CN107231615A - A kind of localization method and system based on network fingerprinting - Google Patents
A kind of localization method and system based on network fingerprinting Download PDFInfo
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- CN107231615A CN107231615A CN201710500551.8A CN201710500551A CN107231615A CN 107231615 A CN107231615 A CN 107231615A CN 201710500551 A CN201710500551 A CN 201710500551A CN 107231615 A CN107231615 A CN 107231615A
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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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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Abstract
The technical program provides a kind of localization method based on network fingerprinting, method includes receiving the positional parameter that user terminal is sent, positional parameter includes cell information and signal strength information, call all fingerprint lists including cell information, based on the similar target fingerprint list of feature is matched in signal strength information and all fingerprint lists, positional information is generated based on target fingerprint list.The positional parameter of the user received is compared with existing fingerprint list, find out the fingerprint list (target fingerprint list) being most consistent, so as to being positioned to user, it is to avoid the influence of environment terrain and weather conditions to positioning result, so as to improve positioning precision.
Description
Technical field
The present invention relates to wireless location technology field, more specifically to a kind of localization method based on network fingerprinting
And system.
Background technology
With the progress and the development of society of science and technology, location technology is more and more applied to daily life
In, such as various vehicle mounted guidances and cell phone map APP.
In the prior art, wireless location technology is mainly positioned using triangulation location.By taking GPS location principle as an example：
24 satellites be evenly distributed in 6 orbital planes, each orbital plane respectively have 4 satellites detour the earth operating, allow ground to use
No matter person is in any place, any time, the gps satellite of at least more than 4 appears in us and above used in the air for user.
Every satellite, which is all launched earth's surface to cover, itself carries the coordinate of orbital plane, the radio signals of run time, the reception list on ground
Position can be according to these data as accurate measurements such as positioning, navigation, terrestrial references.However, triangulation location is by environment terrain and weather
Larger deviation occurs in the influence of factor.
The content of the invention
In view of this, it is an object of the invention to provide a kind of localization method based on network fingerprinting, by the use received
The positional parameter at family is compared with existing fingerprint list, finds out the fingerprint list (target fingerprint list) being most consistent, so that
User is positioned, it is to avoid the influence of environment terrain and weather conditions to positioning result, so as to improve positioning precision.
To achieve the above object, the present invention provides following technical scheme：
A kind of localization method based on network fingerprinting, methods described includes：
The positional parameter that user terminal is sent is received, the positional parameter includes cell information and signal strength information；
Call all fingerprint lists including the cell information；
The similar target fingerprint list of feature is matched in all fingerprint lists based on the signal strength information；
Positional information is generated based on the target fingerprint list.
Preferably, receive before the positional parameter, methods described also includes：
Gathering geographic position information；
Gridding information is generated based on the geographical location information；
Gather network environment information；
Based on the gridding information and network environment information generation fingerprint list；
Store the fingerprint list.
Preferably, the signal strength information includes signal intensity attenuation information and signal intensity value information, described to be based on
The signal strength information matches the similar target fingerprint list of feature in all fingerprint lists to be included：
Based on the signal intensity attenuation information and all fingerprint list generation similar fingerprints lists；
The target fingerprint list is generated based on the signal intensity value information and the similar fingerprints list.
Preferably, it is described based on target fingerprint list generation when the quantity of the target fingerprint list is more than for the moment
Positional information includes：
Calculate the affinity score value of all target fingerprint lists respectively based on the signal strength information；
Positional information is generated based on target fingerprint list described in the affinity score value highest.
Preferably, it is described to be included based on target fingerprint list generation positional information：
Position range information is generated based on the target fingerprint list；
Call triangulation location；
The position letter is generated based on the signal strength information, the position range information and the triangulation location
Breath.
A kind of alignment system based on network fingerprinting, the system includes receiving module, calling module, the first generation module
And second generation module, wherein：
The receiving module is used for the positional parameter for receiving user terminal transmission, and the positional parameter includes cell information and letter
Number strength information；
The calling module is used to call all fingerprint lists including the cell information；
First generation module is used to match spy in all fingerprint lists based on the signal strength information
Levy similar target fingerprint list；
Second generation module is used to generate positional information based on the target fingerprint list.
Preferably, the system also includes the first acquisition module, the 3rd generation module, the second acquisition module, the 4th generation
Module and memory module, wherein：
First acquisition module is used for gathering geographic position information；
3rd generation module is used to generate gridding information based on the geographical location information；
Second acquisition module is used to gather network environment information；
4th generation module is used for based on the gridding information and network environment information generation fingerprint list；
The memory module is used to store the fingerprint list.
Preferably, the signal strength information includes signal intensity attenuation information and signal intensity value information, described first
Generation module includes the first generation unit and the second generation unit, wherein：
First generation unit is used for based on the signal intensity attenuation information and all fingerprint list generation phases
Like fingerprint list；
Second generation unit is used for described based on the signal intensity value information and similar fingerprints list generation
Target fingerprint list.
Preferably, second generation module includes computing unit and the 3rd generation unit, wherein：
The computing unit is used for the phase for calculating all target fingerprint lists respectively based on the signal strength information
Like fractional value；
3rd generation unit is used to be based on target fingerprint list generation position described in the affinity score value highest
Information.
Preferably, second generation module includes the 4th generation unit, call unit and the 5th generation unit, wherein：
4th generation unit is used to generate position range information based on the target fingerprint list；
The call unit is used to call triangulation location；
5th generation unit is used for fixed based on the signal strength information, the position range information and the triangle
Position method generates the positional information.
In summary, the technical program provides a kind of localization method based on network fingerprinting, and method includes receiving user
The positional parameter sent is held, positional parameter includes cell information and signal strength information, calls all fingers including cell information
Line list, matches the similar target fingerprint list of feature, based on target based on signal strength information in all fingerprint lists
Fingerprint list generates positional information.The positional parameter of the user received is compared with existing fingerprint list, found out most
The fingerprint list (target fingerprint list) being consistent, so as to be positioned to user, it is to avoid environment terrain and weather conditions are to fixed
The influence of position result, so as to improve positioning precision.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is a kind of flow chart of the embodiment 1 of the localization method based on network fingerprinting disclosed by the invention；
Fig. 2 is the flow chart of the embodiment 2 of another localization method based on network fingerprinting disclosed by the invention；
Fig. 3 is the flow chart of the embodiment 3 of another localization method based on network fingerprinting disclosed by the invention；
Fig. 4 is the flow chart of the embodiment 4 of another localization method based on network fingerprinting disclosed by the invention；
Fig. 5 is a kind of structural representation of the embodiment 1 of the alignment system based on network fingerprinting disclosed by the invention；
Fig. 6 is the structural representation of the embodiment 2 of another alignment system based on network fingerprinting disclosed by the invention；
Fig. 7 is the structural representation of the embodiment 3 of another alignment system based on network fingerprinting disclosed by the invention；
Fig. 8 is the structural representation of the embodiment 4 of another alignment system based on network fingerprinting disclosed by the invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is all other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
As shown in figure 1, a kind of flow chart of the embodiment 1 of the localization method based on network fingerprinting provided for the present invention,
Methods described includes：
S101, the positional parameter for receiving user terminal transmission, positional parameter include cell information and signal strength information；
User terminal send positional parameter can be Original CDR and MR (Measurement Report, measurement report),
Every ticket or MR contain main plot and information of adjacent cells, and corresponding RSRP (Reference Signal
Receiving Power, Reference Signal Received Power), the important positioning such as TA (Timing Advance, Timing Advance) according to
According to parameter.Cell information includes the list of the current main plot of this user terminal and adjacent cell, and signal strength information includes this user
The signal intensity and the signal intensity of adjacent cell of the current main plot in end.
S102, call all fingerprint lists including cell information；
Fingerprint list is stored in corresponding special fingerprint base (memory module), one net of each fingerprint list correspondence
It may include in lattice, each fingerprint list：The PCI (Physical Cell Identifier, i.e. Physical Cell Identifier) of main plot,
The RSRP (Reference Signal Receiving Power, Reference Signal Received Power) of main plot and adjacent cell, TA (when
Between lead, time advance) and data sampling frequency number of times etc., may also include the area attribute of correspondence grid, example
Such as：Road, office building, residential building, market etc..The fingerprint list of all main plots including in positional parameter is called, for example, fixed
Include A main plots relevant information in the parameter of position, then call all fingerprint lists for including A main plots relevant information.
S103, the similar target fingerprint list of feature is matched in all fingerprint lists based on signal strength information；
Include in positional parameter in the RSRP and/or TA between customer location and main plot and adjacent cell, fingerprint list
If RSRP and/or TA when including user in this grid and between main plot and adjacent cell, it is worth mentioning at this point that, fingerprint list
The RSRP and/or TA included is a value range.The fingerprint list being consistent with the signal strength information in positional parameter is found out,
Then this fingerprint list is target fingerprint list.For example：The cell information of user terminal is that main plot is A cells, and adjacent cell is B small
Area and C cells.Include the signal intensity of A cells, B cells and C cells that active user's termination is received in signal strength information,
Thus the magnitude relationship of the signal intensity of A cells, B cells and C cells in signal strength information can also be obtained.In all fingerprints
Find out identical with the magnitude relationship in signal strength information in list, and it is small to meet A cells in signal strength information, B cells and C
The fingerprint list of the intensity level in area, as target fingerprint list.
S104, based on target fingerprint list generate positional information；
Because all fingerprint lists are corresponding with grid, therefore the positional information of user can be obtained.
The technical program provides a kind of localization method based on network fingerprinting, and method includes receiving determining for user terminal transmission
Position parameter, positional parameter includes cell information and signal strength information, calls all fingerprint lists including cell information, be based on
Signal strength information and the generation target fingerprint list of all fingerprint lists, positional information is generated based on target fingerprint list.It will connect
The positional parameter of the user received is compared with existing fingerprint list, finds out fingerprint list (the target fingerprint row being most consistent
Table), so as to be positioned to user, it is to avoid the influence of environment terrain and weather conditions to positioning result so that improve it is fixed
Position precision.
As shown in Fig. 2 the flow of the embodiment 2 of another localization method based on network fingerprinting provided for the present invention
Figure, methods described includes：
S201, the positional parameter for receiving user terminal transmission, positional parameter include cell information and signal strength information；
User terminal send positional parameter can be Original CDR and MR (Measurement Report, measurement report),
Every ticket or MR contain main plot and information of adjacent cells, and corresponding RSRP (Reference Signal
Receiving Power, Reference Signal Received Power), the important positioning such as TA (Timing Advance, Timing Advance) according to
According to parameter.Cell information includes the list of the current main plot of this user terminal and adjacent cell, and signal strength information includes this user
The signal intensity and the signal intensity of adjacent cell of the current main plot in end.
S202, call all fingerprint lists including cell information；
Fingerprint list is stored in corresponding special fingerprint base (memory module), one net of each fingerprint list correspondence
It may include in lattice, each fingerprint list：The PCI (Physical Cell Identifier, i.e. Physical Cell Identifier) of main plot,
The RSRP (Reference Signal Receiving Power, Reference Signal Received Power) of main plot and adjacent cell, TA (when
Between lead, time advance) and data sampling frequency number of times etc., may also include the area attribute of correspondence grid, example
Such as：Road, office building, residential building, market etc..The fingerprint list of all main plots including in positional parameter is called, for example, fixed
Include A main plots relevant information in the parameter of position, then call all fingerprint lists for including A main plots relevant information.
S203, based on signal intensity attenuation information and all fingerprint lists generation similar fingerprints list；
Signal strength information may include signal strength values and signal intensity attenuation information, and signal intensity attenuation information can be with
The form of signal attenuation ratio equation embodies.It can be found out and the signal intensity in positional parameter based on signal attenuation ratio equation
The similar fingerprint list of fluctuation, i.e. similar fingerprints list.This step is to mention in above-mentioned technical proposal, finds signal intensity big
Small order and the signal intensity size order identical fingerprint list in signal strength information.
S204, based on signal intensity value information and similar fingerprints list generation target fingerprint list；
Find after similar fingerprints list, signal strength values and the signal strength range in similar fingerprints list of positional parameter
Not necessarily it is consistent, rejects the similar fingerprints list not being consistent, remaining is target fingerprint list.
S205, the affinity score value for calculating based on signal strength information all target fingerprint lists respectively；
Target fingerprint list there may be multiple, can now calculate similar to positional parameter point of each target fingerprint list
Numerical value.RSRP and the net of each main adjacent base station can be calculated according to situations such as the main plot in ticket or MR, adjacent cell PCI, RSRP
Network is decayed and the information such as data similar proportion, RSRP scopes, grid property, sample frequency is sampled in fingerprint base by adding successively
The mode of power point calculates affinity score value, and computational methods can have a variety of, only give one example illustrate below：
It is k1 with each subzone network attenuation ratio full marks, is put in order according to the signal attenuation power of cell, attenuation ratio, is pressed
Error, often differs x% buttons 2^m (m is fluctuation ratio * 100/x) point.Meanwhile, with each cell signal advantage put in order such as beyond
Error range, has n different each button y points of order, amounts to y points of button (n-1) *, this minimum 0 point；
RSRP scope full marks k2, often go beyond the scope n unit, subtracts x point, has m cell button (m*n) the * x that go beyond the scope to divide,
It is same minimum 0 point；
Attribute full marks K3, during according to the working region during attribute of fingerprint, such as road, market, public arena, working, rest
Between the p1-p2 such as residential area points, the common p3-p4 in field suburb points, high mountain lake p5 points (px is the bound of fraction)；
Sampled point accounting full marks K4, general sampled point is more, represents User Activity possibility higher.Recorded according in fingerprint base
The sampled point of same period is collected, is averaged with the sampled point of adjacent all grid, the more accounting fractions of sampled point are more
It is high；Sampled point is fewer, and fraction accounting is lower.Score=(current grid sampled point/adjacent grid average sample point * K4).
Final affinity score value is the sum of aforementioned four value.
S206, based on the list of affinity score value highest target fingerprint generate positional information；
Based on the corresponding mess generation positional information of affinity score value highest target fingerprint list.
It is noted that if the affinity score value of multiple fingerprint lists is identical, can also by sampled point number of times probability because
Son, it is the fingerprint list eventually for positioning to select a fingerprint list.
Go out outside the above method, for multiple fingerprint target fingerprint lists, also auxiliary positioning can be carried out using triangulation location
Mode, pass through the regional extent navigated to, wireless base station transmitting parameter (such as room inside/outside, deflection, the covering such as device attribute
Scope), the screening scope of similar grid is further reduced, the fingerprint list eventually for positioning is found out.
As shown in figure 3, the flow of the embodiment 3 of another localization method based on network fingerprinting provided for the present invention
Figure, methods described includes：
S301, gathering geographic position information；
By taking the geographical location information for gathering city as an example, the geographical position latitude and longitude information on the border in city can be gathered.
S302, based on geographical location information generate gridding information；
Using certain precision, city is divided into grid by such as 10m*10m, 20m*20m, 50m*50m equally accurate requirement
And each grid is numbered.The grid of each numbering is to a region in Yingcheng City, for determining in above-mentioned steps
Position.
S303, collection network environment information；
Network environment information can include PCI (the Physical Cell of the main plot in each net region
Identifier, i.e. Physical Cell Identifier), RSRP (the Reference Signal Receiving of main plot and adjacent cell
Power, Reference Signal Received Power), TA (Timing Advance, time advance) and data sampling frequency number of times etc., also
It may include the area attribute of correspondence grid, for example：Road, office building, residential building, market etc..
S304, based on gridding information and network environment information generation fingerprint list；
The network environment information collected is generated to the fingerprint list corresponding with gridding information.
S305, storage fingerprint list.
As shown in figure 4, the disclosed another positioning based on network fingerprinting on the basis of above-described embodiment for the present invention
The flow chart of the embodiment 4 of method, methods described includes：
S401, based on target fingerprint list generate position range information；
The corresponding positional information of target fingerprint list generation is a grid scope, actually one area information.
S402, call triangulation location；
S403, based on signal strength information, position range information and triangulation location generate positional information；
Because having determined user in a certain net region, therefore, now signal strength information is recycled to carry out triangle
Positioning, its computation amount, precision is greatly promoted, so as to orient particular location of the user in grid.
As shown in figure 5, a kind of structural representation of the embodiment 1 of the alignment system based on network fingerprinting provided for the present invention
Figure, the system includes receiving module 101, calling module 102, the first generation module 103 and the second generation module 104, wherein：
Receiving module 101 is used for the positional parameter for receiving user terminal transmission, and positional parameter includes cell information and signal is strong
Spend information；
User terminal send positional parameter can be Original CDR and MR (Measurement Report, measurement report),
Every ticket or MR contain main plot and information of adjacent cells, and corresponding RSRP (Reference Signal
Receiving Power, Reference Signal Received Power), the important positioning such as TA (Timing Advance, Timing Advance) according to
According to parameter.
Calling module 102 is used to call all fingerprint lists including cell information；
Fingerprint list is stored in corresponding special fingerprint base (memory module), one net of each fingerprint list correspondence
It may include in lattice, each fingerprint list：The PCI (Physical Cell Identifier, i.e. Physical Cell Identifier) of main plot,
The RSRP (Reference Signal Receiving Power, Reference Signal Received Power) of main plot and adjacent cell, TA (when
Between lead, time advance) and data sampling frequency number of times etc., may also include the area attribute of correspondence grid, example
Such as：Road, office building, residential building, market etc..The fingerprint list of all main plots including in positional parameter is called, for example, fixed
Include A main plots relevant information in the parameter of position, then call all fingerprint lists for including A main plots relevant information.
First generation module 103 is used to match the similar mesh of feature in all fingerprint lists based on signal strength information
Mark fingerprint list；
Include in positional parameter in the RSRP and/or TA between customer location and main plot and adjacent cell, fingerprint list
If RSRP and/or TA when including user in this grid and between main plot and adjacent cell, it is worth mentioning at this point that, fingerprint list
The RSRP and/or TA included is a value range.The fingerprint list being consistent with the signal strength information in positional parameter is found out,
Then this fingerprint list is target fingerprint list.For example：The cell information of user terminal is that main plot is A cells, and adjacent cell is B small
Area and C cells.Include the signal intensity of A cells, B cells and C cells that active user's termination is received in signal strength information,
Thus the magnitude relationship of the signal intensity of A cells, B cells and C cells in signal strength information can also be obtained.In all fingerprints
Find out identical with the magnitude relationship in signal strength information in list, and it is small to meet A cells in signal strength information, B cells and C
The fingerprint list of the intensity level in area, as target fingerprint list.
Second generation module 104 is used to generate positional information based on target fingerprint list；
Because all fingerprint lists are corresponding with grid, therefore the positional information of user can be obtained.
The technical program provides a kind of alignment system based on network fingerprinting, and the operation principle of the system is used to receive
The positional parameter that family end is sent, positional parameter includes cell information and signal strength information, calls all including cell information
Fingerprint list, based on signal strength information and the generation target fingerprint list of all fingerprint lists, based on target fingerprint list generation
Positional information.The positional parameter of the user received is compared with existing fingerprint list, the fingerprint row being most consistent are found out
Table (target fingerprint list), so as to be positioned to user, it is to avoid the influence of environment terrain and weather conditions to positioning result,
So as to improve positioning precision.
As shown in fig. 6, the structure of the embodiment 2 of another alignment system based on network fingerprinting provided for the present invention is shown
It is intended to, the system includes receiving module 201, calling module 202, the first generation module 203 and the second generation module 204, the
One generation module 203 includes the first generation unit 205 and the second generation unit 206, and the second generation module 204 includes computing unit
207 and the 3rd generation unit 208, wherein：
Receiving module 201 is used for the positional parameter for receiving user terminal transmission, and positional parameter includes cell information and signal is strong
Spend information；
User terminal send positional parameter can be Original CDR and MR (Measurement Report, measurement report),
Every ticket or MR contain main plot and information of adjacent cells, and corresponding RSRP (Reference Signal
Receiving Power, Reference Signal Received Power), the important positioning such as TA (Timing Advance, Timing Advance) according to
According to parameter.
Calling module 202 is used to call all fingerprint lists including cell information；
Fingerprint list is stored in corresponding special fingerprint base (memory module), one net of each fingerprint list correspondence
It may include in lattice, each fingerprint list：The PCI (Physical Cell Identifier, i.e. Physical Cell Identifier) of main plot,
The RSRP (Reference Signal Receiving Power, Reference Signal Received Power) of main plot and adjacent cell, TA (when
Between lead, time advance) and data sampling frequency number of times etc., may also include the area attribute of correspondence grid, example
Such as：Road, office building, residential building, market etc..The fingerprint list of all main plots including in positional parameter is called, for example, fixed
Include A main plots relevant information in the parameter of position, then call all fingerprint lists for including A main plots relevant information.
First generation unit 205 is used for based on signal intensity attenuation information and all fingerprint lists generation similar fingerprints row
Table；
Signal strength information may include signal strength values and signal intensity attenuation information, and signal intensity attenuation information can be with
The form of signal attenuation ratio equation embodies.It can be found out and the signal intensity in positional parameter based on signal attenuation ratio equation
The similar fingerprint list of fluctuation, i.e. similar fingerprints list.This step is to mention in above-mentioned technical proposal, finds signal intensity big
Small order and the signal intensity size order identical fingerprint list in signal strength information.
Second generation unit 206 is used for based on signal intensity value information and similar fingerprints list generation target fingerprint list；
Find after similar fingerprints list, signal strength values and the signal strength range in similar fingerprints list of positional parameter
Not necessarily it is consistent, rejects the similar fingerprints list not being consistent, remaining is target fingerprint list.
Computing unit 207 is used for the affinity score value for calculating all target fingerprint lists respectively based on signal strength information；
Target fingerprint list there may be multiple, can now calculate similar to positional parameter point of each target fingerprint list
Numerical value.RSRP and the net of each main adjacent base station can be calculated according to situations such as the main plot in ticket or MR, adjacent cell PCI, RSRP
Network is decayed and the information such as data similar proportion, RSRP scopes, grid property, sample frequency is sampled in fingerprint base by adding successively
The mode of power point calculates affinity score value, and computational methods can have a variety of, only give one example illustrate below：
It is k1 with each subzone network attenuation ratio full marks, is put in order according to the signal attenuation power of cell, attenuation ratio, is pressed
Error, often differs x% buttons 2^m (m is fluctuation ratio * 100/x) point.Meanwhile, with each cell signal advantage put in order such as beyond
Error range, has n different each button y points of order, amounts to y points of button (n-1) *, this minimum 0 point；
RSRP scope full marks k2, often go beyond the scope n unit, subtracts x point, has m cell button (m*n) the * x that go beyond the scope to divide,
It is same minimum 0 point；
Attribute full marks K3, during according to the working region during attribute of fingerprint, such as road, market, public arena, working, rest
Between the p1-p2 such as residential area points, the common p3-p4 in field suburb points, high mountain lake p5 points (px is the bound of fraction)；
Sampled point accounting full marks K4, general sampled point is more, represents User Activity possibility higher.Recorded according in fingerprint base
The sampled point of same period is collected, is averaged with the sampled point of adjacent all grid, the more accounting fractions of sampled point are more
It is high；Sampled point is fewer, and fraction accounting is lower.Score=(current grid sampled point/adjacent grid average sample point * K4).
Final affinity score value is the sum of aforementioned four value.
3rd generation unit 208 is used to generate positional information based on the list of affinity score value highest target fingerprint；
Based on the corresponding mess generation positional information of affinity score value highest target fingerprint list.
It is noted that if the affinity score value of multiple fingerprint lists is identical, can also by sampled point number of times probability because
Son, it is the fingerprint list eventually for positioning to select a fingerprint list.
Go out outside the above method, for multiple fingerprint target fingerprint lists, also auxiliary positioning can be carried out using triangulation location
Mode, pass through the regional extent navigated to, wireless base station transmitting parameter (such as room inside/outside, deflection, the covering such as device attribute
Scope), the screening scope of similar grid is further reduced, the fingerprint list eventually for positioning is found out.
As shown in fig. 7, the structure of the embodiment 3 of another alignment system based on network fingerprinting provided for the present invention is shown
It is intended to, system includes the first acquisition module 301, the 3rd generation module 302, the second acquisition module 303, the 4th generation module 304
And memory module 305, wherein：
First acquisition module 301 is used for gathering geographic position information；
By taking the geographical location information for gathering city as an example, the geographical position latitude and longitude information on the border in city can be gathered.
3rd generation module 302 is used to generate gridding information based on geographical location information；
Using certain precision, city is divided into grid by such as 10m*10m, 20m*20m, 50m*50m equally accurate requirement
And each grid is numbered.The grid of each numbering is to a region in Yingcheng City, for determining in above-mentioned steps
Position.
Second acquisition module 303 is used to gather network environment information；
Network environment information can include PCI (the Physical Cell of the main plot in each net region
Identifier, i.e. Physical Cell Identifier), RSRP (the Reference Signal Receiving of main plot and adjacent cell
Power, Reference Signal Received Power), TA (Timing Advance, time advance) and data sampling frequency number of times etc., also
It may include the area attribute of correspondence grid, for example：Road, office building, residential building, market etc..
4th generation module 304 is used for based on gridding information and network environment information generation fingerprint list；
The network environment information collected is generated to the fingerprint list corresponding with gridding information.
Memory module 305 is used to store fingerprint list.
As shown in figure 8, the disclosed another positioning based on network fingerprinting on the basis of above-described embodiment for the present invention
The structural representation of the embodiment 4 of system, the second generation module includes the 4th generation unit 401, call unit 402 and the 5th and given birth to
Into unit 403, wherein：
4th generation unit 401 is used to generate position range information based on target fingerprint list；
The corresponding positional information of target fingerprint list generation is a grid scope, actually one area information.
Call unit 402 is used to call triangulation location；
5th generation unit 403 is used for based on signal strength information, position range information and triangulation location generation position
Information；
Because having determined user in a certain net region, therefore, now signal strength information is recycled to carry out triangle
Positioning, its computation amount, precision is greatly promoted, so as to orient particular location of the user in grid.
The embodiment of each in this specification is described by the way of progressive, what each embodiment was stressed be with it is other
Between the difference of embodiment, each embodiment identical similar portion mutually referring to.
The foregoing description of the disclosed embodiments, enables professional and technical personnel in the field to realize or using the present invention.
A variety of modifications to these embodiments will be apparent for those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, it is of the invention
The embodiments shown herein is not intended to be limited to, and is to fit to and principles disclosed herein and features of novelty phase one
The most wide scope caused.
Claims (10)
1. a kind of localization method based on network fingerprinting, it is characterised in that methods described includes：
The positional parameter that user terminal is sent is received, the positional parameter includes cell information and signal strength information；
Call all fingerprint lists including the cell information；
The similar target fingerprint list of feature is matched in all fingerprint lists based on the signal strength information；
Positional information is generated based on the target fingerprint list.
2. the method as described in claim 1, it is characterised in that receive before the positional parameter, methods described also includes：
Gathering geographic position information；
Gridding information is generated based on the geographical location information；
Gather network environment information；
Based on the gridding information and network environment information generation fingerprint list；
Store the fingerprint list.
3. the method as described in claim 1, it is characterised in that the signal strength information include signal intensity attenuation information and
Signal intensity value information, it is described that the similar mesh of feature is matched in all fingerprint lists based on the signal strength information
Mark fingerprint list includes：
Based on the signal intensity attenuation information and all fingerprint list generation similar fingerprints lists；
The target fingerprint list is generated based on the signal intensity value information and the similar fingerprints list.
4. method as claimed in claim 3, it is characterised in that described when the quantity of the target fingerprint list is more than for the moment
Included based on target fingerprint list generation positional information：
Calculate the affinity score value of all target fingerprint lists respectively based on the signal strength information；
Positional information is generated based on target fingerprint list described in the affinity score value highest.
5. the method as described in claim any one of 1-4, it is characterised in that described that position is generated based on the target fingerprint list
Confidence breath includes：
Position range information is generated based on the target fingerprint list；
Call triangulation location；
The positional information is generated based on the signal strength information, the position range information and the triangulation location.
6. a kind of alignment system based on network fingerprinting, it is characterised in that the system includes receiving module, calling module, the
One generation module and the second generation module, wherein：
The receiving module is used for the positional parameter for receiving user terminal transmission, and the positional parameter includes cell information and signal is strong
Spend information；
The calling module is used to call all fingerprint lists including the cell information；
First generation module is used to match feature phase in all fingerprint lists based on the signal strength information
As target fingerprint list；
Second generation module is used to generate positional information based on the target fingerprint list.
7. system as claimed in claim 6, it is characterised in that the system also includes the first acquisition module, the 3rd generation mould
Block, the second acquisition module, the 4th generation module and memory module, wherein：
First acquisition module is used for gathering geographic position information；
3rd generation module is used to generate gridding information based on the geographical location information；
Second acquisition module is used to gather network environment information；
4th generation module is used for based on the gridding information and network environment information generation fingerprint list；
The memory module is used to store the fingerprint list.
8. system as claimed in claim 6, it is characterised in that the signal strength information include signal intensity attenuation information and
Signal intensity value information, first generation module includes the first generation unit and the second generation unit, wherein：
First generation unit is used for based on the signal intensity attenuation information and the similar finger of all fingerprint list generations
Line list；
Second generation unit is used to generate the target based on the signal intensity value information and the similar fingerprints list
Fingerprint list.
9. system as claimed in claim 8, it is characterised in that second generation module includes computing unit and the 3rd generation
Unit, wherein：
The computing unit is used for calculate all target fingerprint lists respectively based on the signal strength information similar point
Numerical value；
3rd generation unit is used to be based on target fingerprint list generation positional information described in the affinity score value highest.
10. the system as described in claim any one of 6-9, it is characterised in that second generation module includes the 4th generation
Unit, call unit and the 5th generation unit, wherein：
4th generation unit is used to generate position range information based on the target fingerprint list；
The call unit is used to call triangulation location；
5th generation unit is used to be based on the signal strength information, the position range information and the triangulation location
Generate the positional information.
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