CN109640260B - Indoor Wi-Fi positioning method - Google Patents

Indoor Wi-Fi positioning method Download PDF

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CN109640260B
CN109640260B CN201811528874.9A CN201811528874A CN109640260B CN 109640260 B CN109640260 B CN 109640260B CN 201811528874 A CN201811528874 A CN 201811528874A CN 109640260 B CN109640260 B CN 109640260B
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positioning
rssi
frequency
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areas
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CN109640260A (en
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杨敬民
林敏敏
张文杰
陈国良
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Minnan Normal University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • 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
    • H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment

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  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses an indoor Wi-Fi positioning method, which comprises a positioning client, a positioning server and a plurality of positioning APs, and further comprises the following steps: a: initializing a positioning system: dividing a positioning area, and setting an acquisition point installation positioning AP; b: signal data acquisition: constructing a signal intensity matrix and a signal distribution matrix; c: and (3) skew distribution filtering: according to the filtering threshold interval TD＝[Mo‑xσ‑,Mo+yσ+]Filtering abnormal points; d: positioning: the invention has the advantages of effectively filtering signal abnormal points, improving the positioning precision and the like.

Description

Indoor Wi-Fi positioning method
Technical Field
The invention relates to an indoor Wi-Fi positioning method.
Background
With the increasing maturity of wireless network technology, wireless networks have been accepted by more and more enterprise users, and at present, wireless local area networks become a hot spot in wireless network technology.
In step C of "a WiFi positioning method" with application number 201711220071.2, the method for filtering out signal outliers in the fingerprint database is: solving the mean value mu and the standard deviation sigma of the signal intensity of each positioning AP according to the signal intensity acquired in one sampling period T, wherein the election principle is that the first p good signal APs are taken according to the large mean value mu, if the mean value mu is equal, the standard deviation sigma is taken to be large, and if the mu and the sigma are equal, the MAC address is taken to be small; the main problems of this method are: when the AP signal fluctuates, the signal is particularly good or poor in a short time when the AP signal fluctuates, and particularly large or particularly small RSSI values directly participate in the operation of the signal strength mean value mu and the standard deviation sigma to influence the values of the signal strength mean value mu and the standard deviation sigma.
The concrete expression is as follows: the first condition is as follows: when the sampling period T is very small, AP signals continuously fluctuate in the period T, only good or poor signals of the AP are often acquired when the AP signals are unstable, bipolar differentiation occurs on the calculated signal mean value mu, or the AP with signal fluctuation caused by a very large mu value is directly elected as the AP with good signals, or the AP with signal fluctuation caused by a very small mu value is directly filtered out as the AP with poor signals; the essence of this situation is that the RSSI value when fluctuation occurs is taken as a normal RSSI value to make an erroneous evaluation on the average value μ and standard deviation σ of the signal strength of the whole AP, so that there is an error in election and the positioning accuracy is affected; case two: when the sampling period T is large, the AP signals in the period T fluctuate for a short time, the collected AP signals comprise fluctuating signals and normal signals, and the RSSI value of the fluctuating signals and the RSSI value of the normal signals participate in the calculation of the signal intensity mean value mu and the standard deviation sigma simultaneously to influence the subsequent election; the essence of this situation is that the RSSI value when fluctuation occurs is used as a part of the normal RSSI value to make wrong evaluation on the average value μ and standard deviation σ of the signal strength of the whole AP, which causes errors in election and further affects the positioning accuracy; the method cannot avoid the influence on the positioning accuracy when AP signal fluctuation occurs, namely the method cannot effectively filter out signal abnormal points in the fingerprint database; in the step D, when the method for filtering the signal outlier of the positioning data encounters signal fluctuation of the AP, similarly, it is impossible to effectively avoid that the signal fluctuation of the AP affects the positioning accuracy.
The 'WiFi positioning method' with application number 201810626172.8 can only determine that a point to be positioned is in a certain area, cannot determine the specific coordinates of the point to be positioned, and cannot meet the application scenario with higher positioning accuracy requirement.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an indoor Wi-Fi positioning method.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an indoor Wi-Fi positioning method, characterized in that: the method comprises the following steps of positioning a client, a positioning server and a plurality of positioning APs, and further comprises the following steps:
a: initializing a positioning system: dividing a region to be positioned into h positioning regions, wherein the number of each positioning region is AkSetting an acquisition point installation positioning AP in each positioning area, wherein h, k belongs to N*，1≤k≤h；
B: signal data acquisition: counting the RSSI of p APs collected by the positioning AP of each positioning area in m time periods t to construct a signal intensity matrix of each positioning area:
Figure GDA0002579315140000031
is stored in a location server, wherein,
Figure GDA0002579315140000032
indicates the positioning area AkThe set of RSSI of p APs acquired by the positioning AP in m time periods t,
Figure GDA0002579315140000033
indicates the positioning area AkAt tiAP is acquired in time periodjWherein i, j, m, p ∈ N*(ii) a Counting the RSSI and the frequency of occurrence f collected by all the positioning areas for each positioning AP to construct a signal distribution matrix:
Figure GDA0002579315140000034
is stored in a location server, wherein,
Figure GDA0002579315140000035
representing APjIn the positioning area AkSignal strength RSSIjAnd their appearanceFrequency of (f)j，APjRepresenting APjSignal strength in all location areas and frequency of occurrence thereof, wherein q ∈ N*Calculating APjIn the positioning area AkBased on the RSSI signal weighted mean value of the frequency of the signal intensity
Figure GDA0002579315140000036
C: and (3) skew distribution filtering: location area AkThe acquisition point acquires the APjThe RSSI of and the fingerprint library samples for each frequency of RSSI are:
Figure GDA0002579315140000037
judging the sample deflection distribution type of the fingerprint library, and determining that the RSSI value falls in a filtering threshold interval TD＝[Mo-xσ-,Mo+yσ+]The outer RSSI values are marked as outlier rejection, where MoIs APjIn the positioning area AkThe acquisition point of (2) acquires the RSSI value with the most frequent occurrence in the RSSI samples, i.e. the RSSI value
Figure GDA0002579315140000041
Wherein n is n1+n2+n3To n is paired-And n+By rounding to the whole, i.e. by rounding
Figure GDA0002579315140000042
n represents the sum of frequency numbers of RSSI values, n1A sum of frequencies, n, representing RSSI values in the sample less than a mode2A sum of frequency numbers representing RSSI values in the sample that are greater than a mode, n3Representing M in said sampleoX and y are adjustable constants, x > 0, y > 0, updating the weighted mean of the signals after filtering outliers
Figure GDA0002579315140000043
D: positioning: positioning client side to test AP at point X to be positionedjThe measured samples of RSSI and frequency of each RSSI are:
Figure GDA0002579315140000044
judging the actually measured sample deflection distribution type, marking the RSSI value of which the RSSI value falls outside the actually measured sample filtering threshold interval as an abnormal point, filtering, and calculating a signal weighted mean value
Figure GDA0002579315140000045
Selecting the weighted mean value of the X signal of the point to be located
Figure GDA0002579315140000046
K3 APs before the minimum absolute value, and signal distribution matrix AP for each APjMatching the positioning areas of the same skew distribution type of the same AP, the method for matching the same skew distribution type comprises respectively calculating fingerprint library samples
Figure GDA0002579315140000047
Skew coefficient and measured sample
Figure GDA0002579315140000048
Selecting positioning areas of the same AP and the same deflection distribution type through matching deflection coefficient values, selecting front K4 positioning areas with the minimum absolute value of the weighted mean difference value of signals of the to-be-positioned points for each positioning area of the same deflection distribution type, calculating the occurrence frequency of each positioning area for K4 positioning areas of K3 APs, selecting the front K5 positioning areas with the maximum occurrence frequency, and weighting the coordinates of the to-be-positioned points through the coordinates of acquisition points of K5 positioning areas, wherein K3, K4 and K5 are adjustable constants, K3, K4 and K5E N*。
In another preferred embodiment, in the step a, the acquisition point is set at a central point of each positioning area, and the positioning APs installed on each acquisition point are numbered by using a MAC address as an identifier.
In another preferred embodiment, in the step B, the algorithm for weighted mean of the RSSI signals is:
Figure GDA0002579315140000051
in another preferred embodiment, in the step B, the step B is to
Figure GDA0002579315140000052
Positioning area A of AP ofkSet to an unset state, where K1 is a tunable constant, K1 ∈ R.
In another preferred embodiment, in the step B, AP is calculatedjIn the positioning area AkTotal frequency of
Figure GDA0002579315140000053
Will be provided with
Figure GDA0002579315140000054
Positioning area A of AP ofkSet to an unset state, where K2 is a tunable constant, K2 ∈ N*。
In another preferred embodiment, in the step D, the step C is to
Figure GDA0002579315140000055
Is set to an undetermined state, where K1 'is an adjustable constant, and K1' e R.
In another preferred embodiment, in said step D, the AP measured by the positioning client at the point X to be positioned is calculatedjTotal frequency of
Figure GDA0002579315140000056
Will be provided with
Figure GDA0002579315140000057
Is set to be in an undetermined state, wherein K2 'is an adjustable constant, and K2' is belonged to N*。
In another preferred embodiment, in the step D, the algorithm for obtaining the coordinates of the to-be-located point by weighting the coordinates of the K5 location areas acquisition points includes an average weighting algorithm and an occurrence weighting algorithm, the average weighting algorithm includes assigning an average weight of 1/K5 to each acquisition point of K5 location areas, and then calculating the coordinates of the to-be-located point according to the average weight; the occurrence frequency weighting algorithm comprises the steps of giving corresponding occurrence frequency weights to all acquisition points of K5 positioning areas according to the occurrence frequency of each positioning area, and calculating the coordinates of the to-be-positioned points according to the occurrence frequency weights.
The invention has the beneficial effects that:
1. constructing a signal intensity matrix and a signal distribution matrix; the method comprises the steps of filtering abnormal points of signal mutation of signals in a server and actual measurement signals, reducing positioning errors, and matching the actual measurement signals with the server to calculate coordinates of a point to be positioned, wherein the abnormal points are filtered by adopting deflection distribution filtering, a mode of frequency is used as an important parameter selected by a filtering interval in the processes of constructing a fingerprint library, establishing a signal distribution matrix and acquiring positioning data, the occurrence frequency, namely the AP signal intensity with smaller frequency and the AP signal intensity with a particularly large difference with the mode are effectively filtered, the influence of the AP signal intensity on the AP signal intensity under normal conditions in the process of calculating the signal intensity mean value when signal fluctuation occurs is reduced, and a user can select the size of the filtering interval according to actual requirements to adjust the filtering intensity; the invention also filters AP with different deflection distribution types from the actually measured sample in the signal distribution matrix by judging whether the deflection distribution types of the sample in the signal distribution matrix and the actually measured sample are consistent, thereby improving the positioning precision; according to the method, the corresponding weight of the RSSI value is given according to the frequency of the signal intensity in the processes of establishing a signal distribution matrix and obtaining positioning data in the fingerprint database, the weight occupying the signal weighted mean value is larger when the frequency is larger, even if signal fluctuation occurs in the AP, the signal abnormal point with small frequency is not filtered by the filtering interval, the influence of the occupied weight on the signal weighted mean value is small when the signal weighted mean value is calculated because the frequency is low, the influence of the signal abnormal point with small frequency on the calculation of the whole signal intensity weighted mean value is greatly reduced, the error is effectively reduced, and the positioning accuracy is improved.
2. The acquisition point is arranged at the central point of the positioning area, the signal coverage efficiency of the positioning AP is improved, the number of each AP is numbered by using the MAC address of the AP as an identifier, the one-to-one correspondence between the number and the AP is realized, and the cost of secondary physical numbering is saved.
3. And filtering positioning areas corresponding to APs with smaller RSSI weighted mean and smaller total frequency for the data collected by the positioning server and the positioning client so as to improve the positioning accuracy.
4. To n-And n+Perform rounding operations to avoid n-And/or n+Influence of odd number
Figure GDA0002579315140000061
Wherein the calculation is accumulated.
5. By comparing the deflection coefficient of the sample in the signal distribution matrix with the deflection coefficient of the actually measured sample, the AP of different deflection distribution types of the signal distribution matrix and the actually measured sample is filtered, and the positioning precision is improved.
5. And an average weighting algorithm or an occurrence number weighting algorithm can be flexibly selected according to actual requirements and hardware configuration to obtain the positioning coordinates.
The invention is further explained in detail with the accompanying drawings and the embodiments; but a method of indoor Wi-Fi positioning of the present invention is not limited to the embodiments.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a positioning area according to a preferred embodiment of the present invention.
Detailed Description
In an embodiment, referring to fig. 1 and fig. 2, an indoor Wi-Fi positioning method according to the present invention includes a plurality of positioning APs, a positioning client, and a positioning server, and further includes the following steps
A: initializing a positioning system: dividing an area to be positioned into 9 positioning areas, wherein the number of each positioning area is A1-A9, acquisition points L1-L9 are respectively arranged at the central points of the positioning areas A1-A9, positioning APs are installed at the acquisition points L1-L7, the number of the positioning APs at the acquisition points L1-L7 is AP 1-AP 7 by adopting MAC addresses, the distance between each acquisition point is 1.2 m, the areas of each positioning area are approximately equal, wherein the coordinate of a position to be positioned X in the positioning area A5 is (1.2 ), and the coordinate distribution is shown in FIG. 2.
B: signal data acquisition: and acquiring signals once every other acquisition period t, wherein t is 10min, and for convenience, identifying the MAC address of each AP, constructing signal distribution matrixes by the APs 1 to 7 and storing the signal distribution matrixes in the positioning server, which are respectively shown in tables 1 to 7.
TABLE 1 Signal distribution matrix for each location area of AP1
Figure GDA0002579315140000081
TABLE 2 Signal distribution matrix for each positioning area of AP2
Figure GDA0002579315140000082
TABLE 3 Signal distribution matrix for each positioning area of AP3
Figure GDA0002579315140000083
TABLE 4 Signal distribution matrix for each positioning area of AP4
Figure GDA0002579315140000091
TABLE 5 Signal distribution matrix for each positioning area of AP5
Figure GDA0002579315140000092
TABLE 6 Signal distribution matrix for each positioning area of AP6
Figure GDA0002579315140000093
Figure GDA0002579315140000101
TABLE 7 Signal distribution matrix for each positioning area of AP7
Figure GDA0002579315140000102
For each AP, calculating a weighted mean of RSSI signals, and setting a positioning area of the AP with the weighted mean of the signals < K1 to be in an undetermined state, where K1 takes a value of-80 in this embodiment, and since the weighted mean of the signals of a positioning area a1 of AP7 is-83.07 less than-80, AP7 is set to be in an undetermined state.
For each AP, count the total frequency
Figure GDA0002579315140000103
Will be provided with
Figure GDA0002579315140000104
The positioning area of the AP in (1) is set to be in the non-positioning state, and the value of K2 in this embodiment is 100, because the total frequency of the positioning area a1 of the AP6 is 26, and the total frequency of the positioning area a2 is 50, both of which are less than 100, the AP6 is set to be in the non-positioning state.
B: and (3) skew distribution filtering: calculating a skew coefficient SK according to a fingerprint library sample, and determining a skew distribution type
Figure GDA0002579315140000105
And its corresponding probability density function, determining parameter Mo，σ-And σ+For RSSI value falling in filtering threshold interval TD＝[Mo-xσ-,Mo+yσ+]The outer RSSI values are marked as outlier rejection, where MoIs APjIn the positioning area AkThe acquisition point of (2) acquires the RSSI value with the most frequent occurrence in the RSSI samples, i.e. the RSSI value
Figure GDA0002579315140000106
Wherein n is n-+n+＝n1+n2+n3，
Figure GDA0002579315140000111
To n-And n+The rounding operation is carried out to round and round,avoidance of n-And/or n+Influence of odd number
Figure GDA0002579315140000112
Wherein the summation is calculated, n represents the sum of frequency of RSSI values, n1A sum of frequencies, n, representing RSSI values in the sample less than a mode2A sum of frequency numbers representing RSSI values in the sample that are greater than a mode, n3Representing M in said sampleoThe frequency of (a) is x and y are adjustable constants, x > 0, y > 0, in this embodiment, x ═ y ═ 2, and the filter parameters of the skewing distribution of each positioning area of each AP are shown in table 8.
Table 8 table of calculation results of skew distribution parameters of each AP in each positioning area
Figure GDA0002579315140000113
Figure GDA0002579315140000121
D: positioning: the positioning client collects data to be positioned at the point X to be positioned, see table 9.
Table 9 signal strength distribution matrix of point X to be located
Figure GDA0002579315140000131
For each AP, calculating an RSSI signal weighted mean, and setting the measured AP with the signal weighted mean < K1 'to be in an undetermined state, where K1' takes the value of-80 in this embodiment, and since the signal weighted mean of AP6 is less than-80, AP6 is set to be in an undetermined state.
Calculating a deflection coefficient aiming at the measured sample, determining a deflection distribution type and a probability density function corresponding to the deflection distribution type, marking the RSSI value of which the RSSI value falls outside a filtering threshold interval as an abnormal point, filtering, and referring to a table 10 for the calculation result of the deflection distribution parameters of each AP measured by the positioning client at the point X to be positioned.
Table 10 table of calculation results of X skew distribution parameters of points to be located
Figure GDA0002579315140000132
The signal strength matrix after the X skew distribution of the anchor points to be located is filtered is shown in the table 11.
TABLE 11 Signal Strength matrix after X-skew distribution filtering of the points to be located
Figure GDA0002579315140000133
The first K3 APs with the smallest weighted mean absolute value of the point to be located X are selected, for each AP, the first K4 positioning areas with the smallest RSSI weighted mean difference absolute value are selected from the positioning areas with the same skew distribution type by matching the positioning areas with the same AP in the same positioning server, in this embodiment, K3 is K4 is 3, and 3 areas of AP2, AP4, and AP5 are selected, see table 12.
TABLE 12 most likely location table for X location points to be located
Figure GDA0002579315140000141
Calculating the possible occurrence frequency of each positioning region, taking the first K5 most positioning regions, where K5 is 3, and taking the occurrence frequency of each positioning region as a weight, calculating a weighted average of coordinates of the to-be-positioned point, where the positioning region where the positioning point X may occur is shown in table 12.
TABLE 13 statistical order table of possible positioning regions for positioning point X
Region(s) (0，0) (0，1.2) (0，2.4) (1.2，0) (2.4，0) (2.4，1.2)
Number of times 1 2 1 2 1 2
Whether to select × √ × √ × √
Calculating the coordinate of the to-be-positioned point X:
Figure GDA0002579315140000142
the coordinates of the point to be positioned X obtained in this embodiment are (0.8,1.2), and the actual coordinates of X are (1.2 ), which has higher accuracy compared with the existing indoor Wi-Fi positioning technology.
The above embodiments are only used to further illustrate the method for indoor Wi-Fi positioning of the present invention, but the present invention is not limited to the embodiments, and any simple modifications, equivalent changes and modifications made to the above embodiments according to the technical spirit of the present invention fall within the protection scope of the technical solution of the present invention.

Claims (8)

1. An indoor Wi-Fi positioning method, characterized in that: the method comprises a positioning client, a positioning server and a plurality of positioning APs, and further comprises the following steps:
a: initializing a positioning system: dividing a region to be positioned into h positioning regions, wherein the number of each positioning region is AkSetting an acquisition point installation positioning AP in each positioning area, wherein h, k belongs to N*，1≤k≤h；
B: signal data acquisition: counting the RSSI of p APs collected by the positioning AP of each positioning area in m time periods t to construct a signal intensity matrix of each positioning area:
Figure FDA0002579315130000011
is stored in a location server, wherein,
Figure FDA0002579315130000012
indicates the positioning area AkThe set of RSSI of p APs acquired by the positioning AP in m time periods t,
Figure FDA0002579315130000013
indicates the positioning area AkAt tiAP is acquired in time periodjWherein i, j, m, p ∈ N*(ii) a Counting the RSSI and the frequency of occurrence f collected by all the positioning areas for each positioning AP to construct a signal distribution matrix:
Figure FDA0002579315130000014
is stored in a location server, wherein,
Figure FDA0002579315130000015
representing APjIn the positioning area AkSignal strength RSSIjAnd frequency of occurrence of fj，APjRepresenting APjSignal strength in all location areas and the frequency of occurrence thereofNumber, wherein q ∈ N*Calculating APjIn the positioning area AkBased on the RSSI signal weighted mean value of the frequency of the signal intensity
Figure FDA0002579315130000016
C: and (3) skew distribution filtering: location area AkThe acquisition point acquires the APjThe RSSI of and the fingerprint library samples for each frequency of RSSI are:
Figure FDA0002579315130000021
judging the sample deflection distribution type of the fingerprint library, and determining that the RSSI value falls in a filtering threshold interval TD＝[Mo-xσ-,Mo+yσ+]The outer RSSI values are marked as outlier rejection, where MoIs APjIn the positioning area AkThe acquisition point of (1) acquires the RSSI value with the most frequent occurrence in the RSSI samples, i.e. the RSSI value
Figure FDA0002579315130000022
Figure FDA0002579315130000023
Wherein n is n1+n2+n3To n is paired-And n+By rounding to the whole, i.e. by rounding
Figure FDA0002579315130000024
n represents the sum of frequency numbers of RSSI values, n1A sum of frequencies, n, representing RSSI values in the sample less than a mode2A sum of frequency numbers representing RSSI values in the sample that are greater than a mode, n3Representing M in said sampleoX and y are adjustable constants, x > 0, y > 0, updating the weighted mean of the signals after filtering outliers
Figure FDA0002579315130000025
D: positioning: positioning client side to test AP at point X to be positionedjRSSI of eachThe measured samples of the frequency of the RSSI are:
Figure FDA0002579315130000026
judging the actually measured sample deflection distribution type, marking the RSSI value of which the RSSI value falls outside the actually measured sample filtering threshold interval as an abnormal point, filtering, and calculating a signal weighted mean value
Figure FDA0002579315130000027
Selecting the weighted mean value of the X signal of the point to be located
Figure FDA0002579315130000028
K3 APs before the minimum absolute value, and signal distribution matrix AP for each APjMatching the positioning areas of the same skew distribution type of the same AP, the method for matching the same skew distribution type comprises respectively calculating fingerprint library samples
Figure FDA0002579315130000031
Skew coefficient and measured sample
Figure FDA0002579315130000032
Selecting positioning areas of the same AP and the same deflection distribution type through matching deflection coefficient values, selecting front K4 positioning areas with the minimum absolute value of the weighted mean difference value of signals of the to-be-positioned points for each positioning area of the same deflection distribution type, calculating the occurrence frequency of each positioning area for K4 positioning areas of K3 APs, selecting the front K5 positioning areas with the maximum occurrence frequency, and weighting the coordinates of the to-be-positioned points through the coordinates of acquisition points of K5 positioning areas, wherein K3, K4 and K5 are adjustable constants, K3, K4 and K5E N*。
2. An indoor Wi-Fi positioning method according to claim 1, wherein: in the step A, the acquisition points are arranged at the central points of the positioning areas, and the positioning APs installed on the acquisition points are numbered by taking MAC addresses as identifiers.
3. An indoor Wi-Fi positioning method according to claim 1, wherein: in step B, the algorithm for weighting the mean value of the RSSI signals is:
Figure FDA0002579315130000033
4. an indoor Wi-Fi positioning method according to claim 1, wherein: in step B, the
Figure FDA0002579315130000034
Positioning area A of AP ofkSet to an unset state, where K1 is a tunable constant, K1 ∈ R.
5. An indoor Wi-Fi positioning method according to claim 1, wherein: in step B, calculating APjIn the positioning area AkTotal frequency of
Figure FDA0002579315130000035
Will be provided with
Figure FDA0002579315130000041
Positioning area A of AP ofkSet to an unset state, where K2 is a tunable constant, K2 ∈ N*。
6. An indoor Wi-Fi positioning method according to claim 1, wherein: in step D, mixing
Figure FDA0002579315130000042
Is set to an undetermined state, where K1 'is an adjustable constant, and K1' e R.
7. An indoor Wi-Fi positioning method according to claim 1, wherein: in step DCalculating AP measured by positioning client at point X to be positionedjTotal frequency of
Figure FDA0002579315130000043
Will be provided with
Figure FDA0002579315130000044
Is set to be in an undetermined state, wherein K2 'is an adjustable constant, and K2' is belonged to N*。
8. An indoor Wi-Fi positioning method according to claim 1, wherein: in the step D, the algorithm for obtaining the coordinates of the to-be-positioned points by weighting the coordinates of the acquisition points of the K5 positioning areas comprises an average weighting algorithm and an occurrence frequency weighting algorithm, wherein the average weighting algorithm comprises the steps of giving an average weight of 1/K5 to each acquisition point of the K5 positioning areas, and then calculating the coordinates of the to-be-positioned points according to the average weights; the occurrence frequency weighting algorithm comprises the steps of giving corresponding occurrence frequency weights to all acquisition points of K5 positioning areas according to the occurrence frequency of each positioning area, and calculating the coordinates of the to-be-positioned points according to the occurrence frequency weights.
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