Positioning accuracy estimation

• US 8,977,292 B2
• Filed: 04/05/2012
• Issued: 03/10/2015
• Est. Priority Date: 04/05/2012
• Status: Active Grant

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: Shu, Edison, Zhang, Yang
• Primary Examiner(s): Siddiqui, Kashif
• Assistant Examiner(s): Kim, Minjung
• Application Number:
  US13/884,251
• Publication Number:
  US 20130310066A1
• Time in Patent Office: 1,069 Days
• Field of Search: 455/456.1, 455/423, 455/67.11, 342/451
• US Class Current: 455/456.1
• CPC Class Codes: G01S 5/02521 using a radio-map
• CPC Class Codes: G01S 5/10 Position of receiver fixed by co-ordinating a plurality of position lines defined by path-difference measurements , e.g. omega or decca systemsG01S5/12 takes precedence ; beacons and receivers cooperating therewith G01S1/306, G01S1/308
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

A method for fingerprinting positioning decision support includes obtaining a geographic distribution probability of positions of measurement. A first collection of simulated measurements according to a signal model is generated, wherein each simulated measurement is associated with a respective pixel. The signal model is based on base stations located in the area. Fingerprinting positioning reference data is computed from the first collection and positions of the selected pixels. A second collection of simulated measurements is generated, which is statistically independent to the first collection. These simulated measurements are associated with a respective intended position within the area. Fingerprinting positioning is performed by the second collection utilizing the computed fingerprinting positioning reference data. An estimated accuracy of the fingerprinting positioning is calculated using a comparison between the intended position and the estimated position. This is made to evaluate positioning capabilities of fingerprinting positioning based on the measurement strategy.

Claims

1. A method for fingerprinting positioning decision support, comprising of:

obtaining a geographic distribution probability of positions of measurement according to a measurement strategy;

generating a first collection of simulated measurements according to a signal model, each simulated measurement in said first collection being associated with a respective pixel selected in dependence on said geographic distribution probability;

said signal model being based on at least base stations located in said area or to be located in said area;

computing fingerprinting positioning reference data from said first collection of simulated measurements and positions of said selected pixels;

generating a second collection of simulated measurements according to said signal model, said second collection being statistically independent to said first collection, each signal of said second collection being associated with a respective intended position within said area;

performing fingerprinting positioning by said second collection of simulated measurements utilizing said computed fingerprinting positioning reference data, giving respective estimated positions; and

calculating an estimated accuracy of said fingerprinting positioning obtainable by said measurement strategy, using a comparison between said intended position and said estimated position for each of the measurements of said second collection, to evaluate positioning capabilities of fingerprinting positioning based on said measurement strategy.

2. The method according to claim 1, wherein said fingerprinting positioning is an adaptive enhanced cell ID positioning.

3. The method according to claim 1, wherein said step of obtaining a geographic distribution probability comprises obtaining of a digital map of an area to be covered by said fingerprinting positioning and converting said map into pixels, wherein each pixel has a value associated with a measurement probability according to said measurement strategy.

4. The method according to claim 3, wherein said value of said pixels is a binary value, whereby a first value represents pixels associated with positions where measurement is intended to be made and a second value represents pixels associated with positions where measurement is not intended to be made, according to said measurement strategy.

5. The method according to claim 4, wherein pixels corresponding to roads of the digital map are given said first value.

6. The method according to claim 4, wherein said measurement strategy is a driving test, whereby pixels of the digital map corresponding to roads accessible by driving tests are given said first value.

7. The method according to claim 1, wherein said step of generation of said second collection of simulated measurement is performed for pixels selected in dependence on said geographic distribution probability.

8. The method according to claim 1, wherein said signal model is based on at least a propagation model, said signal model being dependent on an intended position and base station locations.

9. The method according to claim 8, wherein said signal model is further dependent on at least one of:

environment characteristics;

minimum signal level for access; and

terminal type.

10. The method according to claim 1, wherein said simulated measurements comprise at least one of:

received signal strength;

received signal quality; and

propagation time related parameters.

11. The method according to claim 8, wherein said signal model considers the impact of random fluctuation of signal strength and minimum access signal level.

12. The method according to claim 11, wherein said signal model considers impact of a signal strength error distribution according to at least one of:

Rayleigh distribution;

Rician distribution;

Gaussian distribution; and

a statistical distribution obtained from historical data within said area.

13. The method according to claim 8, wherein said signal model is at least one of:

cell specific;

geographical area specific;

terrain specific;

GIS data specific; and

time specific.

14. The method according to claim 1, further comprising:

calibrating said estimated accuracy by a bias, said bias being computed by comparing estimated accuracy from simulations and real measured accuracy in a comparison area where both are available.

15. The method according to claim 1, wherein said intended positions are selected according to at least one of:

expected user behaviour;

expected user distribution;

randomly; and

in hotspots.

16. A system for fingerprinting positioning decision support, the system comprising:

a processor;

an input for geographical data, connected to said processor, said input for geographical data being configured to obtain a geographical data of an area to be covered by said fingerprinting positioning;

an input for cell planning data, connected to said processor; and

an output connected to said processor, for an estimated accuracy of a fingerprinting positioning;

said processor being configured to obtain a geographic distribution probability of positions of measurement according to a measurement strategy;

said processor further comprising a measurement simulator;

said measurement simulator being configured to generate a first collection of simulated measurements according to a signal model, each simulated measurement in said first collection being associated with a respective pixel selected in dependence on said geographic distribution probability;

said signal model being based on at least base stations located in said area or to be located in said area;

said measurement simulator being further configured to generate a second collection of simulated measurements according to said signal model, said second collection being statistically independent to said first collection, each simulated measurement of said second collection being associated with a respective intended position within said area;

said processor further comprising a fingerprinting positioning part;

said fingerprinting positioning part being configured to compute fingerprinting positioning reference data from said first collection of simulated measurements and positions of associated said pixels;

said fingerprinting positioning part being further configured to perform fingerprinting positioning by said second collection of simulated measurements utilizing said computed fingerprinting positioning reference data, giving respective estimated positions;

said processor being further configured to calculate said estimated accuracy of said fingerprinting positioning obtainable by said measurement strategy, using a comparison between said intended position and said estimated position for each of the simulated measurements of said second collection.