CN106793078B - Bluetooth indoor positioning method based on RSSI correction value dual positioning - Google Patents

Bluetooth indoor positioning method based on RSSI correction value dual positioning Download PDF

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CN106793078B
CN106793078B CN201710006007.8A CN201710006007A CN106793078B CN 106793078 B CN106793078 B CN 106793078B CN 201710006007 A CN201710006007 A CN 201710006007A CN 106793078 B CN106793078 B CN 106793078B
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ibeacon
value
matrix
signal strength
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CN106793078A (en
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杨刚
黄叶超
陈蒙
黄子明
陈建安
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Xian University of Electronic Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
    • G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
    • G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0252—Radio frequency fingerprinting
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0273—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves using multipath or indirect path propagation signals in position determination
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/10—Position of receiver fixed by co-ordinating a plurality of position lines defined by path-difference measurements, e.g. omega or decca systems

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention discloses a Bluetooth indoor positioning method based on RSSI (received signal strength indicator) correction value dual positioning, which has the scheme that: 1. collecting and correcting the received signal strength value of each iBeacon node received by other iBeacon nodes to obtain a received signal strength value correction matrix of the iBeacon nodes; 2. calculating a distance matrix from each node to other nodes according to the correction matrix; 3. estimating coordinate values of all nodes by the distance matrix to obtain coordinate errors; 4. collecting and correcting the signal intensity value received by the iBeacon node of the node to be positioned to obtain a signal intensity correction matrix of the node to be positioned; 5. obtaining a distance matrix from the node to be positioned to other iBeacon nodes according to the correction matrix, and further obtaining a coordinate estimation value of the node to be positioned; 6. and obtaining the final coordinate of the node to be positioned according to the results of the step 3 and the step 5. The invention has high positioning precision and can be used for nursing homes and intelligent communities.

Description

Bluetooth indoor positioning method based on RSSI correction value dual positioning
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a Bluetooth indoor positioning method which can be used for shopping malls, nursing homes, smart communities and fire-fighting places.
Background
With the rapid development of the internet of things society, sensing intelligent equipment is more and more widely applied to the intelligent society with the advantages of low power consumption, self-organization, convenient layout and the like, indoor positioning becomes an important support field of the internet of things society, and an indoor positioning technology based on the bluetooth iBeacon becomes one of hot spots of indoor positioning with unique superiority.
Currently, the indoor positioning algorithm based on bluetooth mainly includes an algorithm TOA based on signal transmission time, an algorithm TDOA based on signal transmission time difference, an algorithm AOA based on signal arrival angle, an algorithm based on received signal strength value RSSI, and a simple centroid algorithm, wherein:
the TOA algorithm has the advantages that because the indoor environment space is relatively narrow, the propagation of signals is interfered by obstacles, the propagation time delay can be caused, the time delay superposition can be generated, meanwhile, the algorithm requires accurate synchronization of clocks between a positioning node and a reference node, the requirement on hardware is high, the complexity and the cost of the system are high, and the practicability is low.
The TDOA algorithm is an improvement of a TOA algorithm, the TDOA algorithm determines the position of a positioning node by using the time difference of a signal reaching two reference nodes instead of directly using the absolute time of the signal reaching the reference nodes, so that the accurate synchronization of clocks between the reference nodes and the positioning node is not needed, only the clock synchronization between the reference nodes is needed, and the algorithm reduces the requirement of the clock synchronization, but has lower accuracy.
The AOA algorithm needs to install an antenna matrix on a node to obtain angle information, but because antennas of most nodes are omnidirectional and cannot distinguish which direction a signal comes from, and special hardware equipment, such as an antenna array, is needed to install the antennas on a bluetooth node, the energy consumption, size and price of the bluetooth node exceed those of a common sensing node, and the wireless smart device is contrary to the characteristics of low cost and low power consumption, so that the practicability is poor.
And the RSSI algorithm is used for obtaining a model between the distance and the RSSI by obtaining the relation between the received signal strength value RSSI and the distance, so as to carry out indoor positioning. However, the RSSI value of the bluetooth signal is greatly changed along with the change of the distance due to the free attenuation effect, the signal absorption effect, the non-line-of-sight propagation effect, the multipath effect and the shadow effect of the radio signal in the indoor environment, so that the error range of the traditional positioning algorithm based on the RSSI value of the received signal strength is large. However, due to low clock processing requirements, low cost and low complexity, a general indoor positioning algorithm is developed based on RSSI.
The simple centroid algorithm is that a positioning node receives information of all reference nodes in a communication range of the positioning node, and positions the geometric centroid of the reference node as an estimated position of the positioning node, but the number of centroids is single, the influence degree of the reference node far away from a node to be positioned is small, the contribution degree of the reference node far away from the node to be positioned cannot be highlighted, the error is large, and the positioning accuracy is not further improved.
Disclosure of Invention
The invention aims to provide a Bluetooth indoor positioning method based on RSSI (received signal strength indicator) correction value dual positioning to improve the indoor positioning accuracy of Bluetooth iBeacon, aiming at overcoming the defects of the prior art.
In order to achieve the above purpose, the technical scheme of the invention comprises the following steps:
(1) the method comprises the following steps of collecting received signal strength values of other iBeacon nodes received by each indoor deployed iBeacon node to form an iBeacon node signal strength matrix R, correcting the iBeacon node signal strength matrix R by using a Dixon detection method and a Gaussian filtering algorithm to obtain signal strength correction values of the iBeacon nodes, and forming an iBeacon node signal strength correction matrix R':
wherein:
i. j is the iBeacon node number, n is the iBeacon node number, i belongs to [1, n ], j belongs to [1, n ], and n is greater than 3;
Rij＝[Rij1 Rij2 Rij3 ... Rij30]the matrix is a 1 multiplied by 30 dimensional matrix, which indicates that the ith iBeacon node receives 30 groups of signal values sent by the jth iBeacon node;
Rij' indicates that the ith iBeacon node receives the signal strength correction value of the jth iBeacon node.
(2) According to the iBeacon node signal strength correction matrix R', the distance value from the iBeacon node to other iBeacon nodes is obtained by utilizing a logarithmic distance path loss model, and an iBeacon node distance matrix D is formed:
wherein: dijRepresents the distance value from the ith iBeacon node to the jth iBeacon node, when i equals j, Dij＝0。
(3) Obtaining each iBeacon node coordinate estimated value Q (x) by adopting a multi-centroid algorithm according to the iBeacon node distance matrix Di,yi) Mixing Q (x)i,yi) With the actual coordinates e (v) of each iBeacon nodei,zi) Comparing to obtain an error point P (alpha, beta);
(4) collecting a received signal intensity value sent by a node to be positioned for receiving an iBeacon node to form a positioning node signal intensity matrix r, and correcting the positioning node signal intensity matrix r by using a Dixon detection method and a Gaussian filtering algorithm to obtain an intensity correction value of the node to be positioned, so as to form a signal intensity correction matrix r' of the node to be positioned:
r＝[r1 r2 r3 ... ri ... rn]，r'＝[r1' r2' r3' ... ri' ... rn']
wherein:
rithe matrix is a 1 multiplied by 30 dimensional matrix and represents that a node to be positioned receives 30 groups of signal values sent by the ith iBeacon node;
ri' indicates that the signal strength correction value of the ith iBeacon node is received by the node to be positioned.
(5) According to the signal intensity correction matrix r' of the node to be positioned, obtaining distance values from the node to be positioned to other iBeacon nodes by using a logarithmic distance path loss model, and forming a distance matrix d of the node to be positioned:
d＝[d1 d2 d3 ... di ... dn]
wherein: diThe distance value from the node to be positioned to the ith iBeacon node is obtained.
(6) Obtaining a coordinate estimation value W (x, y) of the node to be positioned by adopting a multi-centroid algorithm according to the distance matrix d of the node to be positioned;
(7) according to the coordinate estimated value W (x, y) and the error point P (alpha, beta), obtaining the final coordinate of the node to be positioned as follows: w (x + α, y + β).
Compared with the prior art, the invention has the main advantages that:
firstly, compared with the prior preprocessing, the method carries out the processing of the Dixon detection algorithm on the received signal strength value with larger fluctuation in the early stage, eliminates the received signal strength value with severe jitter, and then carries out Gaussian filtering processing on the retained data, thereby furthest retaining the received signal strength value with smaller jitter.
Secondly, compared with the existing path loss model, the optimal environment coefficient reference range is given according to a large amount of experimental data, so that the realization is simple, and the selected coefficient is more suitable for the indoor environment.
Thirdly, compared with the existing positioning algorithm, the method adopts the dual positioning algorithm to respectively calculate the coordinate error of the system and the estimated coordinate of the node to be positioned, so that the final coordinate of the node to be positioned can be calculated according to the coordinate error and the estimated coordinate of the node to be positioned.
Fourthly, compared with the existing positioning algorithm, the method has the advantages that the iBeacon nodes far away from the node to be positioned are grouped, the centroid of the iBeacon nodes is calculated, the centroid is applied to the iBeacon node group close to the node to be positioned, and then the centroid is calculated, so that the positioning accuracy is further improved, and meanwhile, the adaptability can be prevented from being changed along with the state change of the iBeacon nodes in the environment.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a comparison graph of the localization effect of the present invention and the existing single-centroid algorithm.
Detailed Description
Referring to fig. 1, the bluetooth indoor positioning method based on RSSI correction value dual positioning of the present invention comprises the following specific implementation steps:
step 1, constructing an iBeacon node signal intensity matrix R.
Divide into a plurality of regions with regular hexagon with the indoor environment, place an iBeacon node at each summit department in region, place n a plurality of iBeacon nodes altogether, every iBeacon node periodically broadcasts self number, self coordinate and received signal intensity value, gathers the received signal intensity value of other iBeacon nodes that each iBeacon node received, constitutes iBeacon node signal intensity matrix R:
wherein: i. j is the iBeacon node number, n is the iBeacon node number, i belongs to [1, n ], j belongs to [1, n ], and n is greater than 3;
Rij＝[Rij1 Rij2 Rij3 ... Rij30]the matrix is a 1 × 30 dimensional matrix, which indicates that the ith iBeacon node receives 30 groups of signal values sent by the jth iBeacon node.
Step 2, correcting the iBeacon node signal intensity matrix R to obtain an iBeacon node Dixon matrix R*。
2a) Eliminating the severely changed received signal strength values in 30 groups of signal values received by each iBeacon node and sent by other iBeacon nodes by adopting a Dixon detection method, and arranging the 30 groups of received signal strength values received by the ith iBeacon node and the jth iBeacon node from small to large in sequence, namely Rij1,Rij2,...,Rij30；
2b) Determining a detection level of abnormal value detection as a 0.02, and determining a dixon detection critical value M (a, n);
2c) calculating an abnormal value G at the highest end of the iBeacon node and an abnormal value G' at the lowest end of the iBeacon node according to a statistical formula of a Dixon test method:
2d) comparing the iBeacon node highest abnormal value G and the iBeacon node lowest abnormal value G' with a critical value M (a, n):
if G is>M (a, n) or G'>M (a, n), eliminating the received signal strength value R corresponding to the abnormal valueijN；
If G ≦ M (a, n) or G' ≦ M (a, n), the exception value is retainedCorresponding received signal strength value RijNExecution 2e), N is the number of 30 sets of signal values;
2e) reordering the reserved received signal strength values, repeating the steps 2a) -2d) until all the received signal strength values with severe change are eliminated, and taking the final reserved value as the output of a Dixon detection algorithm to form an iBeacon node Dixon matrix R*：
Wherein:
R* ij＝[R* ij1 R* ij2 R* ij3 ... R* ijM]is a 1 × M dimensional matrix, M is the number of signal values retained after the Dixon detection algorithm in 30 sets of signal values, R* ijThe method comprises the steps that a signal value matrix reserved after a Dixon detection algorithm is carried out on 30 groups of signal values sent by the jth iBeacon node is received by the ith iBeacon node;
step 3, carrying out dixon matrix R on iBeacon nodes*And correcting to obtain an iBeacon node signal intensity correction matrix R'.
3a) Dixon matrix R for iBeacon nodes*Carrying out Gaussian filtering to obtain an iBeacon node Gaussian matrix R;
3b) taking an arithmetic mean value of each element matrix of the iBeacon node Gaussian matrix R 'to obtain a received signal strength correction value of each iBeacon node, and forming an iBeacon node signal strength correction matrix R':
wherein: rij' indicates that the ith iBeacon node receives the signal strength correction value of the jth iBeacon node.
And 4, constructing an iBeacon node distance matrix D.
4a) Establishing a path loss relational expression of the distance between the received signal strength correction value and the iBeacon node:
wherein:
Rij' indicating that the ith iBeacon node receives the signal strength correction value of the jth iBeacon node;
q0represents the distance L between two iBeacon nodes0A corrected value of the reference signal strength at 1 meter;
Dijrepresents the distance value from the ith iBeacon node to the jth iBeacon node, when i equals j, Dij＝0；
xεAnd u is an environmental parameter representing the degree of influence of the spatial environment, xεIs the absolute value of the average power received 1 meter away from the iBeacon node, u is the path loss factor, xεThe optimal reference range is 41-47, and the optimal reference range is 2.15-4.3;
4b) converting the path loss relation of step 4a) into the form:
4c) obtaining a distance matrix D from each iBeacon node to all other iBeacon nodes according to the step 4 b):
step 5, calculating a coordinate estimation value Q (x) of each iBeacon nodei,yi)。
5a) The distance value D from the ith iBeacon node to the jth iBeacon nodeijSorting according to the sequence from small to large to obtain a distance set: di1,Di2,Di3,...Dij,...,DinWherein D isi1<Di2<Di3<...<Dij<...<Din；
5b) Subtracting the minimum D from each element in the set of distancesi1And obtaining a difference set:
0,ΔDi1,ΔDi2,ΔDi3,...,ΔDij,...,ΔDin；
5c) calculating the average value of each element in the difference value set
Wherein: delta Dij＝Dij-Di1Indicating the distance value D between the ith iBeacon node and the jth iBeacon nodeijAnd the minimum value Di1A difference of (d);
5d) dividing the iBeacon nodes intoSet A andthe set B is provided with m nodes, the 3 iBeacon nodes with the closest difference in the set B are divided into a group and m-2 groups, the mass center of each group is calculated, and the coordinate (x) of the mass center is calculatedk,yk)：
Wherein: k is an element of [1, m-2 ]]，(xk1,yk1)、(xk2,yk2)、(xk3,yk3) Is the coordinates of 3 iBeacon nodes in each group;
5e) regarding the centroid obtained in the step 5d) as a newly added iBeacon node, forming a polygon by m-2 centroid points and n-m iBeacon nodes in the set A, and calculating the centroid of the polygon by adopting a centroid algorithm to obtain the estimated coordinate Q (x) of the ith iBeacon nodei,yi)。
And 6, calculating an error point P (alpha, beta).
According to the actual coordinate e (v) of the ith iBeacon nodei,zi) And estimate coordinate Q (x)i,yi) And calculating the abscissa and the ordinate of the error point P:
obtaining an error point
And 7, constructing a signal intensity matrix r of the node to be positioned.
Divide into a plurality of regions with regular hexagon with the indoor environment, place an iBeacon node at each summit department in region, place n iBeacon nodes altogether, every iBeacon node periodically broadcasts self number, self coordinate and received signal intensity value, gathers the received signal intensity value that the node that awaits positioning received iBeacon node and sent, constitutes node signal intensity matrix r that awaits positioning:
r＝[r1 r2 r3 ... ri ... rn]
wherein: r isiThe matrix is a 1 multiplied by 30 dimensional matrix and represents that a node to be positioned receives 30 groups of signal values sent by the ith iBeacon node;
step 8, correcting the signal intensity matrix r of the node to be positioned to obtain a Dixon matrix r of the node to be positioned*。
8a) Eliminating sharply changed received signal strength values in 30 groups of signal values received by a node to be positioned and sent by other iBeacon nodes by adopting a Dixon detection method, and arranging 30 groups of received signal strength values received by the node to be positioned and sent by the ith iBeacon node from small to large in sequence, wherein the r is sequentiallyi1,ri2,...,ri30；
8b) Calculating an abnormal value g at the highest end of the node to be positioned and an abnormal value g' at the lowest end of the node to be positioned according to a statistical formula of a Dixon test method:
8c) comparing the abnormal value g at the highest end of the node to be positioned and the abnormal value g' at the lowest end of the node to be positioned with a critical value M (a, n):
if g is>M (a, n) or g'>M (a, n), eliminating the received signal strength value r corresponding to the abnormal valueiN；
If g is less than or equal to M (a, n) or g' ≦ M (a, n), the received signal strength value r corresponding to the abnormal value is retainediNExecution 8d), N is the number of 30 sets of signal values;
8d) reordering the reserved received signal strength values, repeating the steps 8a) to 8c) until all the received signal strength values with violent change are eliminated, and taking the final reserved value as the output of the Dixon detection algorithm to form a Dixon matrix r of the node to be positioned*：
r*＝[r1 * r2 * r3 * ... ri * ... rn *]
Wherein: r isi *＝[r* i1 r* i2 r* i3 ... r* iM]Is a 1 xM dimensional matrix, M is the number of signal values retained after the Dixon detection algorithm in 30 sets of signal values, ri *The node to be positioned receives a signal value matrix reserved after a Dixon detection algorithm in 30 groups of signal values sent by the ith iBeacon node.
Step 9, treating the dixon matrix r of the node to be positioned*And correcting to obtain a signal intensity correction matrix r' of the node to be positioned.
9a) Dixon matrix r of nodes to be positioned by adopting Gaussian filtering*Processing to obtain a Gaussian matrix r' of the node to be positioned;
9b) taking an arithmetic mean value of each element matrix of a Gaussian matrix r 'of the node to be positioned to obtain a received signal strength correction value of the node to be positioned, and forming a signal strength correction matrix r' of the node to be positioned:
r'＝[r1' r2' r3' ... ri' ... rn']
wherein: r isi' indicates that the signal strength correction value of the ith iBeacon node is received by the node to be positioned.
And step 10, constructing a distance matrix d of the nodes to be positioned.
10a) Establishing a path loss relational expression from the received signal strength correction value to the distance between the node to be positioned and the ith iBeacon node:
wherein:
ri' indicating that the node to be positioned receives the signal strength correction value of the ith iBeacon node;
q1represents the distance L between the node to be positioned and the iBeacon node0A corrected value of the reference signal strength at 1 meter;
dithe distance value from the node to be positioned to the ith iBeacon node is obtained.
10b) Converting the path loss relation of step 10a) into the form:
10c) obtaining a distance matrix d from the node to be positioned to all other iBeacon nodes according to the step 10 b):
d＝[d1 d2 d3 ... di ... dn]
and 11, calculating a coordinate estimation value W (x, y) of the node to be positioned.
11a) The distance value d from the node to be positioned to the ith iBeacon nodeiSequencing according to the sequence from small to large to obtain a set of distances to be positioned: dε＝{d1,d2,d3,...,di,...,dnIn which d is1<d2<d3<...<di<...<dn；
11b)Using the distance to be located to set dεEach element in (d) minus the minimum value d1To obtain the set d of undetermined differential valuesε'：
dε'＝{0,Δd1,Δd2,Δd3,...,Δdi,...,Δdn}，
Wherein: Δ di＝di-d1Which represents the distance value d from the node to be positioned to the ith iBeacon nodeiAnd a minimum value d1A difference of (d);
11c) calculating a set d of difference values to be positionedεAverage value of each element in `
11d) Divide iBeacon nodes into two sets, namelySet C of1Andset C of2In set C of2Having z nodes therein, and collecting C2Dividing the 3 iBeacon nodes with the closest phase difference into one group and dividing the group into z-2 groups, and calculating the mass center of each group and the coordinate (x) of the mass centerL,yL) Comprises the following steps:
wherein: l is an element of [1, z-2 ]]，(xL1,yL1)、(xL2,yL2)、(xL3,yL3) Is the 3 iBeacon node coordinates in each group.
11e) Regarding the centroid obtained in the step 11d) as a newly added iBeacon node again, and then adding z-2 centroid points and the set C1The n-z iBeacon nodes in the polygon form a polygon,and calculating the centroid of the polygon by adopting a centroid algorithm to obtain an estimated coordinate W (x, y) of the node to be positioned.
And step 12, calculating the final coordinate W (x + alpha, y + beta) of the node to be positioned.
According to the estimated coordinates W (x, y) of the node to be positioned obtained in the step 11 and the abscissa alpha and the ordinate beta of the error point P obtained in the step 6, calculating to obtain the final coordinates of the node to be positioned as follows:
the positioning effect of the invention is further analyzed by combining simulation experiments.
1. Conditions of the experiment
16 iBeacon nodes and 10 nodes to be positioned are arranged in the experiment under an indoor environment of 50 x 50 meters.
2. Content of the experiment
The 10 nodes to be positioned are respectively tested by the method and the conventional single centroid algorithm to obtain the coordinate values of the nodes to be positioned, and the result is shown in figure 2.
As can be seen from FIG. 2, the coordinate values of the node to be positioned measured by the method are compared with the actual coordinate values of the node to be positioned, the maximum value of the positioning accuracy error is 2.413 m, the minimum value is 0.75 m, and the average value is 1.59 m. The coordinate value of the node to be positioned measured by the existing centroid algorithm is compared with the actual coordinate value of the node to be positioned, the maximum value of the positioning accuracy error is 4.089 meters, the minimum value is 1.56 meters, and the average value is 2.75 meters.
Experiments show that: the invention improves the positioning precision by 25-35%, and has great advantages in indoor positioning.

Claims (5)

1. A Bluetooth indoor positioning method based on RSSI corrected value dual positioning comprises the following steps:
(1) the method comprises the following steps of collecting received signal strength values of other iBeacon nodes received by each indoor deployed iBeacon node to form an iBeacon node signal strength matrix R, correcting the iBeacon node signal strength matrix R by using a Dixon detection method and a Gaussian filtering algorithm to obtain signal strength correction values of the iBeacon nodes, and forming an iBeacon node signal strength correction matrix R':
wherein:
i. j is the iBeacon node number, n is the iBeacon node number, i belongs to [1, n ], j belongs to [1, n ], and n is more than 3;
Rij＝[Rij1 Rij2 Rij3 ... Rij30]the matrix is a 1 multiplied by 30 dimensional matrix, which indicates that the ith iBeacon node receives 30 groups of signal values sent by the jth iBeacon node;
Rij' indicating that the ith iBeacon node receives the signal strength correction value of the jth iBeacon node;
(2) according to the iBeacon node signal strength correction matrix R', the distance value from the iBeacon node to other iBeacon nodes is obtained by utilizing a logarithmic distance path loss model, and an iBeacon node distance matrix D is formed:
wherein: dijRepresents the distance value between the ith iBeacon node and the jth iBeacon node, when i equals j, Dij＝0；
(3) Obtaining a coordinate estimation value Q (x) of each iBeacon node by adopting a multi-centroid algorithm according to the iBeacon node distance matrix Di,yi) Mixing Q (x)i,yi) With the actual coordinates e (v) of each iBeacon nodei,zi) Comparing to obtain an error point P (alpha, beta), and performing the following steps:
3.1) estimating the coordinate estimation value Q (x) of each iBeacon nodei,yi)：
3.1a) distance value D from the ith iBeacon node to the jth iBeacon nodeijSorting according to the sequence from small to large to obtain a distance set: di1,Di2,Di3,...Dij,...,DinWherein D isi1＜Di2＜Di3＜...＜Dij＜...＜Din；
3.1b) subtracting the minimum D from each element in the set of distancesi1And obtaining a difference set:
0,ΔDi1,ΔDi2,ΔDi3,...,ΔDij,...,ΔDin
3.1c) calculating the mean value of each element in the difference set
Wherein: delta Dij＝Dij-Di1Indicating the distance value D between the ith iBeacon node and the jth iBeacon nodeijAnd the minimum value Di1A difference of (d);
3.1d) dividing the iBeacon nodes intoAndthe two sets A and B are set in the set B and have m nodes, the 3 iBeacon nodes with the closest difference in the set B are divided into a group and m-2 groups, the mass center of each group is calculated, and the coordinates (x) of the mass center are calculatedk,yk)：
Wherein: k is an element of [1, m-2 ]]，(xk1,yk1)、(xk2,yk2)、(xk3,yk3) Is the coordinates of 3 iBeacon nodes in each group;
3.1e) regarding the centroid obtained in step 3.1d) as the newly added iBeacon node, and then regarding m-2 centroid points and n-m iBeacos in the set An nodes form a polygon, the centroid of the polygon is calculated by adopting a centroid algorithm to obtain the ith iBeacon node estimated coordinate Q (x)i,yi)；
3.2) according to the actual coordinate e (v) of the ith iBeacon nodei,zi) And estimate coordinate Q (x)i,yi) And calculating the abscissa and the ordinate of the error point P:
(4) collecting a received signal intensity value sent by a node to be positioned for receiving an iBeacon node to form a positioning node signal intensity matrix r, and correcting the positioning node signal intensity matrix r by using a Dixon detection method and a Gaussian filtering algorithm to obtain an intensity correction value of the node to be positioned, so as to form a signal intensity correction matrix r' of the node to be positioned:
r＝[r1 r2 r3 ... ri ... rn]，r'＝[r1' r2' r3' ... ri' ... rn']
wherein:
rithe matrix is a 1 multiplied by 30 dimensional matrix and represents that a node to be positioned receives 30 groups of signal values sent by the ith iBeacon node;
ri' indicating that the node to be positioned receives the signal strength correction value of the ith iBeacon node;
(5) according to the signal intensity correction matrix r' of the node to be positioned, obtaining distance values from the node to be positioned to other iBeacon nodes by using a logarithmic distance path loss model, and forming a distance matrix d of the node to be positioned:
d＝[d1 d2 d3 ... di ... dn]
wherein: diThe distance value from the node to be positioned to the ith iBeacon node is obtained;
(6) obtaining a coordinate estimation value W (x, y) of the node to be positioned by adopting a multi-centroid algorithm according to the distance matrix d of the node to be positioned, and performing the following steps:
6.1) node to be positioned to the ith iBeacon nodeDistance value d of pointsiSequencing according to the sequence from small to large to obtain a set of distances to be positioned: dε＝{d1,d2,d3,...,di,...,dnIn which d is1＜d2＜d3＜...＜di＜...＜dn；
6.2) using the distance to be positioned to set dεEach element in (d) minus the minimum value d1To obtain the set d of undetermined differential valuesε'：
dε'＝{0,Δd1,Δd2,Δd3,...,Δdi,...,Δdn}，
Wherein: Δ di＝di-d1Which represents the distance value d between the node to be positioned and the ith iBeacon nodeiAnd a minimum value d1A difference of (d);
6.3) calculating the set d of undetermined differential valuesεAverage value of each element in `
6.4) dividing the iBeacon nodes intoSet C of1Andset C of2In set C of2Having z nodes therein, and collecting C2Dividing the 3 iBeacon nodes with the closest phase difference into one group and dividing the group into z-2 groups, and calculating the mass center of each group and the coordinate (x) of the mass centerL,yL) Comprises the following steps:
wherein: l is an element of [1, z-2 ]]，(xL1,yL1)、(xL2,yL2)、(xL3,yL3) Is the coordinates of 3 iBeacon nodes in each group;
6.5) regarding the centroid obtained in the step 6.4) as a newly added iBeacon node again, and then regarding z-2 centroid points and the set C1N-z iBeacon nodes in the position form a polygon, the centroid of the polygon is obtained by adopting a centroid algorithm, and the estimated coordinates W (x, y) of the node to be positioned are obtained by calculation;
(7) according to the coordinate estimated value W (x, y) and the error point P (alpha, beta), obtaining the final coordinate of the node to be positioned as follows: w (x + α, y + β).
2. The method of claim 1, wherein the iBeacon node signal strength matrix R is modified in step (1) by using a Dixon detection method and a Gaussian filter algorithm according to the following steps:
1.1) dividing an indoor environment into a plurality of regions by a regular hexagon, placing an iBeacon node at each vertex of each region, placing n iBeacon nodes in total, and periodically broadcasting a self number, a self coordinate and a received signal intensity value by each iBeacon node;
1.2) eliminating the receiving signal strength value which is changed severely in 30 groups of signal values received by each iBeacon node and sent by other iBeacon nodes by adopting a Dixon detection method:
1.2a) the 30 groups of received signal strength values received by the ith iBeacon node from the jth iBeacon node are arranged from small to large in sequence, and are sequentially Rij1,Rij2,...,Rij30；
1.2b) determining that the detection level of the abnormal value detection is a ═ 0.02, and determining a dixon test critical value M (a, n);
1.2c) calculating an abnormal value G at the highest end of the iBeacon node and an abnormal value G' at the lowest end of the iBeacon node according to a statistical formula of a Dixon test method:
1.2d) comparing the iBeacon node highest extreme outlier G and the iBeacon node lowest extreme outlier G' with a critical value M (a, n):
if G > M (a, n) or G' > M (a, n), eliminating the received signal strength value R corresponding to the abnormal valueijN；
If G is less than or equal to M (a, n) or G' isless than or equal to M (a, n), the received signal strength value R corresponding to the abnormal value is reservedijN1.2e) is performed, N is the number of 30 sets of signal values;
1.2e) reordering the retained received signal strength values, repeating steps 1.2a) -1.2d) until all strongly varying received signal strength values are rejected, and using the final retained value as the output of a dixon detection algorithm to form an iBeacon node dixon matrix R*：
Wherein: r* ij＝[R* ij1 R* ij2 R* ij3 ... R* ijM]Is a 1 × M dimensional matrix, M is the number of signal values retained after the Dixon detection algorithm in 30 sets of signal values, R* ijThe method comprises the steps that a signal value matrix reserved after a Dixon detection algorithm is carried out on 30 groups of signal values sent by the jth iBeacon node is received by the ith iBeacon node;
1.3) Dixon matrix R for iBeacon nodes*Carrying out Gaussian filtering processing to obtain an iBeacon node Gaussian matrix R ', then taking an arithmetic mean value for each element of the iBeacon node Gaussian matrix R' to obtain a received signal strength correction value of each iBeacon node, and forming an iBeacon node signal strength correction matrix:
3. the method of claim 1, wherein the distance value D between the ith iBeacon node and the jth iBeacon node is calculated in step (2)ijThe method comprises the following steps:
2.1) establishing a path loss relation between a received signal strength value and a distance:
wherein:
Rij' indicating that the ith iBeacon node receives the signal strength correction value of the jth iBeacon node;
q0represents the distance L between two iBeacon nodes0A corrected value of the reference signal strength at 1 meter;
xεand u is an environmental parameter representing the degree of influence of the spatial environment, xεIs the absolute value of the average power received 1 meter away from the iBeacon node, u is the path loss factor, xεThe optimal reference range is 41-47, and the optimal reference range is 2.15-4.3;
2.2) converting the path loss relation of step 2.1) into the following form:
2.3) obtaining a distance matrix D from each iBeacon node to all other iBeacon nodes according to the step 2.2):
4. the method of claim 1, wherein the signal strength matrix r of the node to be located is modified in step (4) by using a dixon detection method and a gaussian filtering algorithm, according to the following steps:
4.1) dividing an indoor environment into a plurality of regions by a regular hexagon, placing an iBeacon node at each vertex of each region, placing n iBeacon nodes in total, and periodically broadcasting a self number, a self coordinate and a received signal intensity value by each iBeacon node;
4.2) eliminating the received signal strength values which are severely changed in 30 groups of signal values received by the node to be positioned and sent by other iBeacon nodes by adopting a Dixon detection method:
4.2a) the 30 groups of received signal strength values received by the node to be positioned from the ith iBeacon node are arranged from small to large in sequence, and r is arranged in sequencei1,ri2,...,ri30；
4.2b) calculating the abnormal value g at the highest end of the node to be positioned and the abnormal value g' at the lowest end of the node to be positioned according to a statistical formula of a Dixon test method:
4.2c) comparing the abnormal value g at the highest end of the node to be positioned and the abnormal value g' at the lowest end of the node to be positioned with the critical value M (a, n):
if g > M (a, n) or g' > M (a, n), eliminating the received signal strength value r corresponding to the abnormal valueiN；
If g is less than or equal to M (a, n) or g' ≦ M (a, n), the received signal strength value r corresponding to the abnormal value is retainediNExecution 4.2d), N is the number of 30 sets of signal values;
4.2d) reordering the retained received signal strength values, repeating steps 4.2a) -4.2c) until all the strongly changed received signal strength values are eliminated, and using the final retained value as the output of the Dixon detection algorithm to form a Dixon matrix r of the node to be positioned*：
r*＝[r1 * r2 * r3 * ... ri * ... rn *]
Wherein: r isi *＝[r* i1 r* i2 r* i3 ... r* iM]Is a 1 xM dimensional matrix, M is the number of signal values retained after the Dixon detection algorithm in 30 sets of signal values, ri *Representing that a node to be positioned receives a signal value matrix reserved after a Dixon detection algorithm in 30 groups of signal values sent by the ith iBeacon node;
4.3) Dixon matrix r of nodes to be positionedi *Carrying out Gaussian filtering processing to obtain a Gaussian matrix r 'of the node to be positioned, then taking an arithmetic mean value of each element matrix of the Gaussian matrix r' of the node to be positioned to obtain a received signal strength correction value of the node to be positioned, and forming a signal strength correction matrix of the node to be positioned: r' ═ r1' r2' r3' ... ri' ... rn']。
5. The method of claim 1, wherein the distance value d between the node to be positioned and the ith iBeacon node is calculated in step (5)iThe method comprises the following steps:
5.1) establishing a path loss relation between a received signal strength value and a distance:
wherein:
ri' indicating that the node to be positioned receives the signal strength correction value of the ith iBeacon node;
q1represents the distance L between the node to be positioned and the iBeacon node0A corrected value of the reference signal strength at 1 meter;
xεand u is an environmental parameter representing the degree of influence of the spatial environment, xεIs the absolute value of the average power received 1 meter away from the iBeacon node, u is the path loss factor, xεThe optimal reference range is 41-47, and the optimal reference range is 2.15-4.3;
5.2) converting the path loss relation of step 5.1) into the following form:
5.3) obtaining a distance matrix d from the node to be positioned to all other iBeacon nodes according to the step 5.2):
d＝[d1 d2 d3 ... di ... dn]。
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