文件名称:kalman_filter
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OPTIONAL INPUTS (string/value pairs [default in brackets])
model - model(t)=m means use params from model m at time t [ones(1,T) ]
In this case, all the above matrices take an additional final dimension,
i.e., A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m).
However, init_x and init_V are independent of model(1).
u - u(:,t) the control signal at time t [ [] ]
B - B(:,:,m) the input regression matrix for model m
-OPTIONAL INPUTS (string/value pairs [default in brackets])
model - model(t)=m means use params from model m at time t [ones(1,T) ]
In this case, all the above matrices take an additional final dimension,
i.e., A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m).
However, init_x and init_V are independent of model(1).
u - u(:,t) the control signal at time t [ [] ]
B - B(:,:,m) the input regression matrix for model m
model - model(t)=m means use params from model m at time t [ones(1,T) ]
In this case, all the above matrices take an additional final dimension,
i.e., A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m).
However, init_x and init_V are independent of model(1).
u - u(:,t) the control signal at time t [ [] ]
B - B(:,:,m) the input regression matrix for model m
-OPTIONAL INPUTS (string/value pairs [default in brackets])
model - model(t)=m means use params from model m at time t [ones(1,T) ]
In this case, all the above matrices take an additional final dimension,
i.e., A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m).
However, init_x and init_V are independent of model(1).
u - u(:,t) the control signal at time t [ [] ]
B - B(:,:,m) the input regression matrix for model m
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kalman_filter.m