UWB-based in-vehicle 3D localization of mobile devices

Patent metadata

Field	Value
Publication number	US-12584991-B2
Application number	US-202318508579-A
Country	US
Kind code	B2
Filing date	Nov 14, 2023
Priority date	Nov 14, 2023
Publication date	Mar 24, 2026
Grant date	Mar 24, 2026

Abstract

A method for in-vehicle localization of a mobile device includes receiving, in real time, sensor data from a plurality of UWB sensors inside a vehicle. The plurality of UWB sensors includes a plurality of UWB anchors and a UWB tag, which is part of the mobile device. The method further includes determining a plurality of location candidates of the UWB tag based on the sensor data received, determining a plurality of sensed current locations of the UWB tag and a plurality of probabilities for each of the plurality of sensed current locations of the UWB tag using a Gaussian Kernel Density Estimation (KDE), tracking a motion of the UWB tag, and determining a real-time position of the UWB tag using a Bayesian estimation.

First claim

What is claimed is:

1 . A method for in-vehicle localization of a mobile device, comprising:
receiving, in real time, sensor data from a plurality of ultra-wideband (UWB) sensors inside a vehicle, wherein the plurality of UWB sensors includes a plurality of UWB anchors each disposed in a fixed position inside the vehicle and at least one UWB tag that is not in a fixed position relative to the vehicle, the UWB tag is part of the mobile device, and the sensor data includes a location information about the UWB anchors and a distance from the UWB tag to each of the UWB anchors; determining a plurality of location candidates of the UWB tag based on the sensor data received from the plurality of UWB sensors; determining a plurality of sensed current locations of the UWB tag and a plurality of probabilities for each of the plurality of sensed current locations of the UWB tag using a Gaussian Kernel Density Estimation (KDE) based on the location candidates of the UWB tag; tracking a motion of the UWB tag by generating a probability matrix (p mot ) through applying a Gaussian KDE to motion-predicted locations (Lk), wherein the probability matrix represents a distribution of possible tag locations over time; and determining a real-time location of the UWB tag by Bayesian estimation that multiplies the probability matrix (pmot) with a probability matrix (p mea ) generated from sensed current locations of the UWB tag via Gaussian KDE, and selecting a maximum probability from the resulting distribution within the vehicle, wherein the motion of the UWB tag is tracked using a following equations:

L
k

=

L

k
-
1

+

δ
⁢
t
*
v

+

0
.
5

*
a
*
δ
⁢

t
2

p
mot

=

KDE
⁡
(

L
k

)

where:
k is a timestamp;
L k is a current location at timestamp k;
L k-1 is a previous location at timestamp k−1;
δt is a time span between timestamp k and timestamp k−1
v is a moving speed of the UWB tag;
a is a movement acceleration of the UWB tag:
p mot is a probability matrix predicted by a motion tracking; and
KDE is a Kernel Density Estimation,
wherein the Bayesian estimation uses a following equation:

α
=

max
⁡
(

p
mot
k

*

p
mea
k

)

,

where:
p mot k is the probability matrix predicted by the motion tracking;
p mea k is a probability matrix predicted by a sensor measurement through the KDE; and
α is a maximum probability chosen from element-wise-multiplication between p mot k and p mea k .

2 . The method of claim 1 , wherein each of the plurality of location candidates includes coordinates in a three-dimensional space.

3 . The method of claim 2 , wherein the plurality of UWB anchors includes a first UWB anchor, a second UWB anchor, and a third UWB anchor, each of the plurality of location candidates of the UWB tag includes three-dimensional coordinates, and the three-dimensional coordinates for each of the plurality of location candidates is obtained using a following equations:

(

x
-

x
1

)

2

+

(

y
-

y
1

)

2

+

(

z
-

z
1

)

2

=

r
1
2

(

x
-

x
2

)

2

+

(

y
-

y
2

)

2

+

(

z
-

z
2

)

2

=

r
2
2

(

x
-

x
3

)

2

+

(

y

Assignees

• Gm Global Tech Operations Llc

Inventors

• Han Zijun
• Chen Jinzhu
• Adler Aaron
• Bai Fan
• Sergakis John

Classifications

• G01S5/02213

  Receivers arranged in a network for determining the position of a transmitter · CPC title

• G01S5/0278 Primary

  involving statistical or probabilistic considerations (G01S5/0252, G01S5/0294 take precedence) · CPC title

• G01S2205/01

  specially adapted for specific applications · CPC title

• G01S5/14 Primary

  Determining absolute distances from a plurality of spaced points of known location · CPC title

• H04W4/38

  for collecting sensor information · CPC title