mirror of https://github.com/ArduPilot/ardupilot
60 lines
1.4 KiB
Mathematica
60 lines
1.4 KiB
Mathematica
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function P = PredictCovariance(deltaAngle, ...
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deltaVelocity, ...
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quat, ...
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states,...
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P, ... % Previous state covariance matrix
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dt) ... % IMU and prediction time step
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% Set the filter state process noise (IMU errors are already built into the
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% equations). It includes the process noise required for evolution of the
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% IMU bias errors
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dAngBiasSigma = dt*1E-6;
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processNoise = [0*ones(1,7), dAngBiasSigma*[1 1 1]];
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% Specify the estimated errors on the delta angles and delta velocities
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daxNoise = (dt*25.0/60*pi/180)^2;
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dayNoise = (dt*25.0/60*pi/180)^2;
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dazNoise = (dt*25.0/60*pi/180)^2;
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dvxNoise = (dt*0.5)^2;
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dvyNoise = (dt*0.5)^2;
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dvzNoise = (dt*0.5)^2;
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dvx = deltaVelocity(1);
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dvy = deltaVelocity(2);
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dvz = deltaVelocity(3);
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dax = deltaAngle(1);
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day = deltaAngle(2);
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daz = deltaAngle(3);
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q0 = quat(1);
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q1 = quat(2);
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q2 = quat(3);
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q3 = quat(4);
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dax_b = states(8);
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day_b = states(9);
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daz_b = states(10);
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% Predicted covariance
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F = calcF(dax,dax_b,day,day_b,daz,daz_b,dvx,dvy,dvz,q0,q1,q2,q3);
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Q = calcQ(daxNoise,dayNoise,dazNoise,dvxNoise,dvyNoise,dvzNoise,q0,q1,q2,q3);
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P = F*P*transpose(F) + Q;
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% Add the general process noise
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for i = 1:10
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P(i,i) = P(i,i) + processNoise(i)^2;
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end
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% Force symmetry on the covariance matrix to prevent ill-conditioning
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% of the matrix which would cause the filter to blow-up
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P = 0.5*(P + transpose(P));
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% ensure diagonals are positive
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for i=1:9
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if P(i,i) < 0
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P(i,i) = 0;
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end
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end
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end
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