Adaptive Kalman filtering for fast fading removal

• US 20080037689A1
• Filed: 12/21/2006
• Published: 02/14/2008
• Est. Priority Date: 08/09/2006
• Status: Abandoned Application

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: Le Helloco, Yann, Kurt, Tolga
• Application Number:
  US11/642,969
• Publication Number:
  US 20080037689A1
• Time in Patent Office: Days
• US Class Current: 375/340
• CPC Class Codes: H03G 3/3089 Control of digital or coded signals
• CPC Class Codes: H03H 21/003 KALMAN filters
• CPC Class Codes: H04B 17/318 Received signal strength
• CPC Class Codes: H04L 27/0008 arrangements for allowing a transmitter or receiver to use more than one type of modulation negotiating modulation type for two-way transmission paths H04L5/1453
• CPC Class Codes: H04W 52/06 TPC algorithms

Abstract

An adaptive Kalman filtering method and apparatus are used to process signal measurement data associated with the received radio signal. The signal measurement data includes a fast fading component and a slow fading component. The adaptive Kalman filtering process filters out the fast fading component of the signal measurement data but preserves to a large extent the slow fading components. This approach significantly improves the accuracy of the signal strength estimation and fast fading removal while at the same time significantly reduces the number of actual data samples required to remove that fast fading from the signal measurement data. This relaxes the speed and density requirements of the signal measurements, which in turn save time and costs.

Claims

1. A data processing method for processing signal measurement data associated with a received radio signal, where the signal measurement data includes a fast fading component and a slow fading component, including using an adaptive Kalman filtering process to filter out the fast fading component of the signal measurement data.

2. The method in claim 1, wherein the adaptive Kalman filtering process is an iterative process and uses multiple Kalman filtering variables whose values are estimated based on the signal measurements, the method further comprising:

determining an estimate of one or more of the multiple Kalman filtering variables for each iteration.

3. The method in claim 2, wherein the multiple Kalman filtering variables include a variance of the slow fading component.

4. The method in claim 2, wherein the multiple Kalman filtering variables include a variance of the fast fading component.

5. The method in claim 2, wherein the signal measurement data includes signal measurement data associated with a radio signal received at multiple different geographical positions, and wherein multiple Kalman filtering variables include a correlation coefficient associated with a degree of correlation between signal measurement data at each geographical position at a first time and signal measurement data at that geographical position at a second time.

6. The method in claim 2, further comprising:

determining that an output of the adaptive Kalman filtering process has yet to converge to a predetermined point, and

adapting one or more of the multiple Kalman filtering variables based on the output of the adaptive Kalman filtering process and performing another iteration of the adaptive Kalman filtering process based on the adaptation.

7. The method in claim 1, further comprising:

filtering the signal measurement data over a predetermined time period using a windowing technique to determine an averaged slow fading component of the signal measurement data, and

adapting a Kalman filtered result by replacing a slow fading component of the Kalman filtered data with the averaged slow fading component.

8. The method in claim 1, wherein the signal measurement data includes a signal strength of the received radio signal at multiple different geographical positions.

9. The method in claim 8, wherein the adaptive Kalman filtering process includes:

determining an a priori estimate of the signal strength at each of the geographical positions based on a previously-determined signal strength at each of the geographical positions;

determining an a posteriori prediction of a minimum mean square error (MMSE) of a previous determination of the signal strength at each of the geographical positions based on variances and power levels of the fast fading and slow fading components;

determining a Kalman filtering gain based on the determined a posteriori prediction of MMSE and an estimate of a variance of the fast fading component; and

determining a Kalman-filtered output based on the a priori estimate, the Kalman filtering gain, and an average signal strength of the received radio signal at multiple different geographical positions.

10. A method for use in filtering measurement data associated with received radio signals, comprising:

processing the measurement data;

from the processed measurement data, calculating an estimate of one or more filtering variables;

Kalman filtering the measurement data using the estimated one or more filtering variables; and

using the Kalman-filtered measurement data in managing a communications network.

11. The method in claim 10, further comprising:

in a next iteration, processing updated measurement data;

calculating a new estimate of one or more filtering variables from the updated measurement data, and

Kalman filtering the updated measurement data using the new estimate of one or more filtering variables.

12. The method in claim 11, wherein the Kalman filtering is used to filter out a fast fading component in the measurement data associated with received radio signals.

13. The method in claim 10, wherein the signal measurement data includes a signal strength of the received radio signal at multiple different geographical positions, and wherein the filtered measurement data is used to determine direction of arrival information for the received radio signal at the multiple different geographical positions.

14. The method in claim 10, wherein the signal measurement data includes signal strength of the received radio signal at multiple different geographical positions, and wherein the filtered measurement data is used to adapt a modulation method or a coding method used to transmit radio signals to at least some of the multiple different geographical positions.

15. The method in claim 10, wherein the signal measurement data includes a signal strength of the received radio signal at multiple different geographical positions, and wherein the filtered measurement data is used to control transmit power levels used to transmit radio signals to at least some of the multiple different geographical positions.

16. Apparatus for processing signal measurement data associated with a received radio signal, where the signal measurement data includes a fast fading component and a slow fading component, comprising an adaptive Kalman filtering processor configured to filter out the fast fading component of the signal measurement data.

17. The apparatus in claim 16, wherein the adaptive Kalman filtering processor is configured to:

perform an iterative process;

use multiple Kalman filtering variables whose values are estimated based on the signal measurements; and

determine an estimate of one or more of the multiple Kalman filtering variables for each iteration.

18. The apparatus in claim 17, wherein the multiple Kalman filtering variables include a variance of the slow fading component.

19. The apparatus in claim 17, wherein the multiple Kalman filtering variables include a variance of the fast fading component.

20. The apparatus in claim 17, wherein the signal measurement data includes signal measurement data associated with a radio signal received at multiple different geographical positions, and wherein multiple Kalman filtering variables include a correlation coefficient associated with a degree of correlation between signal measurement data at each geographical position at a first time and signal measurement data at that geographical position at a second time.

21. The apparatus in claim 17, wherein the adaptive Kalman filtering processor is configured to:

determine that an output of the adaptive Kalman filtering processor has yet to converge to a predetermined point, and

adapt one or more of the multiple Kalman filtering variables based on the output of the adaptive Kalman filtering process and performing another iteration of the adaptive Kalman filtering process based on the adaptation.

22. The apparatus in claim 17, wherein the adaptive Kalman filtering processor is configured to:

filter the signal measurement data over a predetermined time period using a windowing technique to determine an averaged slow fading component of the signal measurement data, and

adapt a Kalman-filtered result by replacing a slow fading component of the Kalman filtered data with the averaged slow fading component.

23. The apparatus in claim 17, wherein the signal measurement data includes a signal strength of the received radio signal at multiple different geographical positions.

24. The apparatus in claim 23, wherein the adaptive Kalman filtering processor is configured to:

determine an a priori estimate of the signal strength at each of the geographical positions based on a previously-determined signal strength at each of the geographical positions;

determine an a posteriori prediction of a minimum mean square error (MMSE) of a previous determination of the signal strength at each of the geographical positions based on variances and power levels of the fast fading and slow fading components;

determine a Kalman filtering gain based on the determined a posteriori prediction of MMSE and an estimate of a variance of the fast fading component; and

determine a Kalman-filtered output based on the a priori estimate, the Kalman filtering gain, and an average signal strength of the received radio signal at multiple different geographical positions.

25. Apparatus for use in filtering measurement data associated with received radio signals, comprising:

initial processing circuitry for processing the measurement data;

calculating circuitry for calculating an estimate of one or more filtering variables from the processed measurement data;

a Kalman filter for Kalman filtering the measurement data using the estimated one or more filtering variables; and

an output terminal for providing the Kalman-filtered measurement data for use in one or more communications network management functions.

26. The apparatus in claim 25, wherein the initial processing circuitry is configured to process updated measurement data in a next filtering iteration,

wherein the calculating circuitry is configured to calculate a new estimate of one or more filtering variables from the updated measurement data, and

wherein the Kalman filter is configured to Kalman filter the updated measurement data using the new estimate of one or more filtering variables.

27. The apparatus in claim 25, wherein the Kalman filter is configured to filter out a fast fading component in the measurement data associated with received radio signals.

28. The apparatus in claim 25, wherein the signal measurement data includes a signal strength of the received radio signal at multiple different geographical positions, further comprising:

means for determining direction of arrival information for the received radio signal at the multiple different geographical positions based on the filtered measurement data.

29. The apparatus in claim 25, wherein the signal measurement data includes a signal strength of the received radio signal at multiple different geographical positions, further comprising:

means for adapting a modulation method or a coding method used to transmit radio signals to at least some of the multiple different geographical positions based on the filtered measurement data.

30. The apparatus in claim 25, wherein the signal measurement data includes a signal strength of the received radio signal at multiple different geographical positions, further comprising:

means for controlling transmit power levels used to transmit radio signals to at least some of the multiple different geographical positions based on the filtered measurement data.