CN107484123A - A kind of WiFi indoor orientation methods based on integrated HWKNN - Google Patents

A kind of WiFi indoor orientation methods based on integrated HWKNN Download PDF

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CN107484123A
CN107484123A CN201710600157.1A CN201710600157A CN107484123A CN 107484123 A CN107484123 A CN 107484123A CN 201710600157 A CN201710600157 A CN 201710600157A CN 107484123 A CN107484123 A CN 107484123A
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CN107484123B (en
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陈绍建
龙云亮
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Sun Yat Sen University
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Sun Yat Sen 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
    • H04W4/02—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
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • 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

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Quality & Reliability (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Other Investigation Or Analysis Of Materials By Electrical Means (AREA)

Abstract

The present invention discloses a kind of WiFi indoor orientation methods based on integrated HWKNN, including：1) reliable indoor WiFi fingerprint databases are built；2）According to the fingerprint database of foundation, the position coordinates of each access point and corresponding indoor path loss model are estimated；3）When calculating the Euclidean distance between node and fingerprint database to be positioned, 2 are utilized）Obtained indoor path loss model is that corresponding dimension assigns weight；4）To step 3）Obtained Euclidean distance selects the K minimum reference point of distance, and K reference point is weighted according to signal intensity similarity and determines node location to be positioned；5）By step 24）Method as a kind of weak fix method, set up several weak fix estimators by randomly selecting the size of K values and the number of access point finally to estimate the position coordinates of node to be positioned.The location algorithm proposed in the present invention can effectively strengthen the robustness of indoor positioning, final to realize indoor precise positioning.

Description

A kind of WiFi indoor orientation methods based on integrated HWKNN
Technical field
The present invention relates to machine learning, it is wirelessly transferred and indoor positioning field, and in particular to one kind is based on integrated HWKNN WiFi indoor orientation methods.
Background technology
With the extensive use of mobile device and the popularization of wireless network so that location Based service (LBS) not only may be used User behavior information can also be further excavated to obtain customer position information, therefore LBS shows good academic development Prospect and the wide market demand.Positioning field out of doors, global positioning system (GPS) have been realized in precision positioning, but Still it is difficult to accurately position in indoor environment.
In the past decade, with the development of WiFi technology and the gradual increase of wireless access dot coverage, based on wireless Serial of methods has been proposed in the indoor positioning of LAN (WLAN).But because indoor radio signal propagates complexity, no Access point (AP) position is easily found, and determines the coefficient of propagation model.It is main in propagation model localization method based on WiFi Technology is wanted to have AOA models, TOA models, TDOA models and signal path loss model.But due to the complexity of indoor environment, use Family self stops that the different directions of mobile phone and other disturbing factors prevent indoor positioning from meeting high-precision requirement.Cause This, is there is an urgent need to be LBS developing low-costs, the high-precision indoor locating system (IPS) based on WiFi.
Compared to the indoor positioning based on propagation model, the indoor positioning based on WiFi fingerprints is easier to dispose, low cost Wireless signal noise is effectively tolerated, so as to reach highest precision.Traditional K arest neighbors (KNN) algorithm is indoor positioning In one of the most frequently used algorithm, the signal intensity of each WiFi access points received first by calculating at node to be positioned and Euclidean distance between the signal intensity of reference point in fingerprint database, K closest reference points are then found out, using not With weighting scheme increase the influence of the larger reference point positioning result of similarity, finally estimate node location to be positioned Coordinate.But due to the complexity of indoor environment, the signal intensity from different WiFi access points suffers from different degrees of External interference, the relation between signal intensity and physical location are not simple linear mapping relation, and in section to be positioned Unstable WiFi access points can not be determined at point, so as to cause the decline of positioning precision.
The content of the invention
The purpose of the present invention, which essentially consists in, overcomes above-mentioned technological deficiency, there is provided a kind of WiFi based on integrated HWKNN Indoor orientation method, this method consider not only Euclidean distance between reference fingerprint in positioning, it is also contemplated that signal intensity and The influence of the non-linear relation of physical location and the signal value of varying strength to positioning result, and estimated by several weak fixs Device forms strong fix estimator to increase the robustness of alignment system, so as to improve the precision of positioning.
To achieve these goals, the technical scheme is that：
A kind of WiFi indoor orientation methods based on integrated HWKNN, method comprise the following steps：
1) reliable indoor WiFi fingerprint databases are built；
2) according to the fingerprint database of foundation, the position coordinates of each access point and the interior of each access point are estimated Path loss model；
3) Euclidean distance between each fingerprint in calculating the fingerprint database of finger print information to be positioned and structure When, the indoor path loss model of the diverse access point obtained using step 2) assigns weight for corresponding dimension；
4) it is as a reference point that the K minimum fingerprint of distance is selected in the Euclidean distance obtained from step 3), and according to signal Intensity similarity is weighted to K reference point and determines node location to be positioned；
5) using step 2-4) method as a kind of weak fix method, randomly select the size and WiFi access points of K values Number (employs 20 WiFi access points, the random scope of k values is set to 5-15 and WiFi altogether in the example of the application The random scope of the number of access point is set to 16-20) several weak fix estimators are set up, estimated by this several weak fix The strong fix estimator of gauge composition draws final position coordinates to be positioned.
Further, the reliable indoor WiFi fingerprint databases of step 1) structure, comprise the following steps：
1-1) in area to be targeted every a step as a reference point；
1-2) each reference point carries out multiple RSS signal datas measurement, and the WAP of each reference point is physically Location information, the average value of signal intensity and corresponding positional information structure location fingerprint, then should according to all reference points Location fingerprint form location fingerprint storehouse.
Further, the step 2) estimates the position coordinates of each access point and the indoor path of each access point Loss model, comprise the following steps：
The physical distance of the nearer reference point of distance WiFi access point positions 2-1) is calculated, wherein nearer reference point refers to Signal intensity is less than -60Dbm reference point, and its RSS signal intensity is stronger：
Wherein,For the signal intensity of j-th of WiFi access point, i=1,2 ..., m, m is that j-th of WiFi of distance connects The number of the reference point of access point position relatively near (signal intensity is less than -60Dbm), (nf,Af) it is free space path loss propagating mode Shape parameter, it is given value；
2-2) calculate the position coordinates of each WiFi access points：
Wherein,For the position coordinates of j-th of WiFi access point,For the of j-th of WiFi access point I reference point；
2-3) with the coordinate and signal of the reference point of distance WiFi access point positions relatively near (signal intensity is less than -60Dbm) Intensity is as input, to solve WiFi access point positions coordinate as output；
2-4) simultaneous formula (1) and formula (2), constructed fuction group：
Order：
Equation is represented as：
wjXj=Yj (7)
Its object function expression formula is：
min||wjXj-Yj||2 (8)
Wherein | | | | it is norm, finally tries to achieve coordinateThe position of the WiFi access points of as required solution Put coordinate；
The expression formula of indoor path loss model described in 2-5) is：
Wherein,For the signal intensity of j-th of WiFi access point of p-th of reference point,For p-th of reference The position coordinates of j-th of WiFi access point of point, p=1,2 ..., I, I are that can receive j-th of WiFi access point signals The quantity of reference point, εjRepresent the gaussian random error of j-th of WiFi access point, (nj,Aj) represent j-th of WiFi access points room Interior path loss model parameters；
2-6) it is formula (9) constructed fuction group：
Order：
Equation is represented as：
ψjVj=RSSj (13)
Its object function expression formula is：
min||ψjVj-RSSj||2+λ||Vj|| (14)
Machine learning model 2-7) is established, is learnt based on fingerprint database, obtains the interior of each WiFi access points Path loss model parameters.
Further, step 3) assigns power using the indoor path loss model of different WiFi access points for corresponding dimension Weight, comprises the following steps：
3-1) different weights is assigned to the signal intensity of different WiFi access points, it is assumed that node to be positioned receives Signal intensity vector beWherein n is the quantity of WiFi access points, then is treated The physical distance of the node location of positioning and j-th of WiFi access point is calculated：
The distance obtained using formula (15) is weighted coefficient w to the signal intensity of j-th of WiFi access pointjIt is weighted to：
3-2) calculation formula of the signal intensity Euclidean distance between node to be positioned and the i-th reference point is：
Further, step 4) is carried out not according to the different of the Euclidean distance between node to be positioned and K reference point Same weighting, (distance is nearer, the bigger reference point position contribution to be positioned for make it that similarity is bigger of the similarity of signal intensity Degree is bigger), including：
If the unit element distance between preceding K reference point and node to be positioned is：D1,D2…DK, then q-th of reference Point corresponding to weight be：
Node location coordinate to be positioned is calculated as follows：
Further, step 5) randomly selects the size of K values and the number of WiFi access points to build h weak fix estimation Device, the strong fix estimator being made up of weak fix estimator draw final position coordinates to be positioned, are calculated as follows：
The beneficial effects of the invention are as follows：
1) by setting different propagation model parameters for each WiFi access point, it is contemplated that signal intensity and physical location Relation, compared to use same propagation model parameter, the present invention there is higher positioning precision.
2) utilization randomly selects K values size and the mode of WiFi access point numbers builds multiple HWKNN weak fixs estimators, And formed HWKNN strong fix estimators.This method can effectively improve the robustness and positioning precision of alignment system.
Brief description of the drawings
Fig. 1 is the structural representation of the WiFi indoor orientation methods based on integrated HWKNN.
Embodiment
As shown in figure 1, a kind of WiFi indoor orientation methods based on integrated HWKNN, comprise the following steps：
1) reliable indoor WiFi fingerprint databases are built, are comprised the following steps：
1-1) in area to be targeted every a step as a reference point；
1-2) each reference point carries out multiple RSS signal datas measurement, and the WAP of each reference point is physically Location information, the average value of signal intensity and corresponding positional information structure location fingerprint, if there is dropout, the value is set 100 are set to, then location fingerprint forms location fingerprint storehouse according to corresponding to all reference points.
2) position coordinates of each access point and the indoor path loss model of each access point are estimated, including it is following Step：
The physical distance of the nearer reference point of distance WiFi access point positions 2-1) is calculated, its RSS signal intensity is stronger：
Wherein,For the signal intensity of j-th of WiFi access point, i=1,2 ..., m, m is that j-th of WiFi of distance connects The number of the nearer reference point in access point position, (nf, Af) are free space path loss propagation model parameter, and it is given value；
2-2) calculate the position coordinates of each WiFi access points：
Wherein,For the position coordinates of j-th of WiFi access point,For the of j-th of WiFi access point I reference point；
2-3) using the coordinate of the nearer reference point of distance WiFi access point positions and signal intensity as input, to solve WiFi access point positions coordinate is as output；
2-4) simultaneous formula (1) and formula (2), constructed fuction group:
Order：
Equation can be represented as：
wjXj=Yj (7)
Its object function expression formula is:
min||wjXj-Yj||2 (8)
Wherein | | | | it is norm, finally tries to achieve coordinateThe position of the WiFi access points of as required solution Put coordinate.
The expression formula of indoor path loss model described in 2-5) is：
Wherein,For the signal intensity of j-th of WiFi access point of p-th of reference point,For p-th of ginseng The position coordinates of j-th of WiFi access point of examination point, p=1,2 ..., I, I are that can receive j-th of WiFi access point signals Reference point quantity, εjRepresent the gaussian random error of j-th of WiFi access point, (nj,Aj) represent j-th of WiFi access point Indoor path loss model parameter；
2-6) it is formula (9) constructed fuction group：
Order：
Equation can be represented as：
ψjVj=RSSj (13)
Its object function expression formula is：
min||ψjVj-RSSj||2+λ||Vj|| (14)
Machine learning model 2-7) is established, is learnt based on fingerprint database, obtains the interior of each WiFi access points Path loss model parameters.
3) weight, including following step are assigned for corresponding dimension using the indoor path loss model of different WiFi access points Suddenly：
3-1) different weights is assigned to the signal intensity of different WiFi access points, it is assumed that node to be positioned receives Signal intensity vector beWherein n is the quantity of WiFi access points, then The physical distance of node location to be positioned and j-th of WiFi access point is calculated：
The distance obtained using formula (15) is weighted coefficient w to the signal intensity of j-th of WiFi access pointjIt is weighted to：
3-2) calculation formula of the signal intensity Euclidean distance between node to be positioned and the i-th reference point is：
4) add according to the progress of the difference of the signal intensity similarity between node to be positioned and K reference point is different Power, including：
If the unit element distance between preceding K reference point and node to be positioned is：D1,D2…DK, then q-th of reference Point corresponding to weight be：
Node location coordinate to be positioned is calculated as follows：
5) size of K values and the number of WiFi access points are randomly selected to build h weak fix estimator, is estimated by weak fix The strong fix estimator of gauge composition draws final position coordinates to be positioned, is calculated as follows：
The specific embodiment of the above-mentioned detailed description present invention, but and it is nonrestrictive, therefore the present invention is not limited only to have Specific embodiment described in body implementation, it will be understood by those of skill in the art that carrying out equivalent substitution to the solution of the present invention Or modification, all should be in the scope of protection of the invention.

Claims (6)

1. a kind of WiFi indoor orientation methods based on integrated HWKNN, it is characterised in that comprise the following steps：
1) reliable indoor WiFi fingerprint databases are built；
2) according to the fingerprint database of foundation, the position coordinates of each access point and the indoor path of each access point are estimated Loss model；
3) during Euclidean distance between each fingerprint in calculating the fingerprint database of finger print information to be positioned and structure, profit The indoor path loss model of the diverse access point obtained with step 2) is that corresponding dimension assigns weight；
4) it is as a reference point that the K minimum fingerprint of distance is selected in the Euclidean distance obtained from step 3), and according to signal intensity Similarity is weighted to K reference point and determines node location to be positioned；
5) using step 2-4) method as a kind of weak fix method, randomly select the size of K values and the number of WiFi access points Several weak fix estimators are set up, the strong fix estimator being made up of this several weak fix estimator draws final undetermined The position coordinates of position.
2. a kind of WiFi indoor orientation methods based on integrated HWKNN according to claim 1, it is characterised in that described The reliable indoor WiFi fingerprint databases of step 1) structure, comprise the following steps：
1-1) in area to be targeted every a step as a reference point；
1-2) each reference point carries out multiple RSS signal datas measurement, the physical address letter of the WAP of each reference point Breath, the average value of signal intensity and corresponding positional information build location fingerprint, then the position according to corresponding to all reference points Put fingerprint and form location fingerprint storehouse.
A kind of 3. WiFi indoor orientation methods based on integrated HWKNN according to claim 1 or 2, it is characterised in that institute State step 2) and estimate the position coordinates of each access point and the indoor path loss model of each access point, including following step Suddenly：
The physical distance of the nearer reference point of distance WiFi access point positions 2-1) is calculated, wherein nearer reference point refers to signal Intensity is less than -60Dbm reference point, and its RSS signal intensity is stronger：
<mrow> <msubsup> <mi>d</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mfrac> <mrow> <msubsup> <mi>RSS</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msup> <mi>A</mi> <mi>f</mi> </msup> </mrow> <mrow> <mn>10</mn> <msup> <mi>n</mi> <mi>f</mi> </msup> </mrow> </mfrac> </mrow> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
Wherein,For the signal intensity of j-th of WiFi access point, i=1,2 ..., m, m is j-th of WiFi access points position of distance Put the number of the reference point of relatively near (signal intensity is less than -60Dbm), (nf,Af) join for free space path loss propagation model Number, it is given value；
2-2) calculate the position coordinates of each WiFi access points：
<mrow> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <mo>=</mo> <msubsup> <mi>d</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
Wherein,For the position coordinates of j-th of WiFi access point,For i-th of j-th of WiFi access point Reference point；
2-3) with the coordinate and signal intensity of the reference point of distance WiFi access point positions relatively near (signal intensity is less than -60Dbm) As input, to solve WiFi access point positions coordinate as output；
2-4) simultaneous formula (1) and formula (2), constructed fuction group：
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mfrac> <mrow> <msubsup> <mi>RSS</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>+</mo> <msup> <mi>A</mi> <mi>f</mi> </msup> </mrow> <mrow> <mn>10</mn> <msup> <mi>n</mi> <mi>f</mi> </msup> </mrow> </mfrac> </mrow> </msup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mn>2</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mn>2</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mfrac> <mrow> <msubsup> <mi>RSS</mi> <mn>2</mn> <mi>j</mi> </msubsup> <mo>+</mo> <msup> <mi>A</mi> <mi>f</mi> </msup> </mrow> <mrow> <mn>10</mn> <msup> <mi>n</mi> <mi>f</mi> </msup> </mrow> </mfrac> </mrow> </msup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>...</mn> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mfrac> <mrow> <msubsup> <mi>RSS</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msup> <mi>A</mi> <mi>f</mi> </msup> </mrow> <mrow> <mn>10</mn> <msup> <mi>n</mi> <mi>f</mi> </msup> </mrow> </mfrac> </mrow> </msup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
Order：
<mrow> <msup> <mi>Y</mi> <mi>j</mi> </msup> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>-</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>-</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>-</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <mrow> <msubsup> <mi>RSS</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>+</mo> <msup> <mi>A</mi> <mi>f</mi> </msup> </mrow> <mrow> <mn>10</mn> <msup> <mi>n</mi> <mi>f</mi> </msup> </mrow> </mfrac> </mrow> </msup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <mrow> <msubsup> <mi>RSS</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msup> <mi>A</mi> <mi>f</mi> </msup> </mrow> <mrow> <mn>10</mn> <msup> <mi>n</mi> <mi>f</mi> </msup> </mrow> </mfrac> </mrow> </msup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>-</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>-</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>-</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <mrow> <msubsup> <mi>RSS</mi> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> <mo>+</mo> <msup> <mi>A</mi> <mi>f</mi> </msup> </mrow> <mrow> <mn>10</mn> <msup> <mi>n</mi> <mi>f</mi> </msup> </mrow> </mfrac> </mrow> </msup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <mrow> <msubsup> <mi>RSS</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msup> <mi>A</mi> <mi>f</mi> </msup> </mrow> <mrow> <mn>10</mn> <msup> <mi>n</mi> <mi>f</mi> </msup> </mrow> </mfrac> </mrow> </msup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
<mrow> <msup> <mi>w</mi> <mi>j</mi> </msup> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <mn>2</mn> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <mn>2</mn> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>2</mn> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <mn>2</mn> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mi>n</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
<mrow> <msup> <mi>X</mi> <mi>j</mi> </msup> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msubsup> <mi>x</mi> <mn>0</mn> <mi>j</mi> </msubsup> </mtd> </mtr> <mtr> <mtd> <msubsup> <mi>y</mi> <mn>0</mn> <mi>j</mi> </msubsup> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
Equation (3) is expressed as：
wjXj=Yj (7)
Its object function expression formula is：
min||wjXj-Yj||2 (8)
Wherein | | | | it is norm, finally tries to achieve coordinateThe position of the WiFi access points of as required solution is sat Mark；
The expression formula of indoor path loss model described in 2-5) is：
<mrow> <msubsup> <mi>RSS</mi> <mi>p</mi> <mi>j</mi> </msubsup> <mo>=</mo> <mo>-</mo> <mn>10</mn> <msup> <mi>n</mi> <mi>j</mi> </msup> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mi>p</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mi>p</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <mo>)</mo> </mrow> <mo>-</mo> <msup> <mi>A</mi> <mi>j</mi> </msup> <mo>+</mo> <msup> <mi>&amp;epsiv;</mi> <mi>j</mi> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
Wherein,For the signal intensity of j-th of WiFi access point of p-th of reference point,For p-th reference point The position coordinates of j-th of WiFi access point, p=1,2 ..., I, I are the reference that can receive j-th of WiFi access point signals The quantity of point, εjRepresent the gaussian random error of j-th of WiFi access point, (nj,Aj) represent j-th of WiFi access point Shi Nei road Footpath loss model parameter；
2-6) it is formula (9) constructed fuction group：
Order：
<mrow> <msup> <mi>RSS</mi> <mi>j</mi> </msup> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mi>R</mi> <mi>S</mi> <msubsup> <mi>S</mi> <mn>1</mn> <mi>j</mi> </msubsup> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>RSS</mi> <mn>2</mn> <mi>j</mi> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>...</mo> </mtd> </mtr> <mtr> <mtd> <mi>R</mi> <mi>S</mi> <msubsup> <mi>S</mi> <mi>I</mi> <mi>j</mi> </msubsup> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow> 2
<mrow> <msup> <mi>&amp;psi;</mi> <mi>j</mi> </msup> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <mo>-</mo> <mn>10</mn> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mn>1</mn> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <mn>10</mn> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mn>2</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mn>2</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mn>1</mn> </mtd> </mtr> <mtr> <mtd> <mn>...</mn> </mtd> <mtd> <mn>1</mn> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <mn>10</mn> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mi>l</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mi>l</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mn>1</mn> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
<mrow> <msup> <mi>V</mi> <mi>j</mi> </msup> <mo>=</mo> <msup> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msup> <mi>n</mi> <mi>j</mi> </msup> </mtd> </mtr> <mtr> <mtd> <msup> <mi>A</mi> <mi>j</mi> </msup> </mtd> </mtr> </mtable> </mfenced> <mi>T</mi> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>12</mn> <mo>)</mo> </mrow> </mrow>
Equation (10) is expressed as：
ψjVj=RSSj (13)
Its object function expression formula is：
min||ψjVj-RSSj||2+λ||Vj|| (14)
Machine learning model 2-7) is established, is learnt based on fingerprint database, obtains the indoor path of each WiFi access points Loss model parameter.
A kind of 4. WiFi indoor orientation methods based on integrated HWKNN according to claim 3, it is characterised in that step 3) weight is assigned for corresponding dimension using the indoor path loss model of different WiFi access points, comprised the following steps：
3-1) different weights is assigned to the signal intensity of different WiFi access points, it is assumed that the letter that node to be positioned receives Number intensity vector isWherein n is the quantity of WiFi access points, then to be positioned Node location and j-th of WiFi access point physical distance：
<mrow> <msubsup> <mi>d</mi> <mi>u</mi> <mi>j</mi> </msubsup> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mfrac> <mrow> <msubsup> <mi>RSS</mi> <mi>u</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msup> <mi>A</mi> <mi>j</mi> </msup> </mrow> <mrow> <mn>10</mn> <msup> <mi>n</mi> <mi>j</mi> </msup> </mrow> </mfrac> </mrow> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>15</mn> <mo>)</mo> </mrow> </mrow>
The distance obtained using formula (15) is weighted coefficient w to the signal intensity of j-th of WiFi access pointjIt is weighted to：
<mrow> <msup> <mi>w</mi> <mi>j</mi> </msup> <mo>=</mo> <mfrac> <mrow> <mn>1</mn> <mo>/</mo> <msubsup> <mi>d</mi> <mi>u</mi> <mi>j</mi> </msubsup> </mrow> <mrow> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </msubsup> <mn>1</mn> <mo>/</mo> <msubsup> <mi>d</mi> <mi>u</mi> <mi>p</mi> </msubsup> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>16</mn> <mo>)</mo> </mrow> </mrow>
3-2) calculation formula of the signal intensity Euclidean distance between node to be positioned and the i-th reference point is：
<mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> <mo>=</mo> <msqrt> <mrow> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </msubsup> <msup> <mi>w</mi> <mi>j</mi> </msup> <msup> <mrow> <mo>(</mo> <msubsup> <mi>RSS</mi> <mi>u</mi> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>RSS</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> <mo>.</mo> </mrow>
A kind of 5. WiFi indoor orientation methods based on integrated HWKNN according to claim 4, it is characterised in that step 4) K reference point is weighted according to Euclidean distance, i.e. signal intensity similarity, including：
If the unit element distance between preceding K reference point and node to be positioned is：D1,D2…DK, then q-th of reference point The weight answered is：
<mrow> <msub> <mi>&amp;theta;</mi> <mi>q</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mn>1</mn> <mo>/</mo> <msub> <mi>D</mi> <mi>q</mi> </msub> </mrow> <mrow> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </msubsup> <mn>1</mn> <mo>/</mo> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>18</mn> <mo>)</mo> </mrow> </mrow>
Node location coordinate to be positioned is calculated as follows：
<mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mi>u</mi> <mo>&amp;prime;</mo> </msubsup> <mo>,</mo> <msubsup> <mi>y</mi> <mi>u</mi> <mo>&amp;prime;</mo> </msubsup> <mo>)</mo> <mo>=</mo> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </msubsup> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>)</mo> <mo>*</mo> <msub> <mi>&amp;theta;</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mo>(</mo> <mn>19</mn> <mo>)</mo> <mo>.</mo> </mrow>
A kind of 6. WiFi indoor orientation methods based on integrated HWKNN according to claim 5, it is characterised in that step 5) size of K values and the number of WiFi access points are randomly selected to build h weak fix estimator, is made up of weak fix estimator Strong fix estimator draw final position coordinates to be positioned, be calculated as follows：
<mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>u</mi> </msub> <mo>,</mo> <msub> <mi>y</mi> <mi>u</mi> </msub> <mo>)</mo> <mo>=</mo> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>h</mi> </msubsup> <mo>(</mo> <msubsup> <mi>x</mi> <mi>i</mi> <mo>&amp;prime;</mo> </msubsup> <mo>,</mo> <msubsup> <mi>y</mi> <mi>i</mi> <mo>&amp;prime;</mo> </msubsup> <mo>)</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mo>(</mo> <mn>20</mn> <mo>)</mo> <mo>.</mo> </mrow> 4
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