NEWCLAIMS

Claim 1. A method for adjusting model and/or calibration parameters of a sensor system that is equipped with said model of external events that are taking place in the real world where:

a) provides a data base unit for storing information on:

- a plurality of test point sensor output signal values for some of said sensors and a plurality of values for external events that correspond to said test point sensor output signal values and/or simultaneous time series of sensor output signal values from adjacent sensors for comparison;

- values of said sensor output signals, values of said model and/or calibration parameters and values of said external events that correspond to a situation at time t; and,

- controls of said sensors and/or forcings of said external events that correspond to said situation at time t;

b) provides a logic unit for accessing both said sensor signal output values and said model and/or calibration parameter values, where said logic unit has both a two-way communications link to said data base unit and the capability of computing initial values for unknown model and/or calibration parameters, if required, by using Lange's High-pass Filter:

i = (Xi'Vi-1Xi)-1Xi'Vi-1(yi - Gi 0)

0 = (Gi' Ri Gi)-1Gi'Ri yi

where
i = computed initial values for unknown model and/or calibration parameters at an initial time i
0 = computed initial values for those unknown model and/or calibration parameters that are common to all initial times i
yi = vector of data obtained from said plurality of test point sensor output signal values for some of said sensors and/or said plurality of values for said external events corresponding to said test point sensor output signal values, and/or said simultaneous time series of said output signal values from adjacent sensors for comparison
Xi = Jacobian matrix for said state parameters si
Gi = Jacobian matrix for said state parameters c0
Ri = Vi-1-Vi-1Xi(Xi'Vi-1Xi)-1Xi'Vi-1
Vi = error covariance matrix of said vector of data yi
= summation where the time index i runs over those time series of data that are used for initialising the FKF filter;

c) provides said sensor output signal values from said sensors, as available, to said logic unit;

d) provides information on said controls of said sensors and/or said forcings of said external events to said data base unit;

e) accesses current values of both model and/or calibration parameters and state transition matrices, and computes updated values of said model and/or calibration parameters by using FKF-formulas:

t - l = (Xt - l'Vt - l-1Xt - l)-1Xt - l'Vt - l-1(Yt - l - Gt l t)

t = (Gt - l' Rt - l Gt - l)-1Gt - l'Rt - lYt - l

where
t - l = filtered values of model and/or calibration parameters for time block t - l
t = filtered values of those model and/or calibration parameters that are common for said situation at time t
Yt - l = [yt - l', (A t - l - 1 + B ut - l - 1)']' = vector of said sensor output signal values augmented with predicted model and/or calibration parameters
yt - l = vector of said sensor output signal values for time block t - l
Nt - l = number of sensor output signal values in said vector yt - l
A t - l - 1 + B ut - l - 1 = vector of said predicted model and/or calibration parameters for time block t l
A = state transition matrix
t - l - 1 = filtered values of model and/or calibration parameters for previous time block t - l 1
B = control gain matrix
ut - l - 1 = controls of said sensors or forcings of said external events for previous time block t - l - 1
Xt - l = [ Ht - l', I ]' = augmented Jacobian matrix for state parameters st - l
Ht - l = Jacobian matrix for said state parameters st - l
I = identity matrix
Gt - l = [ [Fyt - l', Fst - l']', [   , Mt - l - 1']' ] = augmented Jacobian matrix for state parameters ct
Ft - l = [Fyt - l', Fst - l']' = matrix of common factors for error covariances between time block t - l and other blocks
Mt - l - 1 = matrix of factors for correcting state transition error from time block t - l - 1 to time block t - l
Rt - l = Vt - l-1-Vt - l-1Xt - l(Xt - l'Vt - l-1Xt - l)-1Xt - l'Vt - l-1
Vt - l = error covariance matrix of said augmented vector of data in Yt - l
= summation where the time block index t-l runs over those L time blocks of data that are used for said situation at time t;

wherein the improvement comprises the block-diagonalization of the error covariance matrix of Augmented Model (24):

of the description by exploiting any of said common factors Fy or Fs or said factors M by FKF-formulas, in said logic unit, for said situation at time t;

f) controls stability of said adaptive FKF-filtering by monitoring accuracy estimates of said updated values of model and/or calibration parameters, in said logic unit, and indicates needs for sensor output signal values, test point data, sensor comparisons or a system reconfiguration;

g) adjusts said model and/or calibration parameter values if stable updates are available.

 

Claim 2. A method for adjusting model and/or calibration parameters of a sensor system that is equipped with said model of external events that are taking place in the real world where:

a) provides a data base unit for storing information on:

- a plurality of test point sensor output signal values for some of said sensors and a plurality of values for external events that correspond to said test point sensor output signal values and/or simultaneous time series of sensor output signal values from adjacent sensors for comparison;

- values of said sensor output signals, values of said model and/or calibration parameters and values of said external events that correspond to a situation; and,

- controls of said sensors and/or forcings of said external events that correspond to said situation;

b) provides a logic unit for accessing both said sensor signal output values and said model and/or calibration parameter values, where said logic unit has both a two-way communications link to said data base unit and the capability of computing initial values for unknown model and/or calibration parameters, if required, by using Lange's High-pass Filter;

c) provides said sensor output signal values from said sensors, as available, to said logic unit;

d) provides information on said controls of said sensors and/or said forcings of said external events to said data base unit;

e) accesses current values of both model and/or calibration parameters and state transition matrices, and computes updated values of said model and/or calibration parameters by using FKF-formulas that are obtained by applying Frobenius Inversion Formula (26):

for solving the Normal Equation system, where the improvement comprises a partial diagonalization of the covariance matrix of the residual errors [et', (A (t - 1 - st - 1) - at)']' of Augmented Model (8):

by applying factors Fy, Fs or M to said Augmented Model, in said logic unit, for said situation;

f) controls stability of said adaptive FKF-filtering by monitoring accuracy estimates of said updated values of model and/or calibration parameters, in said logic unit, and indicates needs for sensor output signal values, test point data, sensor comparisons or a system reconfiguration;

g) adjusts said model and/or calibration parameter values if stable updates are available. 


* Last revised: May 23, 2004