IP Library Granted Patent US 12,418,880
Granted Patent B1
US 12,418,880 · App. 19/009,316 · Granted Sep 16, 2025

GNSS and RSSI integrated positioning method considering mac address

Inventors: Ying Xu (Qingdao, CN); Hongzhan Zhou (Qingdao, CN); Zhikun Li (Qingdao, CN); Guangxu Zhang (Qingdao, CN); Jinjie Sun (Qingdao, CN); Tengfei Zhang (Qingdao, CN); Zhihao Zheng (Qingdao, CN)
H04W64/00H04W8/26
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 12,418,880
App. No.
19/009,316
Granted
Sep 16, 2025
Kind
B1
Abstract

The present disclosure relates to the technical field of dense urban area positioning, and specifically discloses a GNSS and RSSI integrated positioning method considering MAC addresses. The method includes the following steps: arranging transmitting nodes and classifying MAC addresses of the transmitting nodes, collecting RSSI raw data; receiving RSSI data and MAC address data by the receiving nodes, and selecting one of the trilateration, two-point positioning, and proximity positioning for positioning based on the MAC address category; collecting GNSS data by the receiving nodes, and uses a differential positioning model to obtain GNSS positioning results; performing weighted fusion of GNSS and RSSI positioning results to ensure positioning accuracy in dense urban areas.

Claims (287)

1. A GNSS and RSSI integrated positioning method considering MAC address, comprising the following steps:

step 1, arranging transmitting nodes, classifying MAC addresses of the transmitting nodes, and using a receiving node to collect RSSI raw data, wherein the transmitting node is BLE Beacon, and the receiving node is a mobile terminal with BLE function;

a process of classifying the MAC addresses of the transmitting nodes is as follows:

labeling a MAC address of the transmitting node with a power of −40 dBm as MACW;

labeling a MAC address of the transmitting node with a power of −8 dBm and located in a special area as MACSb; labeling a MAC address of the transmitting node with a power of −8 dBm and located in an ordinary area as MACSa;

wherein the special area refers to a narrow and elongated area in dense urban area, while the ordinary area are relative to the special area, other area in dense urban area except for the special area is defined as the ordinary area;

step 2, receiving MAC addresses of the transmitting nodes when the receiving node receives the RSSI from the transmitting nodes at the same time, and selecting one of a trilateration method, a two-point positioning method, or a proximity positioning method for MAC address category;

using RSSI based proximity positioning method when the received MAC addresses belongs to the MACW;

otherwise, sorting the RSSI values received from the transmitting nodes in descending order and selecting the first three RSSI values received, if the MAC addresses corresponding to the first two RSSI values in the first three RSSI values belong to the MACSb, using the two-point positioning method; if the MAC addresses corresponding to the first three RSSI values in descending order belong to the MACSa, using the trilateration positioning method;

the specific calculation process for MAC address category is as follows:

when MAC⊆MACSa,

X

^

=

(

A

T

A

)

-

1

A

T

b

;

when MAC⊆MACSb,

[

X

,

Y

]

=

[

d

1

x

2

+

d

2

x

1

d

1

+

d

2

,

d

1

y

2

+

d

2

y

1

d

1

+

d

2

]

;

when MAC⊆MACW, [X, Y=][x Beacon_weak , y Beacon_weak ];

in the formula, X and Y represent an estimated coordinate of the receiving point, and {circumflex over (X)} represents a coordinate obtained by least squares estimation in the trilateration method;

A

=

[

2

(

x

1

-

x

3

)

2

(

y

1

-

y

3

)

2

(

x

2

-

x

3

)

2

(

y

2

-

x

3

)

]

;

b

=

[

x

1

2

-

x

3

2

+

y

1

2

-

y

3

2

+

d

3

2

-

d

1

2

x

2

2

-

x

3

2

+

y

2

2

-

y

3

2

+

d

3

2

-

d

2

2

]

;

wherein, (x 1 , y 1 ), (x 2 , y 2 ), and (x 3 , y 3 ) are coordinates of the transmitting nodes corresponding to the first three RSSI values received in descending order in the trilateration method, and d 1 , d 2 and d 3 are distances from an unknown node to the three transmitting nodes in the trilateration method, respectively;

(x 1 ′, y 1 ′), (x 2 ′, y 2 ′) are coordinates of the transmitting nodes corresponding to the first two RSSI values received in descending order, and d 1 ′ and d 2 ′ are distances from an unknown node to two transmitting nodes in the two-point positioning method, respectively;

(x Beacon_weak , y Beacon_weak ) are coordinates of the transmitting nodes obtained based the proximity positioning method of the RSSI;

step 3, collecting, by the receiving node, GNSS data, and using a differential positioning model to obtain GNSS positioning results;

step 4, after independent positioning of GNSS and RSSI, performing weighted fusion on the positioning results of GNSS and RSSI.

2. The GNSS and RSSI integrated positioning method considering MAC address according to claim 1 , wherein in step 1, the transmitting nodes are arranged in a cross setting on both sides of a road, with a distance of 5-10 meters between adjacent transmitting nodes.

3. The GNSS and RSSI integrated positioning method considering MAC address according to claim 1 , wherein in step 1, a method for determining the special area is as follows:

a maximum value of cosine value of interior angles of a triangle composed of three transmitting nodes in the trilateration method is defined as collinearity, with a range from 0.5 to 1.0, and an area with collinearity greater than 0.9 is defined as the special area.

4. The GNSS and RSSI integrated positioning method considering MAC address according to claim 1 , wherein in step 2, defining a set of positioning results obtained from RSSI within a GNSS epoch as:

L RSSI ={( X 1 ,Y 1 ),( X 2 ,Y 2 ), . . . ,( X h ,Y h )};

wherein, h represents the number of effective positioning results of RSSI when the GNSS effective positioning result is output once; (X 1 , Y 1 ), (X 2 , Y 2 ), . . . , (X h , Y h ) respectively represent a first, a second, . . . , and a h-th positioning results of RSSI;

obtaining an average weighted positioning coordinates L RSSI of RSSI positioning, and a calculation formula is as follows:

L

_

RSSI

=

1

h

(

X

1

,

Y

1

)

+

1

h

(

X

2

,

Y

2

)

+

+

1

h

(

X

h

,

Y

h

)

.

5. The GNSS and RSSI integrated positioning method considering MAC address according to claim 4 , wherein the process of performing the weighted fusion of GNSS and RSSI positioning results in step 4 is as follows:

defining GNSS positioning coordinates obtained within a GNSS measurement epoch as L GNSS ;

determining weights of the coordinates obtained by RSSI positioning and GNSS positioning, defining a variance obtained by GNSS independent positioning as σ GNSS 2 , and a variance obtained by RSSI independent positioning as σ RSSI 2 ;

let

P

GNSS

=

1

σ

GNSS

2

,

P

RSSI

=

1

σ

RSSI

2

,

a calculation formula for a final positioning result is as follows:

L

final

=

{

L

GNSS

P

GNSS

+

L

_

RSSI

P

RSSI

P

GNSS

+

P

RSSI

,

MAC

MACS

(

x

Beacon_weak

,

y

Beacon_weak

)

,

MAC

MACQ

;

wherein, L Final represents the positioning result of GNSS and RSSI integrated positioning method considering MAC address; MACW and MACS represent the MAC addresses of the transmitting nodes with power settings of −40 dBm and −8 dBm, respectively.

Priority Claims (1)
CN 202410288068.8 · Mar 14, 2024 · national
References Cited (39)
US 8743727B2 · Selvam · 2014 [cited by examiner]
US 11143738B1 · Kalavakuru · 2021 [cited by examiner]
US 11828832B2 · Henry · 2023 [cited by examiner]
US 20130267242A1 · Curticapean · 2013 [cited by examiner]
US 20140248899A1 · Emadzadeh · 2014 [cited by examiner]
US 20140256347A1 · Lakhzouri · 2014 [cited by examiner]
US 20140274116A1 · Xu · 2014 [cited by examiner]
US 20150005016A1 · Palanki · 2015 [cited by examiner]
US 20160021511A1 · Jin · 2016 [cited by examiner]
US 20180352055A1 · He · 2018 [cited by examiner]
US 20200379696A1 · Konji · 2020 [cited by examiner]
US 20220007267A1 · Maattanen · 2022 [cited by examiner]
US 20220066010A1 · Henry · 2022 [cited by examiner]
US 20220070612A1 · Henry · 2022 [cited by examiner]
US 20220263700A1 · Dabbs et al. · 2022 [cited by applicant]
US 20220322038A1 · Baek · 2022 [cited by applicant]
US 20230164736A1 · Kim · 2023 [cited by examiner]
US 20230292186A1 · Simoniy · 2023 [cited by examiner]
US 20230328656A1 · Rudolf · 2023 [cited by examiner]
US 20240045046A1 · Henry · 2024 [cited by examiner]
US 20250227787A1 · Kim · 2025 [cited by examiner]
CN 102573053A · 2012 [cited by applicant]
CN 103885955A · 2014 [cited by applicant]
CN 107830862A · 2018 [cited by applicant]
CN 109525935A · 2019 [cited by applicant]
CN 109951798A · 2019 [cited by applicant]
CN 110118549A · 2019 [cited by applicant]
CN 114363808A · 2022 [cited by applicant]
CN 114449438A · 2022 [cited by applicant]
CN 117320148A · 2023 [cited by applicant]
KR 101850332B1 · 2018 [cited by applicant]
WO 2023005814A1 · 2023 [cited by applicant]
Shuaihao Zhao et al., “An indoor positioning technology based on low-power Bluetooth”, Beijing Surveying and Mapping, vol. 34, No. 2, Feb. 29, 2020, pp. 238-242. [cited by applicant]
Weiguo Guan et al., “Indoor and outdoor fusion localization method based on BeiDou pseudo-range difference and WiFi”, Transducer and Microsystem Technologies, vol. 38, No. 5, May 31, 2019, pp. 13-16. [cited by applicant]
Jianwei Niu et al., “Indoor localization system based on multi-information fusion”, Chinese Journal on Internet of Things, vol. 1, No. 1, Jun. 30, 2017, pp. 55-66. [cited by applicant]
Tingting Huang et al., “Research on indoor positioning model based on multi-source data fusion”, Modern Electronics Technique, vol. 43, No. 14, Jul. 31, 2020, pp. 21-29. [cited by applicant]
Hui Tian et al., “A novel method for metropolitan-scale Wi-Fi localization based on public telephone booths”, IEEE/ION Position,Location and Navigation Symposium, Jul. 8, 2010, pp. 357-364. [cited by applicant]
Yu Chen et al., “PDR/iBeacon integration indor positioning algorithm based on mobile intelligent terminal”, Beijing Surveying and Mapping, vol. 37, No. 2, Feb. 28, 2023, pp. 280-288. [cited by applicant]
Iemei Zhang, “Study of feature selection algorithms in Indoor Positioning System”, SOFTWARE, vol. 36, No. 1, Jan. 31, 2015, pp. 38-46. [cited by applicant]