Perform the time update by advancing the mean and covariance to the time of the next observation
Perform the time update by advancing the mean and covariance to the time of the next observation
the system evolution matrix
the time difference between observations
the posterior mean at the previous time
the diagonal matrix containing the singular values (eigen values) of the posterior covariance matrix at the previous time
the unitary matrix containing the eigen vectors of the posterior covariance matrix at the previous time step
Initialise the state of the SVD Filter
Calculate the square root inverse of a matrix using the Eigenvalue decomposition of a matrix
Calculate the square root inverse of a matrix using the Eigenvalue decomposition of a matrix
a symmetric positive definite matrix
the square root inverse of the matrix
Calculate the square root of a symmetric matrix using eigenvalue decomposition
Calculate the square root of a symmetric matrix using eigenvalue decomposition
a symmetric positive definite matrix
the square root of a matrix
Perform the Kalman Filter by updating the value of the Singular Value Decomp. of the state covariance matrix, C = UDU^T https://arxiv.org/pdf/1611.03686.pdf