Compressing radio maps using different compression models

Patent metadata

Field	Value
Publication number	US-10545231-B2
Application number	US-201816147100-A
Country	US
Kind code	B2
Filing date	Sep 28, 2018
Priority date	Jun 2, 2017
Publication date	Jan 28, 2020
Grant date	Jan 28, 2020

Abstract

Embodiments are disclosed for compressing radio maps of fingerprint-based positioning systems using different compression models. In an embodiment, a method comprises: receiving, by a computing device, access point (AP) data from a plurality of mobile devices operating in a geographic region, the AP data including signal strength measurements of AP signals received at a plurality of reference locations in the geographic region and uncertainty measurements associated with the signal strength measurements; determining a level of accuracy with the first compression model; responsive to the determining, selecting one of the first compression model or a second compression model to compress the AP data, the second compression model being different than the first compression model; compressing the AP data using the selected compression model; and responsive to a request from a mobile device operating in the geographic region, sending a data packet including the compressed AP data to the mobile device.

First claim

What is claimed is:

1. A method comprising:
receiving, by a computing device, access point (AP) data from a plurality of mobile devices operating in a geographic region, the AP data including signal strength measurements of AP signals received at a plurality of reference locations in the geographic region and uncertainty measurements associated with the signal strength measurements;
determining a level of accuracy with a first compression model;
responsive to the determining, selecting one of the first compression model or a second compression model to compress the AP data, the second compression model being different than the first compression model;
compressing the AP data using the selected compression model; and
responsive to a request from a mobile device operating in the geographic region, sending a data packet including the compressed AP data to the mobile device.

2. The method of claim 1 , wherein the first compression model is a radio propagation model that predicts a path loss that a radio frequency (RF) signal encounters inside a structure or a densely populated area over distance.

3. The method of claim 2 , wherein the path loss model is a log-distance path loss model given by
mode c =max_ dBm− 10γ log 10 d ( c,c max ),

where mode c is a predicted mode of a probability distribution of the signal strength measurements in a cell c of a two-dimensional (2D) grid of cells associated with an AP, max_dBm is the maximum signal strength in a cell c max of the 2D grid, γ is a path loss coefficient and d(c, c max ) is the Euclidean distance between the center of cell c and the center of cell c max .

4. The method of claim 3 , wherein determining the level of accuracy with the first compression model further comprises:
computing residuals between predicted modes computed from the first compression model and actual modes obtained from the signal strength data;
calculating a residual sum of squares of the residuals;
comparing the residual sum of squares with a residual threshold value; and
selecting the first compression model for compressing the signal strength measurements in the AP data based on results of the comparing.

5. The method of claim 3 , wherein a curve fitting function is used to fit the log-distance path loss model to the signal strength measurements using a non-linear least squares formulation.

6. The method of claim 3 , wherein mode c is a predicted mode of a Rayleigh probability distribution.

7. The method of claim 1 , wherein the second compression model includes fitting a surface to the signal strength measurements.

8. The method of claim 1 , further comprising:
filtering the AP data to remove outlier AP data.

9. The method of claim 8 , wherein filtering AP data further comprises:
identifying non-servable APs in the AP data; and
excluding the non-servable APs from further processing.

10. The method of claim 8 , wherein filtering AP data further comprises:
clustering the AP data;
identifying outlier AP signal strength measurements based on the clustering; and
excluding outlier AP signal strength measurements from further processing.

11. A system comprising:
one or more processors;
memory storing instructions, that when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving access point (AP) data from a plurality of mobile devices operating in a geographic region, the AP data including signal strength measurements of AP signals received at a plurality of reference locations in the geographic region and uncertainty measurements associated with the signal strength measurements;
determining a level of accuracy with a first compression model;
responsive to the determining, selecting one of the first compression model or a second compression model to compress the AP data, the second compression model being different than the first compression model;
compressing the AP data using the selected compression model; and
responsive to a request from a mobile device operating in the geographic region, sending a data packet including the compressed AP data to the mobile device.

12. The system of claim 11 , wherein the first compression model is a radio propagation model that predicts a path loss that a radio frequency (RF) signal encounters inside a structure or a densely populated area over distance.

13. The system of claim 12 , wherein the path loss model is a log-distance path loss model given by
mode c =max_ dBm− 10γ log 10 d ( c,c max ),

where mode c is a predicted mode of a probability distribution of the signal strength measurements in a cell c of a two-dimensional (2D) grid of cells associated with an AP, max_dBm is the maximum signal strength in a cell c max of the 2D grid, γ is a path loss coefficient and d(c, c max ) is the Euclidean distance between the center of cell c and the center of cell c max .

14. The system of claim 13 , wherein determining the level of accuracy with the first compression model further comprises:
computing residuals between predicted modes computed from the first compression model and actual modes obtained from the signal strength data;
calculating a residual sum of squares of the residuals;
comparing the residual sum of squares with a residual threshold value; and
selecting the first compression model for compressing the signal strength measurements in the AP data based on results of the comparing.

15. The system of claim 13 , wherein a curve fitting function is used to fit the log-distance path loss model to the signal strength measurements using a non-linear least squares formulation.

16. The system of claim 13 , wherein mode c is a predicted mode of a Rayleigh probability distribution.

17. The system of claim 11 , wherein the second compression model includes fitting a surface to the signal strength measurements.

18. The system of claim 11 , further comprising:
filtering the AP data to remove outlier AP data.

19. The system of claim 18 , wherein filtering AP data further comprises:
identifying non-servable APs in the AP data; and excluding the non-servable APs from further processing.

20. The system of claim 18 , wherein filtering AP data further comprises:
clustering the AP data;
identifying outlier AP signal strength measurements based on the clustering; and
excluding outlier AP signal strength measurements from further processing.

Assignees

• Apple Inc

Inventors

• Zhang Wuzhou
• Detwiler Benjamin A
• Millman David Benjamin
• Singh Jasvinder
• Holtsberg Anders M
• Tay Darin

Classifications

• G01S13/89 Primary

  for mapping or imaging · CPC title

• G01S11/06

  using intensity measurements · CPC title

• G01S5/0252

  Radio frequency fingerprinting · CPC title

• H04B17/318

  Received signal strength · CPC title

• G01S5/02523 Primary

  Details of interaction of receiver with radio-map · CPC title