IP Library › Granted Patent US 12,411,221
Granted Patent B2
US 12,411,221 · App. 17/919,665 · Granted Sep 9, 2025

User equipment two-dimensional state estimation

Inventor: Torbjörn Wigren (Uppsala, SE)
Assignee: Telefonaktiebolaget LM Ericsson (Publ)
G01S11/10G01S5/02
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 12,411,221
App. No.
17/919,665
Granted
Sep 9, 2025
Kind
B2
Abstract

A method for kinematic state estimation of a UE in a wireless communication system. The method includes estimating of a kinematic state having two-dimensional position, two-dimensional velocity and a frequency bias of the UE. The estimation is based on and fully enabled by obtained measurements of Doppler shifts, relative two different antennas of the wireless communication system, of radio signals transmitted from the UE, and obtained distance-establishing measurements associated with the UE. A network node performing the method and a computer program therefore are also presented.

Claims (32)

1. A method in a network node for kinematic state estimation of a user equipment in a wireless communication system, the method comprising:

obtaining, from each of two different antennas of the wireless communication system, measurements of Doppler shifts;

obtaining distance-establishing measurements associated with the user equipment; and

estimating a kinematic state comprising two-dimensional position, two-dimensional velocity and a frequency bias of said user equipment based on and fully enabled by the obtained measurements of Doppler shifts, relative the two different antennas of the wireless communication system, of radio signals transmitted from the user equipment, and the obtained distance-establishing measurements associated with the user equipment.

2. The method according to claim 1 , comprising:

obtaining the measurements of Doppler shifts; and

obtaining the distance-establishing measurements.

3. The method according to claim 1 , wherein the distance-establishing measurements are selected among timing advance, path loss and fingerprint positioning.

4. The method according to claim 3 , wherein the distance-establishing measurements are timing advance measurements.

5. The method according to claim 1 , characterized in that said estimating is performed utilizing continuous time models.

6. The method according to claim 1 , wherein the estimating is performed utilizing two movement models, both movement models are constant continuous-time constant-velocity models with acceleration noise, where the acceleration noise of one of the two movement models is larger than for the other one of the two movement models.

7. The method according to claim 1 , wherein the measurements of Doppler shifts and the distance-establishing measurements are associated with a respective measurement time, whereby for each measurement, the movements models are propagated to said respective measurement time.

8. The method according to claim 7 , wherein the estimating is performed by interacting multiple model filtering using a continuous-time mode-switching model.

9. The method according to claim 1 , wherein estimating a kinematic state comprises Extended Kalman Filtering.

10. A network node configured to estimate a kinematic state of a user equipment in a wireless communication system, the network node comprising at least one processor configured to:

obtain, from each of two different antennas of the wireless communication system, measurements of Doppler shifts;

obtain distance-establishing measurements associated with the user equipment; and

estimate a kinematic state comprising two-dimensional position, two-dimensional velocity and a frequency bias of the user equipment based on and fully enabled by measurements of the obtained Doppler shifts, relative the two different antennas of the wireless communication system, of radio signals transmitted from the user equipment, and the obtained distance-establishing measurements associated with the user equipment.

11. The network node according to claim 10 , wherein the network node comprises a processor and a memory, the memory comprising instructions executable by the processor, whereby the processor is operative to estimate a kinematic state in two dimensions of the user equipment based on the measurements of obtained Doppler shifts and the obtained distance-establishing measurements.

12. The network node according to claim 11 , wherein the network node further comprises communication circuitry configured to obtain the measurements of Doppler shifts and the distance-establishing measurements.

13. The network node according to claim 10 , wherein the distance-establishing measurements are selected among timing advance, path loss and fingerprint positioning.

14. The network node according to claim 13 , wherein the distance-establishing measurements are timing advance measurements.

15. The network node according to claim 10 , wherein the network node is configured to perform the estimating utilizing continuous time models.

16. The network node according to claim 10 , wherein the network node is configured to perform the estimating utilizing two movement models, both movement models are constant continuous-time constant-velocity models with acceleration noise, where the acceleration noise of one of the two movement models is larger than for the other one of the two movement models.

17. The network node according to claim 10 , wherein the measurements of Doppler shifts and the distance-establishing measurements are associated with a respective measurement time.

18. The network node according to claim 17 , wherein the network node is configured to perform the estimating by interacting multiple model filtering using a continuous-time mode-switching model.

19. The network node according to claim 10 , wherein the network node is configured to perform the estimating of a kinematic state by utilizing Extended Kalman Filtering.

20. The network node according to claim 10 , wherein the network node is a base station.

21. A non-transitory computer storage medium storing a computer program comprising instructions, which when executed by at least one processor, cause the at least one processor to:

obtain, from each of two different antennas of the wireless communication system, measurements of Doppler shifts;

obtain distance-establishing measurements associated with the user equipment; and

estimate a kinematic state comprising two-dimensional position, two-dimensional velocity and a frequency bias of a user equipment based on and fully enabled by measurements of the obtained Doppler shifts, relative the two different antennas of a wireless communication system, of radio signals transmitted from the user equipment, and the obtained distance-establishing measurements associated with the user equipment.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 8, 2022
From: WIGREN, TORBJORN
To: TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)
Reel/Frame 062028/0949 →
Continuity (1)
Related Publication 20230358873A1 · Nov 9, 2023
References Cited (10)
US 20110287778A1 · Levin et al. · 2011 [cited by applicant]
US 20170324441A1 · Seller · 2017 [cited by applicant]
US 20180088205A1 · Shamain et al. · 2018 [cited by applicant]
US 20190049968A1 · Dean · 2019 [cited by examiner]
GB 2388749B · 2003 [cited by applicant]
WO 2019072394A1 · 2019 [cited by applicant]
International Search Report and Written Opinion dated Feb. 25, 2021 for International Application No. PCT/SE2020/050402 filed Apr. 21, 2020; consisting of 11 pages. [cited by applicant]
EPO Communication with Supplementary European Search Report dated Dec. 22, 2023 for European Patent Application No. 20932252.8, consisting of 10 pages. [cited by applicant]
Y. Bar-Shalom et al.; Tracking with Classification-Aided Multiframe Data Association; IEEE Transactions on Aerospace and Electronic Systems, vol. 41, No. 3; Jul. 2005, consisting of 11 pages. [cited by applicant]
A.F. Genovese; The Interacting Multiple Model Algorithm for Accurate State Estimation of Maneuvering Targets; Johns Hopkins APL Technical Digest, vol. 22, No. 4; Jan. 1, 2001, consisting of 10 pages. [cited by applicant]