CN114679683A - Indoor intelligent positioning method based on derivative fingerprint migration - Google Patents

Indoor intelligent positioning method based on derivative fingerprint migration Download PDF

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CN114679683A
CN114679683A CN202210330811.2A CN202210330811A CN114679683A CN 114679683 A CN114679683 A CN 114679683A CN 202210330811 A CN202210330811 A CN 202210330811A CN 114679683 A CN114679683 A CN 114679683A
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reference point
fingerprint
point
points
value
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张晖
王志坤
赵海涛
朱洪波
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Nanjing University of Posts and Telecommunications
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Nanjing University of Posts and Telecommunications
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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
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • 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
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • 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
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

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Abstract

The invention discloses an indoor intelligent positioning method based on derivative fingerprint migration, which comprises the steps that firstly, 5G base stations collect RSS, incidence angles and position information of different indoor reference points, an edge server performs three types of fingerprint derivation by combining the RSS and the incidence angle information, and an offline derivative fingerprint library is constructed by combining the reference point position information and an optimal K value; and finally, indoor positioning is carried out through an optimal K value online selection method of the point to be measured and a KNN model migration mechanism considering environment time variation, and a better positioning result is sent to the device to be measured under the condition of low off-line data re-acquisition. The method improves the defects of common fingerprint characteristics by adopting derived fingerprints, determines the K value selection of the KNN positioning model by an online optimal selection method of the K value of the point to be measured so as to improve the positioning accuracy of the model, and effectively improves the indoor positioning accuracy of time-varying scenes by adopting a transfer learning positioning algorithm under the condition of low data acquisition cost, thereby having very wide application prospect.

Description

Indoor intelligent positioning method based on derivative fingerprint migration
Technical Field
The invention belongs to the technical field of indoor positioning.
Background
With the development of location service industries such as intelligent sorting and disaster rescue, high-precision positioning becomes a popular research direction of wireless positioning technology. According to recent survey statistics, people are located indoors for 80% -90% of the time, so that indoor positioning research is increasingly focused. Simultaneously along with the continuous expansion of enterprise's scale, hardly prevent staff's safety, so the demand carries out accurate indoor location to personnel in specific region. The above results show that the accurate positioning of the indoor environment becomes a positioning research hotspot, and the research results not only bring great economic benefits, but also provide support for technical application in other fields.
The existing indoor positioning methods include technologies such as a proximity detection method, a centroid positioning method, a multilateral positioning method, a fingerprint positioning method, a dead reckoning method and the like, but the methods have more or less defects, for example, although the proximity detection method is simple and easy to implement, only approximate positioning information can be provided, and intensive base station deployment is needed for realizing high-precision positioning by the centroid positioning method, which causes huge economic cost. Comprehensively, indoor positioning generally adopts a multilateral positioning method and a fingerprint positioning method, and both positioning methods can obtain good positioning accuracy. The principle of the multilateration method is that the distance is calculated by the received signal strength and the attenuation model of the signal, and a linear equation set is established according to the distance to solve the positioning position. Indoor positioning based on a fingerprint positioning method is a matching or mapping method of an offline fingerprint library. For example, according to RSS, a classification algorithm is used for matching reference points in an offline fingerprint library, and then the positions of the reference points are weighted to obtain a positioning position; or using regression algorithm to construct mapping relation according to RSS to obtain the positioning position. Multilateration is affected by complex indoor environments and base station deployment locations, and the positioning accuracy is reduced by using a type of attenuation model to represent the RSS-distance relationship. In addition, fingerprint location using RSS data also results in poor location accuracy due to RSS signal fluctuations.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the problems in the prior art, the invention provides an indoor intelligent positioning method based on derivative fingerprint migration.
The technical scheme is as follows: the invention provides an indoor intelligent positioning method based on derivative fingerprint migration, which specifically comprises the following steps:
step 1: in an off-line stage, mobile equipment is adopted to communicate with a 5G base station at different indoor positions, the different positions are used as reference points, RSS information received by the mobile equipment at each reference point in a time sequence, an incident angle of a signal received by the mobile equipment at each reference point and position information of each reference point are obtained; the total number of the moments in the period of time sequence is U, the total number of the 5G base stations is M, the total number of the reference points is E0, and the set of the reference points is R;
and 2, step: calculating for each reference point derived fingerprint features including a cumulative mean fingerprint feature F1(RSS_θr) Differential fingerprint feature F2(RSS_θr) And hyperbolic fingerprint feature F3(RSS_θr)：RSS_θrA joint fingerprint representing a reference point r based on the RSS and the incident angle information; calculating the optimal K value of each reference point according to the three fingerprint characteristics of each reference point; establishing an offline derivative fingerprint database, wherein the offline derivative fingerprint database comprises derivative fingerprint characteristics of each reference point, an optimal K value, RSS information, signal incidence angle and position information, and r is 1, 2.
And step 3: judging whether the time variation of the actual indoor scene exceeds the preset days, if so, turning to the step 4, and otherwise, calculating the position information of the points to be measured in the target domain according to the optimal K value of the reference points in the offline derivative fingerprint library; the target domain comprises a known point and a point to be measured, wherein the known point is RSS information in an actual indoor scene, and both the incident angle and the position information of a signal are known;
and 4, step 4: and migrating the off-line derivative fingerprint library by adopting migration learning so as to construct a radio map, and determining the position information of the point to be measured according to a new reference point in the radio map and the optimal K value of the reference point in the off-line derivative fingerprint library before migration.
Further, the step 2 is as follows: the calculation of the cumulative average fingerprint feature of the reference point r is specifically:
the calculation formula for setting the cumulative average is as follows:
Figure BDA0003573003320000021
wherein the content of the first and second substances,
Figure BDA0003573003320000022
represents the cumulative average of the data from time 1 to time U, l (U +1) represents the actual value of the data at time U +1, U is 1,2, …, U;
calculating the cumulative average fingerprint characteristic of the reference point r based on the calculation formula of the cumulative average:
Figure BDA0003573003320000023
wherein the content of the first and second substances,
Figure BDA0003573003320000031
the accumulated average value of the derivative fingerprints of the Mth 5G base station to the reference point r at the U-1 th moment is represented; RSS _ thetarThe expression of (a) is:
Figure BDA0003573003320000032
wherein M represents the mth 5G base station, and M is 1,2, …, M;
Figure BDA0003573003320000033
representing RSS data collected by the mth 5G base station at time u with respect to reference point r,
Figure BDA0003573003320000034
representing signal incidence angle data collected by the mth 5G base station to the reference point r at the moment u;
differential fingerprint of reference point rCharacteristic F2(RSS_θr) Is expressed as:
F2(RSS_θr)＝[Δψr(1),…,Δψr(u),…,Δψr(U)]
wherein, Delta psirThe expression of (u) is:
Figure BDA0003573003320000035
wherein m is1Denotes the m-th1Base station, m2Denotes the m-th2Base station, m1＝1,2，...,M,m21,2, M, and M1≠m2；
Figure BDA0003573003320000036
The expression of (c) is as follows:
Figure BDA0003573003320000037
hyperbolic fingerprint feature F of reference point r3(RSS_θr) The expression of (a) is:
F3(RSS_θr)＝[lr(1),…,lr(u),…,lr(U)]
wherein lrThe expression of (u) is:
Figure BDA0003573003320000038
wherein the content of the first and second substances,
Figure BDA0003573003320000039
the expression of (a) is:
Figure BDA00035730033200000310
further, the step 2 of calculating the optimal K value of each reference point specifically includes: calculating any two reference points r1And r2Has a European style distance of fingerprint between
Figure BDA0003573003320000041
Figure BDA0003573003320000042
Wherein r is1＝1,2,...,E0,r21,2, E0, and r1≠r2，||·||2Expressing a two-norm formula;
Figure BDA0003573003320000043
representing a weight coefficient of fingerprint features corresponding to the e value;
the reference point r is calculated from the following objective function1Optimum K value of (c):
Figure BDA0003573003320000044
Figure BDA0003573003320000045
wherein the content of the first and second substances,
Figure BDA0003573003320000046
and
Figure BDA0003573003320000047
represents a reference point r1The actual position information of the mobile station (ms),
Figure BDA0003573003320000048
and
Figure BDA0003573003320000049
respectively represent reference points r1The abscissa and the ordinate of the predicted position of (a),
Figure BDA00035730033200000410
to representReference point r1The value of (a) of (b),
Figure BDA00035730033200000411
represents a reference point r1K value of
Figure BDA00035730033200000412
At the reference point r1Selected for KNN positioning
Figure BDA00035730033200000413
A set of reference points, solving the objective function to obtain a reference point r1Optimum K value of
Figure BDA00035730033200000414
Figure BDA00035730033200000415
Representing reference points r based on optimal K values1Selected for KNN positioning
Figure BDA00035730033200000416
A set of reference points;
reference point r1Predicted position of
Figure BDA00035730033200000417
The expression of (a) is:
Figure BDA00035730033200000418
wherein the content of the first and second substances,
Figure BDA00035730033200000419
representation collection
Figure BDA00035730033200000420
The 1 st reference point in the list of (c),
Figure BDA00035730033200000421
representation collection
Figure BDA00035730033200000422
To middle
Figure BDA00035730033200000423
The number of the reference points is,
Figure BDA00035730033200000424
represents a reference point r1And collections
Figure BDA00035730033200000425
Reference point r'1The euclidean distance of the fingerprint of (c),
Figure BDA00035730033200000426
representation collection
Figure BDA00035730033200000427
Reference point r'1Actual location information of.
Further, the step 3 of calculating the position information of the point to be measured in the target domain specifically includes:
using a nearest neighbor algorithm for reference point matching:
Figure BDA0003573003320000051
wherein EDr,*Representing the Euclidean distance of the fingerprint of the point to be measured from the reference point r, Fe(RSS_θ*) Representing fingerprint characteristics of points to be measured corresponding to the values of e;
selecting the optimal K value corresponding to the reference point with the closest Euclidean distance as the optimal K value of the point to be measured
Figure BDA0003573003320000052
Sorting the Euclidean distances of the fingerprints of each reference point and the point to be measured from small to large, and taking the first
Figure BDA0003573003320000053
Reference point composition set corresponding to European style distance of individual fingerprintCombination of Chinese herbs
Figure BDA0003573003320000054
Thereby calculating the position (x) of the point to be measured*,y*)：
Figure BDA0003573003320000055
Wherein the content of the first and second substances,
Figure BDA0003573003320000056
representation collection
Figure BDA0003573003320000057
Is the first of the reference points in (a),
Figure BDA0003573003320000058
representation collection
Figure BDA0003573003320000059
To middle
Figure BDA00035730033200000510
The number of the reference points is,
Figure BDA00035730033200000511
representing points to be measured and the set
Figure BDA00035730033200000512
Reference point r*Of the Euclidean fingerprint distance therebetween, said
Figure BDA00035730033200000513
Representation collection
Figure BDA00035730033200000514
Reference point r*Actual location information of.
Further, the step 4 specifically includes: taking the offline derivative fingerprint library as a source field, and establishing a transfer learning objective function:
Figure BDA00035730033200000515
and (3) carrying out Lagrange multiplier method solving on the target function of the transfer learning to obtain:
Figure BDA00035730033200000516
wherein, χ1Hexix-2Are all parameters, λ is Lagrangian multiplier, V is
Figure BDA00035730033200000517
Characteristic vector of
Figure BDA00035730033200000518
Figure BDA00035730033200000519
Representing the source domain DsAnd a target domain DtThe inter-domain inter-class divergence matrix of (c),
Figure BDA0003573003320000061
representing the source domain DsAnd a target domain DtThe inter-domain intra-class divergence matrix of (c),
Figure BDA0003573003320000062
source domain DsThe inter-class divergence matrix within the domain,
Figure BDA0003573003320000063
source domain DsAn intra-domain intra-class divergence matrix;
Figure BDA0003573003320000064
and
Figure BDA0003573003320000065
the expression of (a) is:
Figure BDA0003573003320000066
Figure BDA0003573003320000067
Figure BDA0003573003320000068
Figure BDA0003573003320000069
wherein, wo,rRepresenting the difference between the known point o and the reference point r, o being a known point, 1,2, …, E1, E1
The total number of the chips is,
Figure BDA00035730033200000625
σeis an adjustable parameter;
Figure BDA00035730033200000610
represents a reference point r1And a reference point r2Inter-difference, lo (r) and lo (o) represent position information of the reference point r and the known point o, respectively;
Figure BDA00035730033200000611
representing the mean value of the fingerprint feature vectors of the known point o after the mapping of the Gaussian kernel function,
Figure BDA00035730033200000612
representing the mean value of the fingerprint characteristic vectors of the reference point r after the Gaussian kernel function mapping,
Figure BDA00035730033200000613
and
Figure BDA00035730033200000614
the expression of (a) is as follows:
Figure BDA00035730033200000615
Figure BDA00035730033200000616
wherein the content of the first and second substances,
Figure BDA00035730033200000617
and
Figure BDA00035730033200000618
representing the fingerprint feature vectors of the known point o and the reference point r respectively,
Figure BDA00035730033200000619
a kernel matrix which is obtained by mapping the fingerprint characteristic vectors of all known points in the target domain through a Gaussian kernel function,
Figure BDA00035730033200000620
a kernel matrix which is obtained by mapping the fingerprint characteristic vectors of all the reference points in the source field through a Gaussian kernel function,
Figure BDA00035730033200000621
and
Figure BDA00035730033200000622
the expression of (a) is as follows:
Figure BDA00035730033200000623
Figure BDA00035730033200000624
will be provided with
Figure BDA0003573003320000071
A plurality of generalized characteristic values are arranged from large to small and selectedBefore selection
Figure BDA0003573003320000072
A generalized characteristic value, establishing a dimension of
Figure BDA0003573003320000073
The new mapping space of (a) is,
establishing a source sub-domain according to the following rules:
Figure BDA0003573003320000074
wherein the content of the first and second substances,
Figure BDA0003573003320000075
the representation is based on a reference point r2Optimum K value of
Figure BDA0003573003320000076
To the reference point r2Selected for KNN positioning
Figure BDA0003573003320000077
A set of reference points; taking a known point with the same position as a reference point in the same source subdomain as the same target subdomain; dividing each source into domains
Figure BDA0003573003320000078
Each reference point r insaMapping to a dimension of
Figure BDA0003573003320000079
In the new mapping space of (2), obtaining a mapped representation
Figure BDA00035730033200000710
Figure BDA00035730033200000711
Wherein the content of the first and second substances,
Figure BDA00035730033200000712
representing source domains
Figure BDA00035730033200000713
And target subdomain
Figure BDA00035730033200000714
A mapping matrix mapped into the new mapping space,
Figure BDA00035730033200000715
representing source subdomains
Figure BDA00035730033200000716
Reference point rsaThe fingerprint feature vector of (2);
Figure BDA00035730033200000717
representing source subdomains
Figure BDA00035730033200000718
Projecting to a kernel matrix in the new mapping space;
dividing each target into domains
Figure BDA00035730033200000719
Each reference point o intaMapping to a dimension of
Figure BDA00035730033200000720
In the new mapping space of (2), obtaining a mapped representation
Figure BDA00035730033200000721
Figure BDA00035730033200000722
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00035730033200000723
representing target domains
Figure BDA00035730033200000724
Reference point otaThe fingerprint feature vector of (2);
Figure BDA00035730033200000729
representing target domains
Figure BDA00035730033200000725
Projecting to a kernel matrix in the new mapping space;
forming a new offline derivative fingerprint library by each known point in the new mapping space and the residual reference points in the new mapping space; the residual reference points are reference points in the source field except for the reference points corresponding to the known points;
constructing a radio map according to the fingerprint feature vectors of the new off-line derivative fingerprint library in the new mapping space, wherein the set of new reference points in the radio map is
Figure BDA00035730033200000726
Figure BDA00035730033200000727
Represents the E0 th new reference point in the wireless map, E0 ═ 1, 2.., E0; and a new reference point
Figure BDA00035730033200000728
The optimal K value is the same as the e0 th reference point in the source field; calculating the point to be measured and the new reference point
Figure BDA0003573003320000081
Fingerprint euclidean distance between:
Figure BDA0003573003320000082
wherein the content of the first and second substances,
Figure BDA0003573003320000083
representing the fingerprint characteristics corresponding to the points to be measured and the e values in the new mapping space，
Figure BDA0003573003320000084
Indicating a new reference point
Figure BDA0003573003320000085
The representation of the fingerprint features corresponding to the e values in the new mapping space;
selecting the optimal K value corresponding to the new reference point closest to the Euclidean distance of the fingerprint of the point to be measured as the optimal K value of the point to be measured
Figure BDA0003573003320000086
Arranging the Euclidean distances of fingerprints between all new reference points and points to be measured from small to large, before selecting
Figure BDA0003573003320000087
New reference point composition set corresponding to individual fingerprint Euclidean distance
Figure BDA0003573003320000088
Thus obtaining the position of the point to be measured:
Figure BDA0003573003320000089
wherein
Figure BDA00035730033200000810
Representation collection
Figure BDA00035730033200000811
The first of the new reference points in the first,
Figure BDA00035730033200000812
representation collection
Figure BDA00035730033200000813
First, the
Figure BDA00035730033200000814
Figure BDA00035730033200000815
Figure BDA00035730033200000816
Representation collection
Figure BDA00035730033200000817
Zhongxin reference point r'*The actual position information of the mobile station (ms),
Figure BDA00035730033200000818
representing points to be measured and a new reference point r'*The euclidean distance of the fingerprints therebetween.
Has the advantages that: the method can improve the defects of common fingerprint characteristics by adopting derived fingerprints, and in addition, the K value selection of the KNN positioning model is determined by an online optimal selection method of the K value of the point to be measured so as to improve the positioning accuracy of the model.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of an online optimal selection positioning method for K values of points to be measured;
fig. 3 is a flow chart of a sectored radio map construction.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention.
The invention provides a 5G indoor intelligent positioning method based on derivative fingerprint migration, as shown in figure 1, the 5G indoor intelligent positioning method based on derivative fingerprint migration provided by the invention comprises the steps that firstly, a 5G base station collects RSS information, signal incidence angles and position information received by indoor mobile equipment at different reference points, and uploads the collected data to an edge server; secondly, the edge server performs three types of fingerprint derivation by combining RSS and incidence angle information, and establishes a derived fingerprint-optimal K value-position fingerprint database (offline derived fingerprint database) by combining reference point position information and optimal K value; and finally, indoor positioning is carried out through an online optimal selection method of the K value of the point to be measured and a KNN model migration mechanism considering environmental time variation, and a better positioning result is sent to the equipment to be measured under the condition of low off-line data re-acquisition.
The invention mainly comprises three contents: firstly, generating derivative fingerprints, and respectively generating three types of fingerprint characteristics according to RSS and incidence angle information; secondly, an online optimal selection method of K values of the points to be measured is provided to improve the KNN positioning model; and thirdly, the edge server performs indoor positioning according to a KNN model migration mechanism considering environmental time variation, and predicts the position of the equipment to be tested under the condition of less off-line data re-acquisition.
1. And (3) generating a derivative fingerprint:
the derived fingerprint in this embodiment is a fingerprint feature derived by combining fingerprints based on RSS and incident angle information, and includes: on the basis of the accumulated average fingerprint characteristics, data acquisition is considered to be carried out in a short time, and the data have larger correlation, so that the currently acquired data are continuously corrected by utilizing historical data. The fingerprint joint characteristic based on difference can effectively solve the problem of device heterogeneous caused by hardware or software problems. The joint fingerprint characteristic based on the hyperbola is used for solving the problem that the difference between the absolute values of the signals acquired based on different users is large, and the data based on the proportion is used for being more reliable.
In the off-line stage, the set of reference points collected in the positioning scene is R, the total number is E0, U time samples are collected for each reference point, and the number of 5G base stations participating in positioning is M. For the reference point r, the corresponding position information is (x)r,yr). Based on the RSS and the angle of incidence information, the joint fingerprint of the reference point r is represented as:
Figure BDA0003573003320000091
wherein the content of the first and second substances,
Figure BDA0003573003320000092
representing RSS data collected by 5G base station m at time index U (at time U, U is 1,2, …, U) to reference point r,
Figure BDA0003573003320000101
representing angle data acquired by the 5G base station m for the reference point r at the time index u; m is 1,2, …, M.
Based on the accumulated and averaged fingerprint joint characteristics, the characteristics can observe the collected historical data and modify the currently collected data by combining the occurrence probability of the collected historical data. The specific formula for the cumulative average is as follows:
Figure BDA0003573003320000102
wherein the content of the first and second substances,
Figure BDA0003573003320000103
represents the cumulative average of the data at times 1 to u, and l (u +1) represents the actual value of the data collected at time u + 1. Accordingly, the cumulative average fingerprint feature for reference point r is represented as:
Figure BDA0003573003320000104
wherein, F1(·) denotes the cumulative average fingerprint feature, wherein,
Figure BDA0003573003320000105
representing the accumulated average value of the joint fingerprint of the Mth 5G base station to the reference point r at the U-1 th moment;
Figure BDA0003573003320000106
representing the cumulative average of the 5G base station M over a certain time sequence for the reference point r combined fingerprint.
Difference-based fingerprint joint feature, which is to perform joint fingerprints received from different base stations under the same time sequence numberDifferenced from each other to construct all fingerprints under that time sequence number. For reference point r, 5G base station m1And 5G base station m2The differential fingerprint of the received data at time u is:
Figure BDA0003573003320000107
wherein m is1＝1,2，...,M,m21,2, M, and M1≠m2。
The joint feature of the reference point r based on the difference fingerprint is expressed as:
F2(RSS_θr)＝[Δψr(1),…,Δψr(u),…,Δψr(U)]
wherein, F2(. represents a differential fingerprint,. DELTA.. psir(u) is expressed as:
Figure BDA0003573003320000111
based on the combined characteristics of the fingerprints of the hyperbola, the fingerprint has better stability than the data of the direct use signal.
For reference point r, 5G base station m1And 5G base station m2The hyperbolic fingerprint of the received data at time sequence u is:
Figure BDA0003573003320000112
so the hyperbolic-based joint fingerprint feature of the reference point r is represented as:
F3(RSS_θr)＝[lr(1),…,lr(u),…,lr(U)]
wherein, F3Denotes the change of the fingerprint into a hyperbolic fingerprint,/r(u) is expressed as:
Figure BDA0003573003320000113
2. the online optimal selection method of the K value of the point to be measured comprises the following steps:
in the KNN indoor positioning method, the positioning accuracy may be degraded due to noise. In addition, due to different position environments of the points to be measured, selecting a fixed K value cannot ensure that the points to be measured at each position obtain better positioning accuracy. In order to find the optimal self-adaptive K value of each initial positioning condition, the invention provides an online optimal selection method of the K value of the point to be measured, as shown in FIG. 2.
In order to determine the optimal K value for each off-line reference point, a KNN algorithm is performed for all reference points in the off-line phase. Calculating any two reference points r1And r2Has a European style distance of fingerprint between
Figure BDA0003573003320000114
Figure BDA0003573003320000115
Wherein r is1＝1,2,...,E0,r21,2, E0, and r1≠r2，||·||2The formula of the two-norm is expressed,
Figure BDA0003573003320000116
and representing the fingerprint characteristic weight coefficient corresponding to the value of e. Smaller Euclidean distance of the fingerprint indicates larger similarity of the fingerprint. Note that while KNN locating reference points, no match itself in the off-line fingerprint library. The specific matching process comprises the following steps: calculating a reference point r1And a reference point r2The Euclidean distance of the fingerprints between the first and second images is sorted from small to large, and the front with the smallest Euclidean distance of the fingerprints is selected
Figure BDA0003573003320000117
A reference point forming a set
Figure BDA0003573003320000121
Wherein the content of the first and second substances,
Figure BDA0003573003320000122
representation collection
Figure BDA0003573003320000123
Reference points with the middle sequence number of lr1, and the position information of these reference points is used for weighted cumulative summation.
Reference point r1The predicted position of (a) is expressed as:
Figure BDA0003573003320000124
wherein the content of the first and second substances,
Figure BDA0003573003320000125
representation collection
Figure BDA0003573003320000126
Reference point r'1The actual position information of the mobile station (ms),
Figure BDA0003573003320000127
representing a reference point r1And collections
Figure BDA0003573003320000128
Reference point r'1The euclidean distance of the fingerprint.
To ensure the minimum positioning error of each reference point, the optimal K value of each reference point needs to be reasonably considered to be the reference point r1For example, the following optimization objectives may be formulated:
Figure BDA0003573003320000129
Figure BDA00035730033200001210
wherein the content of the first and second substances,
Figure BDA00035730033200001211
the upper limit of (2) is because
Figure BDA00035730033200001212
Larger will result in more computation consumption. Obtaining a reference point r by optimizing the target1Optimum K value of
Figure BDA00035730033200001213
Figure BDA00035730033200001214
Representing reference points r based on optimal K values1Selected for KNN positioning
Figure BDA00035730033200001215
A set of reference points.
In the online stage, if the time variation of the actual indoor scene does not exceed the preset days, calculating the optimal K value and position of the point to be measured in the target domain by adopting the following method; the target domain comprises a known point and a point to be measured, wherein the known point is RSS information in an actual indoor scene, and both the incident angle and the position information of a signal are known, and the point to be measured is RSS information in the actual indoor scene, and the incident angle of the signal is known but the position information is unknown.
Calculating the Euclidean distance of the fingerprint between the point to be measured and the reference point r;
Figure BDA00035730033200001216
and then selecting a corresponding K value according to the matched reference point, wherein the specific mode is as follows: calculating Euclidean distance of the point to be measured and all the reference points, and selecting the optimal K value of the reference point with the minimum Euclidean distance as the K value of the point to be measured (namely
Figure BDA0003573003320000131
) Then, continuously selecting the front with minimum European distance of fingerprint
Figure BDA0003573003320000132
A reference point, the number of the finally formed elements is
Figure BDA0003573003320000133
Set of reference points
Figure BDA0003573003320000134
So as to realize the online selection method of the optimal K value of the point to be measured. The final positioning result of the point to be measured is as follows:
Figure BDA0003573003320000135
wherein the content of the first and second substances,
Figure BDA0003573003320000136
representation collection
Figure BDA0003573003320000137
Reference point r*The actual position information of the mobile station (bs),
Figure BDA0003573003320000138
represents a reference point r*The euclidean distance of the fingerprint from the test point,
Figure BDA0003573003320000139
representation collection
Figure BDA00035730033200001310
Is the first of the reference points in (a),
Figure BDA00035730033200001311
representation collection
Figure BDA00035730033200001312
To middle
Figure BDA00035730033200001313
A reference point.
3. KNN model migration mechanism considering environmental time variation:
according to the KNN positioning principle, the position of any point is obtained by selecting the positions of K adjacent points according to the fingerprint similarity and calculating. But the environmental time variation causes the K value required by the KNN positioning model and the off-line derivative fingerprint library to change. To ensure the availability of the optimal K value under ambient time variation (actual indoor ambient time variation exceeds a preset number of days). Thus, as shown in fig. 3, the invention employs known point acquisition based on offline optimal K-values for subsequent construction of a radio map.
According to the optimal K value solved by the offline reference points in the source field (taking the offline derivative fingerprint library as the source field), the feature vectors of the three types of fingerprints of a certain reference point can be confirmed to have larger similarity with the feature vectors of other K-1 reference points; the one-way connection relation matrix between different position reference points is as follows:
Figure BDA00035730033200001314
wherein the content of the first and second substances,
Figure BDA00035730033200001315
represents a reference point r1To a reference point r2The unidirectional connection relation of (1) is generally 0 and 1, 0 represents unidirectional connection, 1 represents unidirectional disconnection,
Figure BDA00035730033200001316
wherein the content of the first and second substances,
Figure BDA00035730033200001317
representation collection
Figure BDA00035730033200001318
The number of elements (c);
Figure BDA00035730033200001319
Figure BDA00035730033200001320
representation is based on the bestOptimal K value versus reference point r1The reference point set matched by KNN is carried out; according to the unidirectional connection relation matrix between the reference points, a reference point set can be constructed according to the following rules:
Figure BDA0003573003320000141
wherein the content of the first and second substances,
Figure BDA0003573003320000142
the representation is based on a reference point r2Optimum K value of
Figure BDA0003573003320000143
To the reference point r1Selected for KNN positioning
Figure BDA0003573003320000144
A set of reference points.
According to the formula, the reference points in the source field can be partitioned, one partition is used as a source sub-field, then the known points of the target field with the same position needing to be collected are selected by combining the position information corresponding to the reference point of each partition, and finally the known points of the target field are subjected to partition migration learning to realize the extension of the radio map of the target field. And partitioning and migrating the source field by using the constructed reference point set.
For a time-varying indoor environment, the distribution of information features obtained at the same position in an offline stage (source domain) and an online stage (target domain) may be inconsistent, thereby causing the accuracy of fingerprint positioning to be reduced. If the fingerprint database is collected again for the target domain, the positioning cost is increased sharply and the subsequent positioning cannot be assisted. However, for a time-varying environment, the location of the positioning base station is substantially fixed, so that a migration learning method can be adopted to migrate the source domain data onto the target domain.
In the off-line phase, the off-line derivative fingerprint library is used as the source domain. Source domain DsThe fingerprint feature vector corresponding to the reference point r is expressed as:
Figure BDA0003573003320000145
source domain DsThe representation contains the set of all reference points, namely:
Figure BDA0003573003320000146
wherein E0 denotes the source domain DsTotal number of reference points.
In an online stage, a known position label and a known point of a known fingerprint characteristic vector and a point to be measured of an unknown position label and a known fingerprint characteristic vector are collected in indoor space to serve as a target domain DtThe fingerprint feature vector constructed by the known point o is represented as:
Figure BDA0003573003320000147
wherein, o is 1,2, …, E1, E1 is the total number of known points.
Target domain DtThe fingerprint feature vector constructed by the middle point to be measured o' is expressed as:
Figure BDA0003573003320000151
wherein, o' 1,2, E2, E2 are the total number of the points to be measured.
In the partition migration learning, a reference point and a known point of the same region are selected for the migration learning. The fingerprint feature vectors are first mapped to other dimensional spaces using a mapping function. Suppose that
Figure BDA0003573003320000152
Representing functions mapped to other dimensions, taking into account the mapping function
Figure BDA0003573003320000153
The specific form of (c) is not well determined, so the kernel function Ke (-) is adopted as a substitute for other dimension spaces and mapping functions, namely:
Figure BDA0003573003320000154
Figure BDA0003573003320000155
Figure BDA0003573003320000156
wherein o is1And o2Represents a known point o1And a known point o2，o1＝1,2,…,E1,o21,2, …, E1, and o1≠o2；o'1And o'2Represents a point to be measured o'1And to-be-measured point o'2，o'1＝1,2,…,E2,o'21,2, …, E2 and o'1≠o'2，σkeRepresents the tunable parameters of a gaussian kernel function.
Accordingly, the source domain DsMean of fingerprint feature vectors with reference point r
Figure BDA0003573003320000157
Expressed as:
Figure BDA0003573003320000158
wherein the content of the first and second substances,
Figure BDA0003573003320000159
representing the representation of the source domain after kernel function mapping,
Figure BDA00035730033200001510
passing of fingerprint feature vectors representing source domain reference points through a Gaussian kernel functionThe number of the mapped kernel matrix is counted,
Figure BDA00035730033200001511
Figure BDA00035730033200001512
representation matrix
Figure BDA00035730033200001513
Middle r1Line r2The elements of the column.
The mean of the fingerprint feature vectors for the known point o in the target domain is represented as:
Figure BDA00035730033200001514
wherein the content of the first and second substances,
Figure BDA00035730033200001515
the representation target domain is mapped to other dimensional spaces,
Figure BDA00035730033200001516
a kernel matrix obtained by mapping the fingerprint feature vector of the known point of the target domain through a Gaussian kernel function,
Figure BDA0003573003320000161
Figure BDA0003573003320000162
representation matrix
Figure BDA0003573003320000163
O in1Line o2The elements of the column.
Source domain DsAnd a target domain DtThe inter-domain inter-class divergence matrix of (a) is represented as:
Figure BDA0003573003320000164
wherein, lo (r) andlo (o) indicates position information of the reference point r and the known point o,
Figure BDA0003573003320000165
representing the difference, σ, between the position of the reference point r and the position of the known point oeIndicating an adjustable parameter.
Source domain DsAnd a target domain DtThe inter-domain intra-class divergence matrix of (a) is represented as:
Figure BDA0003573003320000166
source domain DsThe intra-domain inter-class divergence matrix is represented as:
Figure BDA0003573003320000167
wherein, lo (r)1) And lo (r)2) Represents a reference point r1And a reference point r2The category of the location information of (a),
Figure BDA0003573003320000168
represents a reference point r1And a reference point r2The difference between them.
Source domain DsThe intra-domain intra-class divergence matrix is represented as:
Figure BDA0003573003320000169
the constructed objective function of the transfer learning is as follows:
Figure BDA00035730033200001610
wherein, χ1Hexix-2The method belongs to two adjustable parameters and is used for controlling the importance degree of the inter-domain characteristics and the intra-domain characteristics when calculating the mapping matrix; the objective function is related to
Figure BDA00035730033200001611
And
Figure BDA0003573003320000171
the generalized Rayleigh entropy is solved by a Lagrange multiplier method of the target function of the transfer learning, and finally the generalized Rayleigh entropy can be obtained:
Figure BDA0003573003320000172
where λ is the lagrange multiplier, defined for convenience:
Figure BDA0003573003320000173
so that V is
Figure BDA0003573003320000174
The feature vector of (2).
If newly built a dimension is
Figure BDA0003573003320000175
And (4) new mapping space of dimension, the expression form of the mapping matrix is as follows:
Figure BDA0003573003320000176
wherein the content of the first and second substances,
Figure BDA0003573003320000177
to represent
Figure BDA0003573003320000178
To (1) a
Figure BDA0003573003320000179
And the feature vector matrix corresponding to the large generalized eigenvalue.
Partitioning through a reference point set of a source field, and comparing with the reference point setTaking a known point with the same reference point position in the combined partition as one partition in the target domain, and taking the partition as the target partition; the target domain radio map reconstruction based on the partitions is realized through the migration learning mapping space calculated by each partition set (source partition and target partition); according to a mapping matrix
Figure BDA00035730033200001710
The source can be divided into domains
Figure BDA00035730033200001711
Reference point rsaMapping to a new mapping space, which is represented as:
Figure BDA00035730033200001712
wherein the content of the first and second substances,
Figure BDA00035730033200001713
representing source subdomains
Figure BDA00035730033200001714
Is projected to the representation of the new mapping space,
Figure BDA00035730033200001715
representing source subdomains
Figure BDA00035730033200001716
And target subdomain
Figure BDA00035730033200001717
The mapping matrix of (a) is obtained,
Figure BDA00035730033200001718
and
Figure BDA00035730033200001719
representing source subdomains
Figure BDA00035730033200001720
Scoping with a target
Figure BDA00035730033200001721
And (5) projection kernel matrix.
Likewise, according to the mapping matrix
Figure BDA00035730033200001722
Object sub-domains at the same position in the object domain
Figure BDA00035730033200001723
Known point otaMapping to a new space, which is represented as:
Figure BDA00035730033200001724
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00035730033200001725
representing target domains
Figure BDA00035730033200001726
Known point of (a)taA representation projected into the new mapping space, wherein,
Figure BDA00035730033200001727
representing target subdomains
Figure BDA00035730033200001728
Reference point otaThe fingerprint feature vector of (2);
Figure BDA00035730033200001729
representing target domains
Figure BDA00035730033200001730
The kernel matrix in the new mapping space is projected.
Forming a new offline derivative fingerprint database by each known point in the new mapping space and the residual reference points in the new mapping space; the remaining reference points are reference points in the source domain other than the reference points corresponding to the known points.
In the new off-line derivative fingerprint database, the position is
Figure BDA0003573003320000181
The fingerprint feature vector of the point in the new mapping space is:
Figure BDA0003573003320000182
constructing a radio map according to the fingerprint feature vectors of the new offline derivative fingerprint library in the new mapping space, wherein the set of the new reference points in the constructed radio map is
Figure BDA0003573003320000183
Wherein the content of the first and second substances,
Figure BDA0003573003320000184
denotes a new reference point with sequence number e0, and
Figure BDA0003573003320000185
and (3) carrying out reference point matching by using a nearest neighbor algorithm, wherein the optimal K value is the same as the e0 th reference point in the source field:
Figure BDA0003573003320000186
wherein the content of the first and second substances,
Figure BDA0003573003320000187
representing new reference points in a constructed radiomap
Figure BDA0003573003320000188
The Euclidean distance of the fingerprint between the measuring point and the measuring point through other dimension transformation,
Figure BDA0003573003320000189
fingerprint characteristics corresponding to points to be measured and values of e are mapped in a new spaceIs shown in a schematic view of a display device,
Figure BDA00035730033200001810
indicating a new reference point
Figure BDA00035730033200001811
Representation of fingerprint features corresponding to the e-value in the new mapping space; calculating other dimensional transformation of all new reference points and points to be measured
Figure BDA00035730033200001812
The Euclidean distances of the fingerprints are sorted from small to large, and the optimal K value of the new reference point with the minimum distance is selected as the optimal K value of the point to be measured
Figure BDA00035730033200001813
Then, the front with minimum Euclidean distance of fingerprints transformed in other dimensions is continuously selected
Figure BDA00035730033200001814
A new reference point, the number of elements formed is
Figure BDA00035730033200001815
Set of (2)
Figure BDA00035730033200001816
By adopting the method for realizing the online selection of the optimal K value of the point to be measured, the final positioning result of the point to be measured is as follows:
Figure BDA00035730033200001817
wherein, therein
Figure BDA00035730033200001818
Representation collection
Figure BDA00035730033200001819
The first of the new reference points in the first,
Figure BDA00035730033200001820
representation collection
Figure BDA00035730033200001821
First, the
Figure BDA00035730033200001822
The new reference point is then used to determine the new reference point,
Figure BDA00035730033200001823
representation collection
Figure BDA00035730033200001824
Zhongxin reference point r'*The actual position information of the mobile station (bs),
Figure BDA00035730033200001825
representing points to be measured and a new reference point r'*The euclidean distance of the fingerprints therebetween.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.

Claims (5)

1. An indoor intelligent positioning method based on derivative fingerprint migration is characterized in that: the method specifically comprises the following steps:
step 1: in an off-line stage, mobile equipment is adopted to communicate with a 5G base station at different indoor positions, the different positions are taken as reference points, RSS information received by the mobile equipment at each reference point in a time sequence, an incident angle of a signal received by the mobile equipment at each reference point and position information of each reference point are obtained; the total number of the moments in the period of time sequence is U, the total number of the 5G base stations is M, the total number of the reference points is E0, and the set of the reference points is R;
step 2: calculating for each reference point a derived fingerprint feature comprising a cumulative mean fingerprint feature F1(RSS_θr) Differential fingerprintCharacteristic F2(RSS_θr) And hyperbolic fingerprint feature F3(RSS_θr)：RSS_θrA joint fingerprint representing a reference point r based on the RSS and the incident angle information; calculating the optimal K value of each reference point according to the three fingerprint characteristics of each reference point; establishing an offline derivative fingerprint database, wherein the offline derivative fingerprint database comprises derivative fingerprint characteristics of each reference point, an optimal K value, RSS information, signal incidence angle and position information, and r is 1, 2.
And step 3: judging whether the time variation of the actual indoor scene exceeds the preset days, if so, turning to the step 4, and otherwise, calculating the position information of the points to be measured in the target domain according to the optimal K value of the reference points in the offline derivative fingerprint library; the target domain comprises a known point and a point to be measured, wherein the known point is RSS information in an actual indoor scene, and both the incident angle and the position information of a signal are known;
and 4, step 4: and migrating the off-line derivative fingerprint library by adopting migration learning so as to construct a radio map, and determining the position information of the point to be measured according to a new reference point in the radio map and the optimal K value of the reference point in the off-line derivative fingerprint library before migration.
2. The indoor intelligent positioning method based on derivative fingerprint migration according to claim 1, wherein the step 2 is: the calculation of the cumulative average fingerprint feature of the reference point r is specifically:
the calculation formula for setting the cumulative average is as follows:
Figure FDA0003573003310000011
wherein the content of the first and second substances,
Figure FDA0003573003310000012
represents the cumulative average of the data from time 1 to time u, l (u +1) represents the actual value of the data at time u +1, u＝1,2,…,U；
Calculating the cumulative average fingerprint characteristic of the reference point r based on a cumulative average calculation formula:
Figure FDA0003573003310000021
wherein the content of the first and second substances,
Figure FDA0003573003310000022
the accumulated average value of the derivative fingerprints of the Mth 5G base station to the reference point r at the U-1 th moment is represented; RSS _ thetarThe expression of (a) is:
Figure FDA0003573003310000023
wherein M represents the mth 5G base station, and M is 1,2, …, M;
Figure FDA0003573003310000024
representing RSS data collected by the mth 5G base station at time u with respect to reference point r,
Figure FDA0003573003310000025
representing signal incidence angle data collected by the mth 5G base station to the reference point r at the moment u;
differential fingerprint feature F of reference point r2(RSS_θr) Is expressed as:
F2(RSS_θr)＝[Δψr(1),…,Δψr(u),…,Δψr(U)]
wherein, Delta psirThe expression of (u) is:
Figure FDA0003573003310000026
wherein m is1Represents the m-th1Base station, m2Denotes the m-th2Base station, m1＝1,2，...,M,m21,2, M, and M1≠m2；
Figure FDA0003573003310000027
The expression of (a) is as follows:
Figure FDA0003573003310000028
hyperbolic fingerprint feature F of reference point r3(RSS_θr) The expression of (a) is:
F3(RSS_θr)＝[lr(1),…,lr(u),…,lr(U)]
wherein lrThe expression of (u) is:
Figure FDA0003573003310000031
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003573003310000032
the expression of (a) is:
Figure FDA0003573003310000033
3. the method according to claim 2, wherein the step 2 of calculating the optimal K value of each reference point specifically comprises: calculating any two reference points r1And r2Has a European style distance of fingerprint between
Figure FDA0003573003310000034
Figure FDA0003573003310000035
Wherein r is1＝1,2,...,E0,r21,2, E0, and r1≠r2，||·||2Expressing a two-norm formula;
Figure FDA0003573003310000036
representing a weight coefficient of fingerprint features corresponding to the e value;
calculating the reference point r according to the following objective function1Optimum K value of (c):
Figure FDA0003573003310000037
Figure FDA0003573003310000038
wherein the content of the first and second substances,
Figure FDA0003573003310000039
and
Figure FDA00035730033100000310
represents a reference point r1The actual position information of the mobile station (ms),
Figure FDA00035730033100000311
and
Figure FDA00035730033100000312
respectively represent reference points r1The abscissa and the ordinate of the predicted position of (c),
Figure FDA00035730033100000313
representing a reference point r1The value of (a) of (b),
Figure FDA00035730033100000314
represents a reference point r1K value of
Figure FDA00035730033100000315
At the reference point r1Selected for KNN positioning
Figure FDA00035730033100000316
A set of reference points, solving the objective function to obtain a reference point r1Optimum K value of
Figure FDA00035730033100000317
Representing reference points r based on optimal K values1Selected for KNN positioning
Figure FDA00035730033100000318
A set of reference points;
reference point r1Predicted position of (2)
Figure FDA00035730033100000319
The expression of (a) is:
Figure FDA00035730033100000320
wherein the content of the first and second substances,
Figure FDA0003573003310000041
representation collection
Figure FDA0003573003310000042
The 1 st reference point in the list of (c),
Figure FDA0003573003310000043
representation collection
Figure FDA0003573003310000044
To middle
Figure FDA0003573003310000045
The number of the reference points is,
Figure FDA0003573003310000046
represents a reference point r1And collections
Figure FDA0003573003310000047
Reference point r'1The euclidean distance of the fingerprint of (c),
Figure FDA0003573003310000048
representation collection
Figure FDA0003573003310000049
Reference point r'1Actual location information of.
4. The method according to claim 3, wherein the step 3 of calculating the position information of the points to be measured in the target domain specifically comprises:
using a nearest neighbor algorithm for reference point matching:
Figure FDA00035730033100000410
wherein EDr,*Representing the Euclidean distance of the fingerprint of the point to be measured from the reference point r, Fe(RSS_θ*) Representing fingerprint characteristics of points to be measured corresponding to the values of e;
selecting the optimal K value corresponding to the reference point with the closest Euclidean distance as the optimal K value of the point to be measured
Figure FDA00035730033100000411
Sorting the Euclidean distances of the fingerprints of each reference point and the point to be measured from small to large, and taking the first
Figure FDA00035730033100000412
Corresponding to European distance of individual fingerprintReference points constitute a set
Figure FDA00035730033100000413
Thereby calculating the position (x) of the point to be measured*,y*)：
Figure FDA00035730033100000414
Wherein the content of the first and second substances,
Figure FDA00035730033100000415
representation collection
Figure FDA00035730033100000416
Is the first of the reference points in (a),
Figure FDA00035730033100000417
representation collection
Figure FDA00035730033100000418
To middle
Figure FDA00035730033100000419
A reference point, EDr*,*Representing points to be measured and the set
Figure FDA00035730033100000420
Reference point r*Of the Euclidean fingerprint distance therebetween, said (x)r*,yr*) Representation collection
Figure FDA00035730033100000421
Reference point r*Actual location information of.
5. The method according to claim 3, wherein the step 4 specifically comprises: taking the offline derivative fingerprint library as a source field, and establishing a transfer learning objective function:
Figure FDA00035730033100000422
and (3) carrying out Lagrange multiplier method solving on the target function of the transfer learning to obtain:
Figure FDA0003573003310000051
wherein, χ1Hexix-2Are all parameters, λ is Lagrangian multiplier, V is
Figure FDA0003573003310000052
Characteristic vector of (2) to
Figure FDA0003573003310000053
Figure FDA0003573003310000054
Representing the source domain DsAnd a target domain DtThe inter-domain inter-class divergence matrix of (c),
Figure FDA0003573003310000055
representing the source domain DsAnd a target domain DtThe inter-domain intra-class divergence matrix of (c),
Figure FDA0003573003310000056
source domain DsThe inter-class divergence matrix within the domain,
Figure FDA0003573003310000057
source domain DsAn intra-domain intra-class divergence matrix;
Figure FDA0003573003310000058
and
Figure FDA0003573003310000059
the expression of (a) is:
Figure FDA00035730033100000510
Figure FDA00035730033100000511
Figure FDA00035730033100000512
Figure FDA00035730033100000513
wherein, wo,rThe difference between the known point o and the reference point r is shown, wherein o is 1,2, …, E1, E1 is the total number of the known points,
Figure FDA00035730033100000514
σeis an adjustable parameter;
Figure FDA00035730033100000515
represents a reference point r1And a reference point r2The differences between lo (r) and lo (o) represent the position information of the reference point r and the known point o, respectively;
Figure FDA00035730033100000516
representing the mean value of the fingerprint feature vectors of the known point o after the mapping of the Gaussian kernel function,
Figure FDA00035730033100000517
representing the mean value of the fingerprint characteristic vectors of the reference point r after the Gaussian kernel function mapping,
Figure FDA00035730033100000518
and
Figure FDA00035730033100000519
the expression of (c) is as follows:
Figure FDA00035730033100000520
Figure FDA00035730033100000521
wherein the content of the first and second substances,
Figure FDA0003573003310000061
and
Figure FDA0003573003310000062
representing the fingerprint feature vectors of the known point o and the reference point r respectively,
Figure FDA0003573003310000063
a kernel matrix which is obtained by mapping the fingerprint characteristic vectors of all known points in the target domain through a Gaussian kernel function,
Figure FDA0003573003310000064
a kernel matrix which is obtained by mapping the fingerprint characteristic vectors of all reference points in the source field through a Gaussian kernel function,
Figure FDA0003573003310000065
and
Figure FDA0003573003310000066
the expression of (a) is as follows:
Figure FDA0003573003310000067
Figure FDA0003573003310000068
will be provided with
Figure FDA00035730033100000634
A plurality of generalized eigenvalues are arranged from large to small before selection
Figure FDA0003573003310000069
A generalized characteristic value, establishing a dimension of
Figure FDA00035730033100000610
The new mapping space of (2);
establishing a source subdomain according to the following rules:
Figure FDA00035730033100000611
wherein the content of the first and second substances,
Figure FDA00035730033100000612
the representation is based on a reference point r2Optimum K value of
Figure FDA00035730033100000613
To the reference point r2Selected for KNN positioning
Figure FDA00035730033100000614
A set of reference points; taking a known point with the same position as a reference point in the same source subdomain as the same target subdomain; dividing each source into domains
Figure FDA00035730033100000615
Each reference point r insaMapping to a dimension of
Figure FDA00035730033100000616
In the new mapping space of (2), obtaining a mapped representation
Figure FDA00035730033100000617
Figure FDA00035730033100000618
Wherein the content of the first and second substances,
Figure FDA00035730033100000619
representing source subdomains
Figure FDA00035730033100000620
And target subdomain
Figure FDA00035730033100000621
A mapping matrix mapped into the new mapping space,
Figure FDA00035730033100000622
representing source domains
Figure FDA00035730033100000623
Reference point rsaThe fingerprint feature vector of (2);
Figure FDA00035730033100000624
representing source subdomains
Figure FDA00035730033100000625
Projecting to a kernel matrix in the new mapping space;
dividing each target into domains
Figure FDA00035730033100000626
Each reference point o intaMapping to a dimension of
Figure FDA00035730033100000627
In the new mapping space of (2), obtaining a mapped representation
Figure FDA00035730033100000628
Figure FDA00035730033100000629
Wherein the content of the first and second substances,
Figure FDA00035730033100000630
representing target domains
Figure FDA00035730033100000631
Reference point otaThe fingerprint feature vector of (2);
Figure FDA00035730033100000632
representing target subdomains
Figure FDA00035730033100000633
Projecting to a kernel matrix in the new mapping space;
forming a new offline derivative fingerprint database by each known point in the new mapping space and the residual reference points in the new mapping space; the residual reference points are reference points in the source field except for the reference points corresponding to the known points;
constructing a radio map according to the fingerprint feature vectors of the new off-line derivative fingerprint library in the new mapping space, wherein the set of new reference points in the radio map is
Figure FDA0003573003310000071
Figure FDA0003573003310000072
Represents the E0 th new reference point in the wireless map, E0 ═ 1, 2.., E0; and a new reference point
Figure FDA0003573003310000073
The optimal K value is the same as the e0 th reference point in the source field; calculating the point to be measured and the new reference point
Figure FDA0003573003310000074
Fingerprint euclidean distance between:
Figure FDA0003573003310000075
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003573003310000076
representing the representation of the fingerprint characteristics corresponding to the points to be measured and the values of e in the new mapping space,
Figure FDA0003573003310000077
indicating a new reference point
Figure FDA0003573003310000078
The representation of the fingerprint features corresponding to the e values in the new mapping space;
selecting the optimal K value corresponding to the new reference point closest to the Euclidean distance of the fingerprint of the point to be measured as the optimal K value of the point to be measured
Figure FDA0003573003310000079
Arranging the Euclidean distances of fingerprints between all new reference points and points to be measured from small to large, before selecting
Figure FDA00035730033100000710
New reference point composition set corresponding to individual fingerprint Euclidean distance
Figure FDA00035730033100000711
Thus obtaining the position of the point to be measured:
Figure FDA00035730033100000712
wherein
Figure FDA00035730033100000713
Representation collection
Figure FDA00035730033100000714
The first of the new reference points in the first,
Figure FDA00035730033100000715
representation collection
Figure FDA00035730033100000716
To middle
Figure FDA00035730033100000717
The new reference point is the point of reference,
Figure FDA00035730033100000720
representation collection
Figure FDA00035730033100000718
Zhongxin reference point r'*The actual position information of the mobile station (ms),
Figure FDA00035730033100000719
representing points to be measured and a new reference point r'*The euclidean distance of the fingerprints therebetween.
CN202210330811.2A 2022-03-30 2022-03-30 Indoor intelligent positioning method based on derivative fingerprint migration Pending CN114679683A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116233747A (en) * 2023-03-09 2023-06-06 江苏亨鑫科技有限公司 BLE positioning method and device based on transfer learning and fingerprint library updating

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116233747A (en) * 2023-03-09 2023-06-06 江苏亨鑫科技有限公司 BLE positioning method and device based on transfer learning and fingerprint library updating
CN116233747B (en) * 2023-03-09 2023-12-22 江苏亨鑫科技有限公司 BLE positioning method and device based on transfer learning and fingerprint library updating

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