mirror of https://github.com/ArduPilot/ardupilot
72 lines
2.4 KiB
Mathematica
72 lines
2.4 KiB
Mathematica
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function [...
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quat, ... % quaternion state vector after fusion of measurements
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states, ... % state vector after fusion of measurements
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tiltErr, ... % angle error
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P, ... % state covariance matrix after fusion of corrections
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innovation,... % NED velocity innovations (m/s)
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varInnov] ... % NED velocity innovation variance ((m/s)^2)
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= FuseVelocity( ...
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quat, ... % predicted quaternion states from the INS
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states, ... % predicted states from the INS
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P, ... % predicted covariance
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measVel) % NED velocity measurements (m/s)
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R_OBS = 0.5^2;
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innovation = zeros(1,3);
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varInnov = zeros(1,3);
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% Fuse measurements sequentially
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angErrVec = [0;0;0];
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for obsIndex = 1:3
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stateIndex = 3 + obsIndex;
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% Calculate the velocity measurement innovation
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innovation(obsIndex) = states(stateIndex) - measVel(obsIndex);
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% Calculate the Kalman Gain taking advantage of direct state observation
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H = zeros(1,9);
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H(1,stateIndex) = 1;
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varInnov(obsIndex) = P(stateIndex,stateIndex) + R_OBS;
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K = P(:,stateIndex)/varInnov(obsIndex);
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% Calculate state corrections
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xk = K * innovation(obsIndex);
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% Apply the state corrections
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states(1:3) = 0;
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states = states - xk;
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% Store tilt error estimate for external monitoring
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angErrVec = angErrVec + states(1:3);
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% the first 3 states represent the angular misalignment vector. This is
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% is used to correct the estimated quaternion
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% Convert the error rotation vector to its equivalent quaternion
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% truth = estimate + error
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rotationMag = sqrt(states(1)^2 + states(2)^2 + states(3)^2);
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if rotationMag > 1e-12
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deltaQuat = [cos(0.5*rotationMag); [states(1);states(2);states(3)]/rotationMag*sin(0.5*rotationMag)];
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% Update the quaternion states by rotating from the previous attitude through
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% the error quaternion
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quat = QuatMult(quat,deltaQuat);
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% re-normalise the quaternion
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quatMag = sqrt(quat(1)^2 + quat(2)^2 + quat(3)^2 + quat(4)^2);
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quat = quat / quatMag;
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end
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% Update the covariance
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P = P - K*P(stateIndex,:);
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% Force symmetry on the covariance matrix to prevent ill-conditioning
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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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tiltErr = sqrt(dot(angErrVec(1:2),angErrVec(1:2)));
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end
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