g13edc performs a combined measurement and time update of one iteration of the time-invariant Kalman filter. The method employed for this update is the square root information filter with the system matrices in condensed controller Hessenberg form.
The function may be called by the names: g13edc, nag_tsa_kalman_sqrt_filt_info_invar or nag_kalman_sqrt_filt_info_invar.
3Description
For the state space system defined by
the estimate of given observations to is denoted by with (where , and are time invariant). The function performs one recursion of the square root information filter algorithm, summarised as follows:
where is an orthogonal transformation triangularizing the pre-array, and the matrix pair is in upper controller Hessenberg form. The triangularization is done entirely via Householder transformations exploiting the zero pattern of the pre-array. An example of the pre-array is given below (where , and ):
The term is the mean process noise, and is the estimated error at instant . The inverse of the state covariance matrix is factored as follows
where ( is lower). The new state filtered state estimate is computed via
The function returns and, optionally, (see the Introduction to Chapter G13 for more information concerning the information filter).
4References
Anderson B D O and Moore J B (1979) Optimal Filtering Prentice–Hall
Vanbegin M, van Dooren P and Verhaegen M H G (1989) Algorithm 675: FORTRAN subroutines for computing the square root covariance filter and square root information filter in dense or Hessenberg forms ACM Trans. Math. Software15 243–256
van Dooren P and Verhaegen M H G (1988) Condensed forms for efficient time-invariant Kalman filtering SIAM J. Sci. Stat. Comput.9 516–530
Verhaegen M H G and van Dooren P (1986) Numerical aspects of different Kalman filter implementations IEEE Trans. Auto. Contr.AC-31 907–917
5Arguments
1: – IntegerInput
On entry: the actual state dimension, , i.e., the order of the matrices and .
Constraint:
.
2: – IntegerInput
On entry: the actual input dimension, , i.e., the order of the matrix .
Constraint:
.
3: – IntegerInput
On entry: the actual output dimension, , i.e., the order of the matrix .
Constraint:
.
4: – doubleInput/Output
Note: the th element of the matrix is stored in .
On entry: the leading upper triangular part of this array must contain the square root of the inverse of the state covariance matrix .
On exit: the leading upper triangular part of this array contains , the square root of the inverse of the of the state covariance matrix .
5: – IntegerInput
On entry: the stride separating matrix column elements in the array t.
Constraint:
.
6: – const doubleInput
Note: the th element of the matrix is stored in .
On entry: the leading part of this array must contain the upper controller Hessenberg matrix . Where is the inverse of the state transition matrix, and is the unitary matrix generated by the function g13exc.
7: – IntegerInput
On entry: the stride separating matrix column elements in the array ainv.
Constraint:
.
8: – const doubleInput
Note: the th element of the matrix is stored in .
On entry: the leading part of this array must contain the upper controller Hessenberg matrix . Where is the inverse of the transition matrix, is the input weight matrix , and is the unitary transformation generated by the function g13exc.
9: – IntegerInput
On entry: the stride separating matrix column elements in the array ainvb.
Constraint:
.
10: – const doubleInput
Note: the th element of the matrix is stored in .
On entry: if the noise covariance matrix is to be supplied separately from the output weight matrix, then the leading upper triangular part of this array must contain the right Cholesky factor of the inverse of the measurement noise covariance matrix. If this information is not to be input separately from the output weight matrix c then the array rinv must be set to NULL.
11: – IntegerInput
On entry: the stride separating matrix column elements in the array rinv.
On entry: if the array argument rinv (above) has been defined then the leading part of this array must contain the matrix , otherwise (if rinv is NULL then the leading part of the array must contain the matrix . is the output weight matrix, is the noise covariance matrix and is the same unitary transformation used for defining array arguments ainv and ainvb.
13: – IntegerInput
On entry: the stride separating matrix column elements in the array c.
Constraint:
.
14: – const doubleInput
Note: the th element of the matrix is stored in .
On entry: the leading upper triangular part of this array must contain the right Cholesky factor of the inverse of the process noise covariance matrix.
15: – IntegerInput
On entry: the stride separating matrix column elements in the array qinv.
Constraint:
.
16: – doubleInput/Output
On entry: this array must contain the estimated state
On exit: this array contains the estimated state .
17: – const doubleInput
On entry: this array must contain , the product of the upper triangular matrix and the measured output vector .
18: – const doubleInput
On entry: this array must contain , the mean value of the state process noise.
19: – doubleInput
On entry: tol is used to test for near singularity of the matrix . If you set tol to be less than then the tolerance is taken as , where is the machine precision.
20: – NagError *Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).
6Error Indicators and Warnings
NE_2_INT_ARG_LT
On entry, while . These arguments must satisfy .
On entry while . These arguments must satisfy .
On entry while . These arguments must satisfy .
On entry while . These arguments must satisfy .
On entry while . These arguments must satisfy .
On entry while . These arguments must satisfy .
NE_ALLOC_FAIL
Dynamic memory allocation failed.
NE_INT_ARG_LT
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry, .
Constraint: .
NE_MAT_SINGULAR
The matrix inverse(S) is singular.
7Accuracy
The use of the square root algorithm improves the stability of the computations.
8Parallelism and Performance
Background information to multithreading can be found in the Multithreading documentation.
g13edc makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.
For this function two examples are presented. There is a single example program for g13edc, with a main program and the code to solve the two example problems is given in the functions ex1 and ex2.
Example 1 (ex1)
To apply three iterations of the Kalman filter (in square root information form) to the time-invariant system supplied in upper controller Hessenberg form.
Example 2 ex2)
To apply three iterations of the Kalman filter (in square root information form) to the general time-invariant system . The use of the time-varying Kalman function g13ecc is compared with that of the time-invariant function g13edc. The same original data is used by both functions but additional transformations are required before it can be supplied to g13edc. It can be seen that (after the appropriate back-transformations on the output of g13edc) the results of both g13ecc and g13edc are in agreeement.