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- function [...
- states, ... % state vector after fusion of measurements
- P, ... % state covariance matrix after fusion of corrections
- innovation,... % NED velocity innovations (m/s)
- varInnov] ... % NED velocity innovation variance ((m/s)^2)
- = FuseVelocity( ...
- states, ... % predicted states from the INS
- P, ... % predicted covariance
- measVel) % NED velocity measurements (m/s)
- R_OBS = 0.5^2;
- innovation = zeros(1,3);
- varInnov = zeros(1,3);
- % Fuse measurements sequentially
- for obsIndex = 1:3
- stateIndex = 4 + obsIndex;
- % Calculate the velocity measurement innovation
- innovation(obsIndex) = states(stateIndex) - measVel(obsIndex);
-
- % Calculate the Kalman Gain
- H = zeros(1,10);
- H(1,stateIndex) = 1;
- varInnov(obsIndex) = (H*P*transpose(H) + R_OBS);
- K = (P*transpose(H))/varInnov(obsIndex);
-
- % Calculate state corrections
- xk = K * innovation(obsIndex);
-
- % Apply the state corrections
- states = states - xk;
-
- % re-normalise the quaternion
- quatMag = sqrt(states(1)^2 + states(2)^2 + states(3)^2 + states(4)^2);
- states(1:4) = states(1:4) / quatMag;
-
- % Update the covariance
- P = P - K*H*P;
-
- % Force symmetry on the covariance matrix to prevent ill-conditioning
- P = 0.5*(P + transpose(P));
-
- % ensure diagonals are positive
- for i=1:10
- if P(i,i) < 0
- P(i,i) = 0;
- end
- end
-
- end
- end
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