IP Library Granted Patent US 12,372,601
Granted Patent B2
US 12,372,601 · App. 17/918,695 · Granted Jul 29, 2025

Method for geolocating interference source in communication-based transport system

Inventors: Viet Hoa Nguyen (Rennes, FR); Nicolas Gresset (Rennes, FR)
Assignee: MITSUBISHI ELECTRIC CORPORATION
G01S5/0215B61L25/025B61L27/20G01S5/0249H04B1/1027B61L2027/204
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 12,372,601
App. No.
17/918,695
Granted
Jul 29, 2025
Kind
B2
Abstract

A method for geolocating an interference source in a communication-based transport system, wherein the communication-based transport system comprises: —a plurality of interference sources, distributed in a space and respectively emitting signal, —a vehicle, moving along a known trajectory, receiving the signal from the interference sources, and measuring the signal strength of the signal of only one interference source at a time instance; the method comprising: —separating the interference sources by clustering the signal strength of the signal with a clustering method; —estimating the locations of the interference sources in the space based on the separated interference sources.

Claims (31)

1. A method for geolocating an interference source in a communication-based transport system, wherein the communication-based transport system comprises:

a plurality of interference sources, distributed in a space and respectively emitting signal,

a vehicle, moving along a known trajectory, receiving the signal from the interference sources, and measuring the signal strength of the signal of only one interference source at a time instance;

the method comprising:

separating the interference sources by clustering the signal strength of the signal with a clustering method;

estimating the locations of the interference sources in the space based on the separated interference sources.

2. The method according to claim 1 , wherein the communication-based transport system is a communications-based train control system, and the vehicle is a train.

3. The method according to claim 1 , wherein the known trajectory comprises known position, velocity and direction of the at least one vehicle.

4. The method according to claim 1 , wherein at any time instance, only one interference source emits the signal, so as to avoid collision between the interference sources.

5. The method according to claim 1 , wherein the interference sources emit signal using CSMA/CA or CSMA/CD protocols.

6. The method according to claim 1 , wherein the interference sources are randomly activated.

7. The method according to claim 1 , wherein separating the interference sources and estimating the location of the interference sources are iteratively applied.

8. The method according to claim 7 , wherein separating the interference sources uses K-mean clustering method, and estimating the location of the interference sources uses maximum-likelihood estimation.

9. The method according to claim 1 , wherein separating the interference sources and estimating the location of the interference sources are sequentially applied.

10. The method according to claim 9 , wherein separating the interference sources uses joint Bayesian clustering method, and estimating the location of the interference sources uses Maximum-A-Posteriori (MAP) estimation.

11. The method according to claim 10 , wherein separating the interference sources is progressively applied from a previous known knowledge of an interference source.

12. The method according to claim 11 , wherein the method further comprising:

geometrically dividing the measurements of the signal strength into successive clusters;

estimating the locations of the interference sources in one cluster;

exploiting the estimated locations of the interference sources in the cluster as the prior knowledge to another cluster in the successive clusters; and

filtering significant interference sources from a neighbour to another.

13. The method according to claim 11 , wherein the vehicle has a plurality of travels along the known trajectory, and the method further comprises:

estimating the locations of the interference sources in one travel;

exploiting the estimated locations of the interference sources in the travel as the prior knowledge to other travels.

14. A communication-based transport system for geolocating an interference source, wherein the communication-based transport system comprises:

a plurality of interference sources, distributed in a space and respectively emitting signal,

a vehicle, moving along a known trajectory and having a radio module, which is capable of receiving the signal from the interference sources and measuring the signal strength of the signal of only one interference source at a time instance;

a controller, configured to:

separate the interference sources by clustering the measured signal strength of the signal with clustering method;

estimate the location of the interference sources in the space by the separated interference sources.

15. A non-transitory computer readable medium storing a computer program comprising program code to be executed by a processor, the program code being adapted to performance of a method as claimed in claim 1 when executed by the processor.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 14, 2022
From: MITSUBISHI ELECTRIC R&D CENTRE EUROPE B.V.
To: MITSUBISHI ELECTRIC CORPORATION
Reel/Frame 061424/0965 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 14, 2022
From: NGUYEN, VIET HOA; GRESSET, NICOLAS
To: MITSUBISHI ELECTRIC R&D CENTRE EUROPE B.V.
Reel/Frame 061424/0974 →
Priority Claims (1)
EP 20305605 · Jun 5, 2020 · regional
Continuity (1)
Related Publication 20230141700A1 · May 11, 2023
References Cited (18)
US 9992778B2 · Brunel · 2018 [cited by examiner]
US 10305649B2 · Fang · 2019 [cited by examiner]
US 10893416B2 · Gresset · 2021 [cited by applicant]
US 20060133543A1 · Linsky et al. · 2006 [cited by applicant]
US 20150163817A1 · Brunel · 2015 [cited by examiner]
US 20160285602A1 · Fang · 2016 [cited by examiner]
US 20170195832A1 · Santiago et al. · 2017 [cited by applicant]
US 20210168820A1 · Hashimoto · 2021 [cited by examiner]
US 20210288731A1 · Yun · 2021 [cited by examiner]
US 20230141700A1 · Nguyen · 2023 [cited by examiner]
CN 113562031A · 2021 [cited by examiner]
JP 2008524961A · 2008 [cited by applicant]
JP 2017122724A · 2017 [cited by applicant]
JP 2018508418A · 2018 [cited by applicant]
JP 2019503109A · 2019 [cited by applicant]
WO WO03063532A1 · 2003 [cited by examiner]
WO WO2016118672A2 · 2018 [cited by applicant]
Japanese Office Action for Japanese Application No. 2022-578018, dated May 23, 2023, with an English translation. [cited by applicant]