IP Library Granted Patent US 12,543,019
Granted Patent B2
US 12,543,019 · App. 18/182,713 · Granted Feb 3, 2026

Realizing enterprise-grade localization using WiFi 802.11mc fine tune measurement

Inventors: Ramanujan Sheshadri (Jersey City, NJ); Karthikeyan Sundaresan (Manalapan, NJ); Shivang Aggarwal (San Jose, CA)
Assignee: NEC Corporation
H04W4/029H04W4/023H04W84/12
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,543,019
App. No.
18/182,713
Granted
Feb 3, 2026
Kind
B2
Abstract

A method for assessing viability of a WiFi fine tune measurement (FTM) based ranging protocol for enterprise-grade localization with heterogeneity across operational parameters is presented. The method includes enabling a plurality of access points to range with a plurality mobile devices handled by a plurality of users within an enterprise environment, tracking a mobile device of the plurality of mobile devices using onboard sensors as the mobile device travels within the enterprise environment, measuring a plurality of ranges with at least three access points of the plurality of access points at various points along a trajectory, and combining information related to the plurality of ranges and the trajectory to predict a range offset of the mobile device such that the mobile device itself self-calibrates all its range offsets on-demand.

Claims (197)

1 . A method for assessing viability of a WiFi fine tune measurement (FTM) based ranging protocol for enterprise-grade localization with heterogeneity across operational parameters, the method comprising:

enabling a plurality of access points to range with a plurality of mobile devices handled by a plurality of users within an enterprise environment;

tracking a mobile device of the plurality of mobile devices using onboard sensors as the mobile device travels within the enterprise environment;

measuring a plurality of ranges with at least three access points of the plurality of access points at various points along a trajectory; and

combining information related to the plurality of ranges and the trajectory to predict a range offset of the mobile device such that the mobile device itself self-calibrates all its range offsets on-demand, wherein the range offset is computed by using a quadratic solver and a new position in the trajectory is reached by solving:

(

x

AP

-

i

=

1

T

d

i

cos

(

α

i

)

)

2

+

(

y

AP

-

i

=

1

T

d

i

sin

(

α

i

)

)

2

=

R

p

-

δ

,

where α i is an angular displacement of the mobile device, d i is a distance traveled by the mobile device between time-intervals on the trajectory, (x AP , y AP ) is an initial position of an access point, R p is a range of the plurality of ranges, and δ is the range offset.

2 . The method of claim 1 , wherein the mobile device of the plurality of mobile devices performs FTM ranging at a plurality of time-intervals to measure the plurality of ranges.

3 . The method of claim 1 , wherein the range offset is given by:

δ

=

R

1

-

x

A

P

2

+

y

AP

2

,

where (x AP , y AP ) is an initial position of the access point and R 1 is a range when a user of the mobile device was at position (0,0).

4 . A system for assessing viability of a WiFi fine tune measurement (FTM) based ranging protocol for enterprise-grade localization with heterogeneity across operational parameters, the system comprising:

a plurality of access points ranging with a plurality of mobile devices handled by a plurality of users within an enterprise environment for:

tracking a mobile device of the plurality of mobile devices using onboard sensors as the mobile device travels within the enterprise environment;

measuring a plurality of ranges with at least three access points of the plurality of access points at various points along a trajectory; and

combining information related to the plurality of ranges and the trajectory to predict a range offset of the mobile device such that the mobile device itself self-calibrates all its range offsets on-demand, wherein the range offset is computed by using a quadratic solver and a new position in the trajectory is reached by solving:

(

x

AP

-

i

=

1

T

d

i

cos

(

α

i

)

)

2

+

(

y

AP

-

i

=

1

T

d

i

sin

(

α

i

)

)

2

=

R

p

-

δ

,

where α i is an angular displacement of the mobile device, d i is a distance traveled by the mobile device between time-intervals on the trajectory, (x AP , y AP ) is an initial position of an access point, R p is a range of the plurality of ranges, and δ is the range offset.

5 . The system of claim 4 , wherein the mobile device of the plurality of mobile devices performs FTM ranging at a plurality of time-intervals to measure the plurality of ranges.

6 . A method for assessing viability of a WiFi fine tune measurement (FTM) based ranging protocol for enterprise-grade localization with heterogeneity across operational parameters, the method comprising:

enabling a plurality of access points to range with a plurality of mobile devices handled by a plurality of users within an enterprise environment;

tracking a mobile device of the plurality of mobile devices using onboard sensors as the mobile device travels within the enterprise environment;

measuring a plurality of ranges with at least three access points of the plurality of access points at various points along a trajectory; and

combining information related to the plurality of ranges and the trajectory to predict a range offset of the mobile device such that the mobile device itself self-calibrates all its range offsets on-demand, wherein the range offset is computed by using a linear solver and a new position in the trajectory is reached by solving:

(

x

C

+

i

=

1

T

d

i

cos

(

α

i

)

)

2

+

(

y

C

-

i

=

1

T

d

i

sin

(

α

i

)

)

2

=

(

R

p

-

δ

)

2

,

where α i is an angular displacement of the mobile device, d i is a distance traveled by the mobile device between time-intervals on the trajectory, (x C , y C ) is an initial position of the mobile device, R p is a range of the plurality of ranges, and δ is the range offset.

7 . The method of claim 6 , wherein the mobile device of the plurality of mobile devices performs FTM ranging at a plurality of time-intervals to measure the plurality of ranges.

8 . The method of claim 6 , wherein the range offset is given by:

δ

=

R

1

-

x

AP

2

+

y

AP

2

,

where (x AP , y AP ) is an initial position of the access point and R 1 is a range when a user of the mobile device was at position (0,0).

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 12, 2026
From: NEC LABORATORIES AMERICA, INC.
To: NEC CORPORATION
Reel/Frame 073431/0568 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 13, 2023
From: SHESHADRI, RAMANUJAN; SUNDARESAN, KARTHIKEYAN; AGGARWAL, SHIVANG
To: NEC LABORATORIES AMERICA, INC.
Reel/Frame 062962/0793 →
Continuity (2)
Provisional Application 63323555 · Mar 25, 2022
Related Publication 20230345207A1 · Oct 26, 2023
References Cited (27)
US 20060040670A1 · Li · 2006 [cited by examiner]
US 20070103303A1 · Shoarinejad · 2007 [cited by examiner]
US 20130051255A1 · Estevez · 2013 [cited by examiner]
US 20130324154A1 · Raghupathy · 2013 [cited by examiner]
US 20140365488A1 · Arslan · 2014 [cited by examiner]
US 20150085836A1 · Kang · 2015 [cited by examiner]
US 20150189476A1 · Tanaka · 2015 [cited by examiner]
US 20150237471A1 · Li · 2015 [cited by examiner]
US 20160088440A1 · Palanki · 2016 [cited by examiner]
US 20180103352A1 · Murase · 2018 [cited by examiner]
US 20190013978A1 · Zhou · 2019 [cited by examiner]
US 20190364380A1 · Khawand · 2019 [cited by examiner]
US 20200271747A1 · Wu · 2020 [cited by examiner]
US 20200336872A1 · Basu Mallick · 2020 [cited by examiner]
US 20210111782A1 · Kim · 2021 [cited by examiner]
US 20220070607A1 · Ebner · 2022 [cited by examiner]
US 20220201437A1 · Sanchez · 2022 [cited by examiner]
US 20220217749A1 · Yu · 2022 [cited by examiner]
US 20230081472A1 · Wang · 2023 [cited by examiner]
US 20230131322A1 · Huang · 2023 [cited by examiner]
US 20230176206A1 · Skoglar · 2023 [cited by examiner]
[Online]. Android Developer Guide. https://developer.android.com/guide/topics/connectivity/wifi-rtt (Retrieved 2023, Jan. 17). [cited by applicant]
Banin, L., Bar-Shalom, O., Dvorecki, N., & Amizur, Y. (2018, Dec. 17). Scalable Wi-Fi client self-positioning using cooperative FTM-sensors. IEEE Transactions on Instrumentation and Measurement, 68(10), 3686-3698. [cited by applicant]
Ibrahim, M., Liu, H., Jawahar, M., Nguyen, V., Gruteser, M., Howard, R., . . . & Bai, F. (2018, Oct. 15). Verification: Accuracy evaluation of WiFi fine time measurements on an open platform. In Proceedings of the 24th … [cited by applicant]
Ibrahim, M., Rostami, A., Yu, B., Liu, H., Jawahar, M., Nguyen, V., . . . & Howard, R. (2020, Jun. 15). Wi-go: accurate and scalable vehicle positioning using wifi fine timing measurement. In Proceedings of the 18th Int… [cited by applicant]
Rea, M., Giustiniano, D., & Widmer, J. (Dec. 10, 2020). Virtual inertial sensors with fine time measurements. In 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) (pp. 658-665). IEEE. [cited by applicant]
Shao, W., Luo, H., Zhao, F., Tian, H., Yan, S., & Crivello, A. (2020, May 4). Accurate indoor positioning using temporal-spatial constraints based on Wi-Fi fine time measurements. IEEE Internet of Things Journal, 7(11),… [cited by applicant]