US 10,884,112 B2Granted

Fingerprint positioning method and system in smart classroom

AssigneeCentral China Normal University

InventorsZongkai Yang, Sannvya Liu, Zhicheng Dai, Zengzhao Chen, Xiuling He

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Abstract

The disclosure discloses a fingerprint positioning method in a smart classroom, which is specifically: firstly, performing Gaussian filtering and taking the average value on a wireless signal strength value RSSI in the fingerprint database; then finding the neighbor point closest to the signal strength of the to-be-measured point; finally, the Euclidean distance is used as the weight reference, and the weighted center of mass is obtained for the nearest neighbor points. The weight index is introduced as an index of the weight, and the coordinates of the to-be-tested node are obtained. The disclosure has a higher positioning accuracy, smaller positioning error fluctuations and greater environmental adaptability.

Claims4

Dependent claims

A fingerprint positioning method in a smart classroom, comprising an offline database establishing phase and an online positioning phase;

wherein, specifically, the offline database establishing phase comprises:
(S1) arranging P iBeacons inside a room as wireless signal transmitting terminals;
gridding the room, defining an intersection point of a grid as an anchor node, recording a physical coordinate of the nth anchor node Ln=(xn, yn), n=1 to N;
(S2) collecting a signal strength value RSSIijn transmitted by the jth iBeacon at the nth anchor node by a Bluetooth device, at the ith sampling moment, n=1 to N, i=1 to M, j=1 to P;
(S3) Gaussian filtering and averaging the M signal strength values transmitted by the jth iBeacon collected at the nth anchor node by the Bluetooth device by a data processing center, and an average is used as a standard signal strength value of the jth iBeacon collected at the nth anchor node and recorded as an original fingerprint Fn=(PRan1, . . . , PRanP), n=1 to N, comprising:
at the nth anchor node, n=1 to N, determining a confidence interval of the signal strength
(
RSSI
j
n
_
-
0.845
⁢
σ
n
M
,
RSSI
j
n
_
+
0.845
⁢
σ
n
M
)
by the data processing center, wherein,
RSSI
j
n
_
=
1
M
⁢
∑
i
=
1
M
⁢
RSSI
ij
n
,
σ
n
=
1
M
-
1
⁢
∑
i
=
1
M
⁢
(
RSSI
ij
n
-
RSSI
j
n
_
)
2
,
j
=
1
⁢
⁢
to
⁢
⁢
P
;
at nth anchor node, n=1 to N, extracting M′ signal strengths RSSIi′, jn, i′=1 to M′ in the confidence interval, calculating the average of the extracted M′ signal strengths
P
Ranj
=
1
M
′
⁢
∑
i
′
=
1
M
′
⁢
RSSI
i
′
⁢
j
n
,
j
=
1
⁢
⁢
to
⁢
⁢
P
by the data processing center;
(S4) constituting a fingerprint database, n=1 to N, by the physical coordinate Ln=(xn, yn) at the nth anchor node and a corresponding original fingerprint Fn=(PRan1, . . . , PRanP) by the data processing center;
wherein, specifically, the online positioning phase comprises:
(T1) collecting a signal strength value RSSIJ transmitted by the jth iBeacon at a to-be-measured point by the Bluetooth device, j=1 to P;
(T2) according to the signal strength value RSSIj, j=1 to P, searching for K anchor nodes that are the closest to its signal strength value in the fingerprint database as the reference anchor nodes by the data processing center;
(T3) calculating a coordinate (X, Y) of the to-be-measured point according to the reference anchor nodes by the data processing center:
{
X
=
ω
1
⁢
x
1
+
ω
2
⁢
x
2
+
…
+
ω
k
⁢
x
k
Y
=
ω
1
⁢
y
1
+
ω
2
⁢
y
2
+
…
+
ω
k
⁢
y
k
,
(xk, yk) is the coordinate of the kth reference anchor node among the searched K anchor nodes, ωk is a weight coefficient, k=1 to K;
wherein in the weight coefficient
ω
k
=
(
1
/
d
k
)
β
∑
k
=
1
K
⁢
(
1
/
d
k
)
β
,
dk represents an Euclidean distance between the to-be-measured point and the kth reference anchor node, β is a second weight index.

190 more elements

A fingerprint positioning system in a smart classroom, comprising:

P iBeacon wireless signal transmitting terminals, a Bluetooth device and a data processing center;
iBeacon for transmitting a wireless signal;
Bluetooth device for collecting the wireless signal;
a data processing center configured to position a to-be-measured point according to the collected wireless signal, specifically:
comprising an offline database establishing phase and an online positioning phase;
wherein, specifically, the offline database establishing phase comprises:
(S1) gridding a room, gridding an intersection point as an anchor node, recording a physical coordinate of the nth anchor node Ln=(xn, yn), n=1 to N;
(S2) collecting a signal strength value RSSIijn transmitted by the jth iBeacon at the nth anchor node by the Bluetooth device, at the ith sampling moment, n=1 to N, i=1 to M, j=1 to P;
(S3) Gaussian filtering and averaging the M signal strength values transmitted by the jth iBeacon collected at the nth anchor node by the Bluetooth device by a data processing center, and an average is used as a standard signal strength values of the jth iBeacon collected at the nth anchor node and recorded as an original fingerprint Fn=(PRan1, . . . , PRanP), n=1 to N, comprising:
at the nth anchor node, n=1 to N, determining a confidence interval of the signal strength
(
RSSI
j
n
_
-
0.845
⁢
σ
n
M
,
RSSI
j
n
_
+
0.845
⁢
σ
n
M
)
by the data processing center, wherein,
RSSI
j
n
_
=
1
M
⁢
∑
i
=
1
M
⁢
RSSI
ij
n
,
σ
n
=
1
M
-
1
⁢
∑
i
=
1
M
⁢
(
RSSI
ij
n
-
RSSI
j
n
_
)
2
,
j
=
1
⁢
⁢
to
⁢
⁢
P
;
at nth anchor node, n=1 to N, extracting M′ signal strengths RSSIijn=1 to M′ in the confidence interval, calculating the average of the extracted M′ signal strengths
P
Ranj
=
1
M
′
⁢
∑
i
′
=
1
M
′
⁢
RSSI
i
′
⁢
j
n
,
j
=
1
⁢
⁢
to
⁢
⁢
P
by the data processing center;
(S4) constituting a fingerprint database, n=1 to N, by a physical coordinate Ln=(xn, yn) at the nth anchor node and a corresponding original fingerprint Fn=(PRan1, . . . , PRanP) by the data processing center;
wherein, specifically, the online positioning phase comprises:
(T1) collecting the signal strength value RSSIj transmitted by the jth iBeacon at a to-be-measured point by the Bluetooth device, j=1 to P;
(T2) according to the signal strength value RSSI1, j=1 to P searching for the K anchor nodes that are the closest to its signal strength value in a fingerprint database as a reference anchor nodes by the data processing center;
(T3) calculating a coordinate (X, Y) of the to-be-measured point according to the reference anchor nodes by the data processing center:
{
X
=
ω
1
⁢
x
1
+
ω
2
⁢
x
2
+
…
+
ω
k
⁢
x
k
Y
=
ω
1
⁢
y
1
+
ω
2
⁢
y
2
+
…
+
ω
k
⁢
y
k
,
(xk, yk) is the coordinate of the kth reference anchor node among the searched K anchor nodes, ωk is a weight coefficient, k=1 to K;
wherein in the weight coefficient
ω
k
=
(
1
/
d
k
)
β
∑
k
=
1
K
⁢
(
1
/
d
k
)
β
,
dk represents an Euclidean distance between the to-be-measured point and the kth reference anchor node, β is a second weight index.

194 more elements

Dependencies are inferred from the claim text.