Operators |

`update_kalman` — Read an update file of a Kalman filter.

`update_kalman` is obsolete and is only provided for
reasons of backward compatibility.

**update_kalman**( : : *FileName*, *DimensionIn*, *ModelIn*, *MeasurementIn* : *DimensionOut*, *ModelOut*, *MeasurementOut*)

The operator `update_kalman` reads the update file
* FileName* of a Kalman filter. Kalman filters return an
estimate of the current state (or even the prediction of a future state)
of a discrete, stochastically disturbed, linear system.

A Kalman filtering is based on a mathematical model of the system to be examined which at any point in time has the following characteristics:

**Model parameter:**-
transition matrix A, control matrix G including the controller output u and the measurement matrix C

**Model stochastic:**-
system-error covariance matrix Q, system-error - measurement-error covariance matrix L and measurement-error covariance matrix R

**Measurement vector:**-
y

**History of the system:**extrapolation vector and extrapolation-error covariance matrix

Many systems do not need entries “from outside” and therefore G
and u can be dropped. Further, system errors and measurement
errors are normally not correlated (L is dropped). Some of the
characteristics mentioned above may change dynamically (from one iteration
to the next). The operator `update_kalman` serves to modify
parts of the system according to an update file (ASCII) with the following
structure (see also `read_kalman`):

Dimension row + content row + matrix A + matrix C + matrix Q + matrix G + vector u + matrix L + matrix RThe dimension row thereby has the following form:

n = <integer> m = <integer> p = <integer>whereby n indicates the number of the state variables, m the number of the measurement values and p the number of the controller members (see also

The content row has the following form:

A*C*Q*G*u*L*R*and describes the further content of the file. Instead of '*', '+' (= parameter is available) respectively '-' (= parameter is missing) has to be set. In contrast to description files for

(r x s) matrices will be stored in row-major order in the following form:

vectors will be stored correspondingly in the following form:

/`DimensionIn`:`DimensionOut`-
These parameters include the dimensions of the state vector, measurement vector and controller vector and therefore are vectors [n,m,p], whereby n indicates the number of the state variables, m the number of the measurement values and p the number of the controller members. n and m are invariant for a given system, i.e. they must not differ from corresponding input values of the update file. For a system without without influence “from outside”

*'p = 0'*. /`ModelIn`:`ModelOut`-
These parameters include the lined up matrices (vectors) A, C, Q, G, u and if necessary L which have been stored in row-major order.

/`ModelIn`therefore are vectors of the length n*n + n*m + n*n + n*p + p [+ n*m]. The last summand is dropped if system errors and measurement errors are not correlated, i.e. no value has been set for L.`ModelOut` /`MeasurementIn`:`MeasurementOut`These parameters include the matrix R stored in row-major order, and therefore are vectors of the dimension m*m.

- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.

Update file for a Kalman filter.

Default value: 'kalman.updt'

The dimensions of the state vector, measurement vector and controller vector.

Default value: [3,1,0]

Typical range of values: `0
≤
DimensionIn
≤
30`

The lined up matrices A,C,Q, possibly G and u, and if necessary L which all have been stored in row-major order.

Default value: [1.0,1.0,0.5,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,54.3,37.9,48.0,37.9,34.3,42.5,48.0,42.5,43.7]

Typical range of values: `0.0
≤
ModelIn
≤
10000.0`

The matrix R stored in row-major order.

Default value: [1,2]

Typical range of values: `0.0
≤
MeasurementIn
≤
10000.0`

The dimensions of the state vector, measurement vector and controller vector.

The lined up matrices A,C,Q, possibly G and u, and if necessary L which all have been stored in row-major order.

The matrix R stored in row-major order.

* The following values are describing the system * * DimensionIn = [3,1,0] * ModelIn = [1.0,1.0,0.5,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0, * 54.3,37.9,48.0,37.9,34.3,42.5,48.0,42.5,43.7] * MeasurementIn = [1,2] * * An example of the Updatefile: * * n=3 m=1 p=0 * A+C-Q-G-u-L-R- * transitions at time t=15: * 2 1 1 * 0 2 2 * 0 0 2 * * the results of update_kalman: * * DimensionOut = [3,1,0] * ModelOut = [2.0,1.0,1.0,0.0,2.0,2.0,0.0,0.0,2.0,1.0,0.0,0.0, * 54.3,37.9,48.0,37.9,34.3,42.5,48.0,42.5,43.7] * MeasurementOut = [1.2]

If the update file is readable and correct, the operator
`update_kalman` returns the value 2 (H_MSG_TRUE).
Otherwise an exception is raised.

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