Apparatus and method for calibrating a sensor system using the Fast Kalman Filtering formula

• US 5,506,794 A
• Filed: 10/09/1991
• Issued: 04/09/1996
• Est. Priority Date: 04/28/1989
• Status: Expired due to Term

• Current Assignee: Antti A.I. Lange
• Original Assignee: Antti A.I. Lange
• Inventors: Lange, Antti A. I.
• Primary Examiner(s): Cosimano, Edward R.
• Application Number:
  US07/768,770
• Time in Patent Office: 1,644 Days
• Field of Search: 364/571.01, 364/571.02, 364/571.03, 364/571.07, 364/571.05, 364/724.2
• US Class Current: 702/104
• CPC Class Codes: G01D 18/008 with calibration coefficients stored in memory
• CPC Class Codes: G01D 3/022 having an ideal characteristic, map or correction data stored in a digital memory

Abstract

A method and apparatus for calibrating a sensor system output that includes a logic unit (1) for receiving uncalibrated sensor signals from a centralized sensor system (3) and for reading/writing data from/to a data base unit (2) that contains updated information on all control (4) and performance aspects of the sensors. Based upon these inputs, the logic unit (1) can provide real-time or near real-time optimum calibration (5) of the sensors by using the Fast Kalman Filtering (FKF) method when the stability conditions of standard Kalman Filtering are met by the sensor system.

Claims

1. A calibration device for use with a flow-tracking system, the device comprising the following subsystems, control facilities and a processing unit of which at least some are integral parts of the flow-tracking system:

(A) flow-driven subsystem(s) including

(i) means for capturing position location signals,

(ii) means for forwarding said signals;

(B) receiver subsystem(s) including

(i) means for receiving said forwarded signals,

(ii) means for connecting to said processing unit;

(C) said processing unit including

(i) means for acquiring values of said forwarded signals,

(ii) data base means for storing information on calibration of said subsystems and on differences between said values and expected values of said forwarded signals,

(iii) means for computing tracks of said flow-driven subsystem(s) for alternative configurations of said subsystems, said expected values from said computed tracks, said differences and calibration updates by processing said computed differences initially with Lange'"'"'s High-pass Filter for a calibration of entirely uncalibrated subsystems, if there are any, and subsequently with the Fast Kalman filter (FKF); and

,

(D) said control facilities including

(i) means for altering configuration of said calibration device by coupling/decoupling each of said subsystems to/from said device,

(ii) means for acquiring precalibration, accuracy and system noise data of said subsystems,

(iii) means for communicating with said processing unit,

(iv) means for monitoring stability of said computed calibration updates for each of said alternative configurations, and

(v) means for selecting one of said alternative configurations whose calibration updates exhibit a proper convergence, and thereby providing a stable calibration for all those integral parts of said flow-tracking system which are coupled to said device.

2. A large calibration apparatus for use with a state-finding system, the apparatus comprising the following subsystems, control facilities and a processing unit of which at least some are integral parts of the state-finding system and which together exploit more than 50 sensor signals simultaneously:

(A) sensor subsystems including

(i) means for capturing signals,

(ii) means for forwarding said captured signals;

(B) transmission subsystems including

(i) means for receiving said forwarded signals,

(ii) means for connecting to said processing unit;

(C) said processing unit including

(i) means for acquiring values of said forwarded signals,

(ii) data base means for storing information on calibration of said subsystems and on differences between said values and expected values of said forwarded signals,

(iii) means for computing state parameters, said expected values from said computed parameters, said differences and calibration updates by processing said computed differences initially with Lange'"'"'s High-pass Filter for a calibration of entirely uncalibrated subsystems, if there are any, and subsequently with the Fast Kalman Filter (FKF); and

,

(D) said control facilities including

(i) means for coupling each of said subsystems to said apparatus,

(ii) means for acquiring precalibration, accuracy and system noise data of said subsystems,

(iii) means for communicating with said processing unit,

(iv) means for monitoring stability of said calibration updates by using accuracy information obtained from Lange'"'"'s sparse-matrix inversion of said Filter(s), and

(v) means for selecting those of said subsystems whose calibration updates exhibit a proper convergence, and thereby providing a stable calibration for said selected integral parts of said state-finding system coupled to said apparatus.

3. A method for calibrating readings of a large centralized sensor system by Kalman filtering, the sensor output units providing signals in response to external events and where the number of simultaneously processed sensor output signal values in excess of 50, the method comprising the steps of:

a) providing 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 said external events corresponding to said test point sensor output values;

simultaneous time series of said readings from adjacent sensors for comparison;

said readings accompanied with their calibration parameters and values for said external events corresponding to a situation; and

,

controls of said sensors and changes in said external events corresponding to a new situation;

b) providing a logic unit for accessing said readings with said calibration parameters, said logic unit having a two-way communications link to said data base unit, and computing initial values for unknown calibration parameters and their accuracy estimates by using Lange'"'"'s High-pass Filter as required;

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

d) providing information on said controls and changes to said data base unit;

e) accessing current values of said calibration parameters and computing by using the Fast Kalman Filter (FKF) formulas obtained from Lange'"'"'s sparse-matrix inversion formula, in said logic unit, updates of said calibration parameters, values of said external events and their accuracies corresponding to said new situation;

f) controlling stability of said Kalman filtering by monitoring said accuracy estimates, in said logic unit, and by indicating need for new test point data, comparisons or a system reconfiguration,

g) calibrating those of said sensor readings for which stable calibration parameters are available.