]'); ... | ||
| sym('P[3][1]') sym('P[3][2]') sym('P[3][3]')]; | ||
| K_mat = [sym('K[1][1]') sym('K[1][2]'); ... | ||
| sym('K[2][1]') sym('K[2][2]'); ... | ||
| sym('K[3][1]') sym('K[3][2]')]; | ||
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| z_new = A * state + K_mat * (measure - C * A * state); | ||
| V = (eye(3) - K_mat * C) * P_mat; | ||
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| ccode(P) | ||
| ccode(K) | ||
| ccode(z_new) | ||
| ccode(V) | ||
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| %% For when there is no baro update | ||
| % Generate the symbolic code for the kalman filter on altitude | ||
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| dT = sym('dT','real'); | ||
| A = [1 dT 0; 0 1 dT; 0 0 1]; | ||
| Nu = diag([sym('V[1]') sym('V[2]') sym('V[3]')]); | ||
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| Gamma = diag([sym('G[1]') sym('G[2]') sym('G[3]')]); | ||
| Sigma = sym('S[2]'); | ||
| C = [0 0 1]; | ||
| state = [sym('z[1]'); sym('z[2]'); sym('z[3]')]; | ||
| measure = [sym('x[2]')]; | ||
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| P = simplify(A * Nu * A' + Gamma); | ||
| K = simplify(P*C'*(C*P*C'+Sigma)^-1); | ||
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| P_mat = [sym('P[1][1]') sym('P[1][2]') sym('P[1][3]'); ... | ||
| sym('P[2][1]') sym('P[2][2]') sym('P[2][3]'); ... | ||
| sym('P[3][1]') sym('P[3][2]') sym('P[3][3]')]; | ||
| K_mat = [sym('K[1][1]'); ... | ||
| sym('K[2][1]'); ... | ||
| sym('K[3][1]')]; | ||
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| z_new = A * state + K_mat * (measure - C * A * state); | ||
| V = (eye(3) - K_mat * C) * P_mat; | ||
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| ccode(P) | ||
| ccode(K) | ||
| ccode(z_new) | ||
| ccode(V) |