update_kalmanT_update_kalmanUpdateKalmanUpdateKalmanupdate_kalman (算子名称)

名称

update_kalmanT_update_kalmanUpdateKalmanUpdateKalmanupdate_kalman — Read an update file of a Kalman filter.

警告

update_kalmanupdate_kalmanUpdateKalmanUpdateKalmanUpdateKalmanupdate_kalman is obsolete and is only provided for reasons of backward compatibility.

参数签名

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

描述

该算子 update_kalmanupdate_kalmanUpdateKalmanUpdateKalmanUpdateKalmanupdate_kalman reads the update file FileNameFileNameFileNameFileNamefileNamefile_name 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). 该算子 update_kalmanupdate_kalmanUpdateKalmanUpdateKalmanUpdateKalmanupdate_kalman serves to modify parts of the system according to an update file (ASCII) with the following structure (see also read_kalmanread_kalmanReadKalmanReadKalmanReadKalmanread_kalman): Dimension row + content row + matrix A + matrix C + matrix Q + matrix G + vector u + matrix L + matrix R The 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 DimensionInDimensionInDimensionInDimensionIndimensionIndimension_in / DimensionOutDimensionOutDimensionOutDimensionOutdimensionOutdimension_out). The maximal dimension will hereby be limited by a system constant (= 30 for the time being). As in this case changes should take effect at a valid model, the dimensions n and m are invariant (and will only be indicated for purposes of control).

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 read_kalmanread_kalmanReadKalmanReadKalmanReadKalmanread_kalman, the system description needs not be complete in this case. Only those parts of the system which are changed must be indicated. The indication of estimated values is unnecessary, as these values must stem from the latest filtering according to the structure of the filter.

(r x s) matrices will be stored in row-major order in the following form: (the spaces/line feed characters can be chosen at will),

vectors will be stored correspondingly in the following form: The following parameter values of 该算子 read_kalmanread_kalmanReadKalmanReadKalmanReadKalmanread_kalman will be changed:

DimensionInDimensionInDimensionInDimensionIndimensionIndimension_in / DimensionOutDimensionOutDimensionOutDimensionOutdimensionOutdimension_out:: 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'"p""p""p""p""p" = 0.
ModelInModelInModelInModelInmodelInmodel_in / ModelOutModelOutModelOutModelOutmodelOutmodel_out:: 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. ModelInModelInModelInModelInmodelInmodel_in / ModelOutModelOutModelOutModelOutmodelOutmodel_out 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.
MeasurementInMeasurementInMeasurementInMeasurementInmeasurementInmeasurement_in / MeasurementOutMeasurementOutMeasurementOutMeasurementOutmeasurementOutmeasurement_out:: These parameters include the matrix R stored in row-major order, and therefore are vectors of the dimension m*m.

运行信息

多线程类型:可重入(与非独占操作符并行运行)。
多线程作用域:全局(可以从任何线程调用)。
未经并行化处理。

参数表

FileNameFileNameFileNameFileNamefileNamefile_name (input_control) filename.read → (string)

Update file for a Kalman filter.

Default: 'kalman.updt' "kalman.updt" "kalman.updt" "kalman.updt" "kalman.updt" "kalman.updt"

DimensionInDimensionInDimensionInDimensionIndimensionIndimension_in (input_control) integer-array → (integer)

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

Default: [3,1,0]

Value range: 0 ≤ DimensionIn DimensionIn DimensionIn DimensionIn dimensionIn dimension_in ≤ 30

ModelInModelInModelInModelInmodelInmodel_in (input_control) real-array → (real)

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: [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]

Value range: 0.0 ≤ ModelIn ModelIn ModelIn ModelIn modelIn model_in ≤ 10000.0

MeasurementInMeasurementInMeasurementInMeasurementInmeasurementInmeasurement_in (input_control) real-array → (real)

The matrix R stored in row-major order.

Default: [1,2]

Value range: 0.0 ≤ MeasurementIn MeasurementIn MeasurementIn MeasurementIn measurementIn measurement_in ≤ 10000.0

DimensionOutDimensionOutDimensionOutDimensionOutdimensionOutdimension_out (output_control) integer-array → (integer)

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

ModelOutModelOutModelOutModelOutmodelOutmodel_out (output_control) real-array → (real)

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

MeasurementOutMeasurementOutMeasurementOutMeasurementOutmeasurementOutmeasurement_out (output_control) real-array → (real)

The matrix R stored in row-major order.

例程 (HDevelop)

* 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]

例程 (C)

/* 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]                                               */

例程 (HDevelop)

* 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]

例程 (HDevelop)

* 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]

例程 (HDevelop)

* 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]

结果

If the update file is readable and correct, 该算子 update_kalmanupdate_kalmanUpdateKalmanUpdateKalmanUpdateKalmanupdate_kalman 返回值 2 ( H_MSG_TRUE) . Otherwise an exception is raised.

可能的后置算子

filter_kalmanfilter_kalmanFilterKalmanFilterKalmanFilterKalmanfilter_kalman

参考其它

read_kalmanread_kalmanReadKalmanReadKalmanReadKalmanread_kalman, filter_kalmanfilter_kalmanFilterKalmanFilterKalmanFilterKalmanfilter_kalman

模块

Foundation

算子列表