CN108549049A - A kind of the Bayes's fingerprint positioning method and device of ray tracing auxiliary - Google Patents
A kind of the Bayes's fingerprint positioning method and device of ray tracing auxiliary Download PDFInfo
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- CN108549049A CN108549049A CN201810326517.8A CN201810326517A CN108549049A CN 108549049 A CN108549049 A CN 108549049A CN 201810326517 A CN201810326517 A CN 201810326517A CN 108549049 A CN108549049 A CN 108549049A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0252—Radio frequency fingerprinting
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
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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 present invention provides the Bayes's fingerprint positioning methods and device of a kind of ray tracing auxiliary.Method includes：According to the transmission power and preset ray-tracing algorithm of each wireless access points AP in area to be targeted, reference signal strengths of each AP at each reference point is calculated；The signal strength probability Distribution Model of the AP is calculated according to the composition parameter of gauss hybrid models, the reference signal strength of the AP and predetermined probabilities distributed model calculation formula of the AP got in advance at default collection point for every AP；When receiving the Location Request for carrying detection signal strengths of each AP at point to be determined of terminal transmission, point to be determined position is determined by bayesian algorithm according to the detection signal strength of the signal strength probability Distribution Model of each AP and each AP.Using the present invention, it is possible to reduce position required manpower consumption and time loss.
Description
Technical field
This application involves fields of communication technology, more particularly to a kind of Bayes's fingerprint positioning method of ray tracing auxiliary
And device.
Background technology
Indoor location service is received extensive attention and is studied because of its huge society and economic potential.It is predicted, it arrives
The market of the year two thousand twenty indoor location service is up to 10,000,000,000 dollars.Nowadays, the whole world based on global positioning system, the Big Dipper
The development of global position system technology is more ripe, is capable of providing accurate outdoor location service.But in mountain valley, urban architecture
Under close quarters and indoor environment, blocking for barrier makes the propagation of satellite positioning signal hindered, satellite positioning system
System is unable to get accurately positioning result under these circumstances.In order to solve the problems, such as the precise positioning under these scenes, Duo Zhongji
It is proposed in succession in the indoor positioning technologies of wireless signal, mainly using Wi-Fi, (Wireless Fidelity are based on IEEE
The WLAN of 802.11b standards), bluetooth, the wireless technologys such as ultra wide band.Wherein, consider technology popularization degree, locating ring
Border deployment cost and positioning accuracy, Wi-Fi and bluetooth location technology because with high-tech popularization degree and low deployment cost and
Acceptable positioning accuracy has obtained broad commercial applications.
It is positioned usually using the fingerprint matching algorithm based on received signal strength RSS in Wi-Fi and bluetooth positioning.Refer to
Line matching algorithm：Acquire RSS data at each reference point in area to be targeted, and by corresponding each of the RSS data of acquisition
With reference in point coordinates storage to fingerprint base；Server is when the positioning for carrying the RSS at point to be determined for receiving terminal transmission
When request, the RSS at point to be determined is matched with fingerprint base, obtains positioning result.Based on this positioning method, need
Setting will acquire to reference point and at each reference point a large amount of data very much in area to be targeted, take considerable time and people
Power.
Invention content
The embodiment of the present application is designed to provide a kind of the Bayes's fingerprint positioning method and device of ray tracing auxiliary,
To realize the manpower consumption and the time loss that reduce needed for positioning.Specific technical solution is as follows：
In a first aspect, a kind of Bayes's fingerprint positioning method of ray tracing auxiliary is provided, the method includes：
According to the transmission power and preset ray-tracing algorithm of each wireless access points AP in area to be targeted, calculate
Reference signal strengths of each AP in the area to be targeted at each reference point；
For every AP, joined according to the composition of gauss hybrid models of the AP got in advance at default collection point
Number, the reference signal strength of the AP and predetermined probabilities distributed model calculation formula, calculate the signal strength probability distribution mould of the AP
Type, the gauss hybrid models indicate signal strength probability distribution of the AP at the default collection point；
When the Location Request for carrying detection signal strengths of each AP at point to be determined for receiving terminal transmission
When, according to the signal strength probability Distribution Model of each AP and detection signal strengths of each AP at point to be determined, lead to
Bayesian algorithm is crossed, determines the point to be determined position.
Optionally, described to be chased after according to the transmission power of each wireless access points AP and preset ray in area to be targeted
Track algorithm calculates reference signal strengths of each AP in the area to be targeted at each reference point, including：
For every AP, according to the transmission power of the AP, the signal strength that the AP is sent out is determined；
According to corresponding distribution of obstacles data at wireless signal path loss calculation formula, each reference point, calculate
Signal strength loss of the signal that the AP is sent out in each reference point；
The signal that the signal strength that is sent out according to the AP and the AP are sent out each reference point signal strength loss, really
Fixed signal strengths of the AP at each reference point.
Optionally, the method further includes：
It obtains in preset duration, presets the sampled data of each AP signal strengths at collection point；
For every AP, according to the sampled data of the AP signal strengths of acquisition, gauss hybrid models formula and default estimate
Calculating method determines the composition parameter of gauss hybrid models of the AP at the default collection point；The gauss hybrid models table
Show signal strength probability distribution of the AP at the default collection point.
Optionally, the signal strength probability Distribution Model according to each AP and each AP are at point to be determined
Detection signal strength determines the point to be determined position by bayesian algorithm, including：
For every AP, according to the signal strength probability Distribution Model of the AP and detection signals of the AP at point to be determined
Intensity, the prior probability for the detection signal strength that the signal strength for calculating the AP at each reference point is the AP；According to shellfish
This criterion new probability formula of leaf and calculated each prior probability calculate the reference probability that each reference point is point to be determined；
According to the reference probability that each reference point is point to be determined, determine that each reference point is the general of point to be determined
Rate；
The reference point of maximum probability is determined as point to be determined.
Optionally, described according to the reference probability that each reference point is point to be determined, determine that each reference point is to wait for
The probability of anchor point, including：
For each reference point, the corresponding product with reference to probability of detection signal strength of each AP is calculated, is obtained
To result of calculation be probability that the reference point is point to be determined.
Second aspect, provides a kind of Bayes's fingerprint location device of ray tracing auxiliary, and described device includes：
First computing module, for the transmission power according to each wireless access points AP in area to be targeted, by penetrating
Line back tracking method calculates, and obtains reference signal strengths of each AP in the area to be targeted at each reference point；
Second computing module, for being directed to every AP, according to Gausses of the AP got in advance at default collection point
The composition parameter of mixed model, the reference signal strength of the AP and predetermined probabilities distributed model calculation formula, calculate the letter of the AP
Number intensive probable distributed model；The gauss hybrid models indicate signal strength probability of the AP at the default collection point point
Cloth；
Locating module, for when receive terminal transmission to carry detection signals of each AP at point to be determined strong
When the Location Request of degree, according to the signal strength probability Distribution Model of each AP and detections of each AP at point to be determined
Signal strength determines the point to be determined position by bayesian algorithm.
Optionally, first computing module, including：
First computing unit, for determining that the signal that the AP is sent out is strong according to the transmission power of the AP for every AP
Degree；
Second computing unit, for according to corresponding barrier at wireless signal path loss calculation formula, each reference point
Hinder object distributed data, calculate signal that the AP is sent out each reference point signal strength loss；
Third computing unit, the signal that signal strength and the AP for being sent out according to the AP are sent out is in each reference point
Signal strength loss, determine signal strengths of the AP at each reference point.
Optionally, described device further includes gauss hybrid models computing module, the gauss hybrid models computing module tool
Body is used for：
It obtains in preset duration, presets the sampled data of each AP signal strengths at collection point；
For every AP, according to the sampled data of the AP signal strengths of acquisition, gauss hybrid models formula and default estimate
Calculating method determines the composition parameter of gauss hybrid models of the AP at the default collection point；The gauss hybrid models table
Show signal strength probability distribution of the AP at the default collection point.
Optionally, the locating module, including：
First localization computation unit, for being directed to every AP, according to the signal strength probability Distribution Model of the AP and the AP
Detection signal strength at point to be determined, the signal strength for calculating the AP at each reference point is the detection signal of the AP
The prior probability of intensity；According to bayesian criterion new probability formula and calculated each prior probability, it is undetermined to calculate each reference point
The reference probability in site；
Second localization computation unit, for according to the reference probability that each reference point is point to be determined, determining described each
Reference point is the probability of point to be determined；
Anchor point determination unit, for the reference point of maximum probability to be determined as point to be determined.
Optionally, second localization computation unit, is specifically used for：
For each reference point, the corresponding product with reference to probability of detection signal strength of each AP is calculated, is obtained
To result of calculation be probability that the reference point is point to be determined.
A kind of Bayes's fingerprint positioning method of ray tracing auxiliary provided in an embodiment of the present invention, utilizes ray casting
Bayes's fingerprint matching positioning can be realized with low volume data acquisition statistics, the manpower consumption needed for positioning can be efficiently reduced
With time loss.
Certainly, implementing any product of the application or method must be not necessarily required to reach all the above excellent simultaneously
Point.
Description of the drawings
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of application for those of ordinary skill in the art without creative efforts, can be with
Obtain other attached drawings according to these attached drawings.
Fig. 1 is a kind of Bayes's fingerprint positioning method flow chart of ray tracing auxiliary provided in an embodiment of the present invention；
Fig. 2 is the side of reference signal strength at each reference point in a kind of determining area to be targeted provided in an embodiment of the present invention
Method flow chart；
Fig. 3 is a kind of composition of gauss hybrid models of each AP of acquisition provided in an embodiment of the present invention at default collection point
The method flow diagram of parameter；
Fig. 4 is a kind of method flow diagram of determining locating point position provided in an embodiment of the present invention；
Fig. 5 is a kind of Bayes's fingerprint location apparatus structure signal of ray tracing auxiliary provided in an embodiment of the present invention
Figure；
Fig. 6 is a kind of structural schematic diagram of server provided in an embodiment of the present invention.
Specific implementation mode
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.It is based on
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall in the protection scope of this application.
The embodiment of the present invention additionally provides a kind of Bayes's fingerprint positioning method of ray tracing auxiliary, and this method is applied to
Server.In practical applications, multiple routers would generally be arranged in location service provider indoors, that is, provide multiple access points
AP.Server is directed to every AP, the composition of gauss hybrid models that can be according to the AP got in advance at default collection point
Parameter, the reference signal strength of the AP and predetermined probabilities distributed model calculation formula, calculate the signal strength probability distribution of the AP
Model.Wherein, the acquisition modes of the composition parameter of gauss hybrid models of each AP at default collection point are：Terminal indoors one
The signal strength of each AP is sampled at collection point in preset time period；Server obtains the signal strength data of terminal sampling, needle
To every AP, server is calculated according to the sampled data of the AP signal strengths of acquisition, gauss hybrid models formula and default estimation
Method determines the composition parameter of gauss hybrid models of the AP at default collection point.Wherein, gauss hybrid models indicate that the AP exists
Signal strength probability distribution at default collection point.When terminal needs positioning, terminal to server transmission carries described each
The Location Request of detection signal strengths of the AP at point to be determined.When each AP that carries that server receives terminal transmission is being waited for
When the Location Request of the detection signal strength at anchor point, signal strength probability Distribution Model and each AP of the server according to each AP
Detection signal strength at point to be determined determines point to be determined position by bayesian algorithm.
As shown in Figure 1, this method may comprise steps of：
Step 101, according to the transmission power of each wireless access points AP and preset ray tracing in area to be targeted
Algorithm calculates reference signal strengths of each AP in area to be targeted at each reference point.
In force, can be using the positions AP as origin for every AP, and the signal that AP is sent out is considered as
The a plurality of ray signal projected from origin when every ray signal passes through barrier, has part signal and penetrates barrier and portion
Sub-signal is reflected by barrier, and the direction of transmission and indirect ray signal, and ray signal can be obtained according to minute surface principle
By signal strength loss can be made when barrier.
The rate of signal attenuation that ray signal is equal to barrier through the signal strength loss of barrier is multiplied by barrier thickness,
Wherein, the rate of signal attenuation of barrier can be calculated by formula (1).
Wherein, the rate of signal attenuation of A barriers, σ are the capacitivity of barrier, εrIt is the relative dielectric constant of barrier.
When the reflectance factor that signal strength loss of the ray signal after barrier reflects is equal to barrier is multiplied by incidence
Signal strength, the wherein reflectance factor of barrier can be obtained by formula (2), formula (3) and formula (4).
Wherein, η is the complex dielectric permittivity of barrier, the angle of the incident direction and blocking surfaces normal vector of θ line signals,
RCFor the reflectance factor of barrier.
Based on the above, for every AP, server obtains the positions AP, further according to the distributed intelligence of barrier, really
The barrier that the ray signal that the fixed AP is sent out is passed through when reaching at each reference point is respectively penetrated according to the determination of the transmission power of the AP
The signal strength of line signal, and it is the capacitivity of the barrier that is passed through of while being reached at each reference point according to each ray signal, opposite
Dielectric constant, dielectric functions calculate each ray signal and reach signal strength loss at each reference point, will reach each reference point
Ray signal transmitting when signal strength subtraction signal loss of intensity, obtain the reference signal strength of each reference point.
Optionally, ray is passed through according to the transmission power of each wireless access points AP in area to be targeted referring to Fig. 2
Back tracking method calculates, and obtains reference signal strengths of each AP in area to be targeted at each reference point, specific processing step is as follows：
Step 201, for every AP, according to the transmission power of the AP, the signal strength that the AP is sent out is determined.
In force, for every AP, server obtains the positions AP, according to the transmission power of the AP determine the AP from this
The signal strength for the ray signal that the positions AP are sent out.
Step 202, according to corresponding distribution of obstacles data at wireless signal path loss calculation formula, each reference point,
Calculate signal that the AP is sent out each reference point signal strength loss.
In force, for every AP, server determines the ray signal that the AP is sent out according to the distributed data of barrier
The barrier that the middle ray signal reached at each reference point and the ray signal pass through, believes according to the ray reached at each reference point
Number signal strength and reach the capacitivity for the barrier that the ray signal at each reference point passes through, relative dielectric constant, it is multiple to be situated between
It counts by means of emails or letters, by above-mentioned formula (1)~(4), calculates ray signal and reach the signal strength loss at each reference point.
Step 203, the signal that the signal strength and the AP sent out according to the AP is sent out is damaged in the signal strength of each reference point
Consumption, determines signal strengths of the AP at each reference point.
In force, signal strength when server emits the ray signal for reaching each reference point subtracts the signal of ray
Loss of intensity obtains the reference signal strength of each reference point.
Step 102, for every AP, according to gauss hybrid models of the AP got in advance at default collection point
Composition parameter, the reference signal strength of the AP and predetermined probabilities distributed model calculation formula, calculate the signal strength probability of the AP
Distributed model.
Wherein, gauss hybrid models indicate signal strength probability distribution of the AP at default collection point.
In force, for every AP, server is mixed according to Gausses of the AP got in advance at default collection point
At the Gauss model number of molding type, the weight of each Gauss model, the mean value of each Gauss model and variance, each reference point
Reference signal strength, the signal intensity profile model of the AP is obtained by formula (5).
Wherein, RSS0For reference signal strengths of the AP at reference point (x, y), Q is the number of Gauss model, σmax 2For
The variance yields of the maximum Gauss model of weight, αmaxFor the weighted value of the maximum Gauss model of weight, q is except the maximum height of weight
Gauss model other than this model, σq 2For the variance yields of Gauss model q, αqFor the weighted value of Gauss model q, μqFor Gauss model
The desired value of q, P (RSS | (x, y)) are the signal intensity profile model of the AP, dqFor μmaxWith the difference of the mean value of Gauss model q
Value, μmaxFor the maximum Gauss model desired value of weight.
Optionally, referring to Fig. 3, the composition parameter of gauss hybrid models of each AP at default collection point is obtained, it is specific to locate
Steps are as follows for reason：
Step 301, it obtains in preset duration, presets the sampled data of each AP signal strengths at collection point.
In force, for every AP, it is strong to obtain one group of signal in the signal strength of the default acquisition point sampling AP for terminal
Degrees of data, such as { RSS1,…,RSSN}。
Step 302, for every AP, according to the sampled data of the AP signal strengths of acquisition, gauss hybrid models formula
With default algorithm for estimating, the composition parameter of gauss hybrid models of the AP at default collection point is determined；Gauss hybrid models table
Show signal strength probability distribution of the AP at default collection point.
In force, server obtains the signal strength data of terminal sampling, is obtained with gauss hybrid models formula (6) fitting
The signal strength data taken estimates the composition parameter of gauss hybrid models formula (6).
Wherein, Q is the number of Gauss model, σqFor the standard deviation of Gauss model q, αqFor the weighted value of Gauss model q, μq
For the desired value of Gauss model q, P (RSS) is gauss hybrid models of the AP in default collection point.
Step 103, when the positioning for carrying detection signal strengths of each AP at point to be determined for receiving terminal transmission
When request, according to the signal strength probability Distribution Model of each AP and detection signal strengths of each AP at point to be determined, pass through shellfish
This algorithm of leaf, determines point to be determined position.
In force, when server receive terminal transmission carry detection signal strengths of each AP at point to be determined
Location Request when, for every AP, server is according to the signal strength probability Distribution Model of the AP with the AP in point to be determined
The detection signal strength at place, the probability for the detection signal strength that the signal strength for calculating the AP at each reference point is the AP, will count
The probability of calculation is as prior probability, and then it is to be positioned to calculate each reference point according to prior probability and bayesian criterion calculation formula
The probability of point.Then, server is determined according to the probability that the corresponding each reference point of detection signal strength of each AP is point to be determined
Each reference point is the probability of point to be determined, using the reference point of maximum probability as point to be determined.
Optionally, referring to Fig. 4, according to the signal strength probability Distribution Model of each AP and detections of each AP at point to be determined
Signal strength determines that the point to be determined position, specific processing step are as follows by bayesian algorithm：
Step 401, for every AP, according to the signal strength probability Distribution Model of the AP and the AP at point to be determined
Signal strength is detected, the prior probability for the detection signal strength that the signal strength for calculating the AP at each reference point is the AP；Root
According to bayesian criterion new probability formula and calculated each prior probability, the reference probability that each reference point is point to be determined is calculated.
In force, for every AP, server is according to the signal strength probability Distribution Model of the AP with the AP undetermined
Detection signal strength at site, the priori for the detection signal strength that the signal strength for calculating the AP at each reference point is the AP
Probability, the corresponding each reference point of detection signal strength that the AP is calculated by formula (7) is the reference probability of point to be determined.
Wherein, S is area to be targeted, (xr,yr) it is reference point, RSSiFor APiDetection signal at point to be determined is strong
Degree, P ((xr,yr)) it is reference point (xr,yr) be point to be determined reference probability.
Wherein, in no position history data,
Wherein, M is reference point number.
When there is position history data,
Wherein, N is to pass through (x in position history datar-1,yr-1) point total number of tracks amount, n be pass through (xr-1,yr-1) point
Next anchor point is (x afterwardsr,yr) tracking quantity.
Step 402, according to the probability that the corresponding each reference point of the detection signal strength of each AP is point to be determined, each ginseng is determined
Examination point is the probability of point to be determined.
Optionally, according to the reference probability that each reference point is point to be determined, determine that each reference point is the probability of point to be determined
It is specific processing can be：For each reference point, detection signal strength corresponding the multiplying with reference to probability of each AP is calculated
Product, obtained result of calculation is the probability that the reference point is point to be determined.
Optionally, according to the reference probability that each reference point is point to be determined, determine that each reference point is the probability of point to be determined
Specific processing can be specific processing can be：For each reference point, the corresponding institute of detection signal strength of each AP is calculated
The sum with reference to probability is stated, using obtained result of calculation as the probability that the reference point is point to be determined.
Step 403, the reference point of maximum probability is determined as point to be determined.
In this way, a kind of Bayes's fingerprint positioning method of ray tracing auxiliary provided in an embodiment of the present invention, utilizes ray
Bayes's fingerprint matching positioning can be realized with low volume data acquisition statistics in back tracking method, can efficiently reduce the people needed for positioning
Power consumes and time loss.
Based on the same technical idea, embodiment of the method shown in Fig. 1 is corresponded to, the embodiment of the present invention additionally provides one kind and penetrates
Bayes's fingerprint location device of line tracking auxiliary, as shown in figure 5, the device includes：
First computing module 501 passes through for the transmission power according to each wireless access points AP in area to be targeted
Ray casting calculates, and obtains reference signal strengths of each AP in the area to be targeted at each reference point；
Second computing module 502, for being directed to every AP, according to height of the AP got in advance at default collection point
The composition parameter of this mixed model, the reference signal strength of the AP and predetermined probabilities distributed model calculation formula, calculate the AP's
Signal strength probability Distribution Model；
Wherein, gauss hybrid models indicate signal strength probability distribution of the AP at default collection point；
Locating module 503, for when receive terminal transmission to carry detection signals of each AP at point to be determined strong
It is strong according to the signal strength probability Distribution Model of each AP and detection signals of each AP at point to be determined when the Location Request of degree
Degree, by bayesian algorithm, determines point to be determined position.
Optionally, the first computing module 501, including：
First computing unit, for determining that the signal that the AP is sent out is strong according to the transmission power of the AP for every AP
Degree；
Second computing unit, for according to corresponding barrier at wireless signal path loss calculation formula, each reference point
Distributed data, calculate signal that the AP is sent out each reference point signal strength loss；
Third computing unit, letter of the signal that signal strength and the AP for being sent out according to the AP are sent out in each reference point
Number loss of intensity, determines signal strengths of the AP at each reference point.
Optionally, device further includes gauss hybrid models computing module, and gauss hybrid models computing module is specifically used for：
It obtains in preset duration, presets the sampled data of each AP signal strengths at collection point；
For every AP, according to the sampled data of the AP signal strengths of acquisition, gauss hybrid models formula and default estimate
Calculating method determines the composition parameter of gauss hybrid models of the AP at default collection point.
Wherein, gauss hybrid models indicate signal strength probability distribution of the AP at the default collection point.
Optionally, locating module 503, including：
First localization computation unit, for being directed to every AP, according to the signal strength probability Distribution Model of the AP and the AP
Detection signal strength at point to be determined, the signal strength for calculating the AP at each reference point is the detection signal strength of the AP
Prior probability；According to bayesian criterion new probability formula and calculated each prior probability, it is point to be determined to calculate each reference point
Reference probability；
Second localization computation unit, for according to the reference probability that each reference point is point to be determined, determining that each reference point is
The probability of point to be determined；
Anchor point determination unit, for the reference point of maximum probability to be determined as point to be determined.
Optionally, the second localization computation unit is specifically used for：
For each reference point, the corresponding product with reference to probability of detection signal strength of each AP, obtained calculating are calculated
As a result it is the probability that the reference point is point to be determined.
The embodiment of the present invention additionally provides a kind of server, as shown in fig. 6, including processor 601, communication interface 602, depositing
Reservoir 603 and communication bus 604, wherein processor 601, communication interface 602, memory 603 are completed by communication bus 604
Mutual communication；
Memory 603, for storing computer program；
Processor 601, when for executing the program stored on memory 603, so that the node device executes following step
Suddenly, which includes：
According to the transmission power and preset ray-tracing algorithm of each wireless access points AP in area to be targeted, calculate
Reference signal strengths of each AP in area to be targeted at each reference point；
For every AP, joined according to the composition of gauss hybrid models of the AP got in advance at default collection point
Number, the reference signal strength of the AP and predetermined probabilities distributed model calculation formula, calculate the signal strength probability distribution mould of the AP
Type；
Wherein, gauss hybrid models indicate signal strength probability distribution of the AP at the default collection point；
When receiving the Location Request for carrying detection signal strengths of each AP at point to be determined of terminal transmission, root
According to signal strength probability Distribution Model and detection signal strengths of each AP at point to be determined of each AP, by bayesian algorithm,
Determine point to be determined position.
Optionally, it is calculated according to the transmission power of each wireless access points AP in area to be targeted and preset ray tracing
Method calculates reference signal strengths of each AP in the area to be targeted at each reference point, including：
For every AP, according to the transmission power of the AP, the signal strength that the AP is sent out is determined；
According to corresponding distribution of obstacles data at wireless signal path loss calculation formula, each reference point, the AP is calculated
Signal strength loss of the signal sent out in each reference point；
For the signal that the signal strength and the AP sent out according to the AP is sent out in the signal strength loss of each reference point, determining should
Signal strengths of the AP at each reference point.
Optionally, further include：
It obtains in preset duration, presets the sampled data of each AP signal strengths at collection point；
For every AP, according to the sampled data of the AP signal strengths of acquisition, gauss hybrid models formula and default estimate
Calculating method determines the composition parameter of gauss hybrid models of the AP at default collection point；
Wherein, gauss hybrid models indicate signal strength probability distribution of the AP at the default collection point.
Optionally, strong according to the signal strength probability Distribution Model of each AP and detection signals of each AP at point to be determined
Degree, by bayesian algorithm, determines point to be determined position, including：
For every AP, according to the signal strength probability Distribution Model of the AP and detection signals of the AP at point to be determined
Intensity, the prior probability for the detection signal strength that the signal strength for calculating the AP at each reference point is the AP；According to Bayes
Criterion new probability formula and calculated each prior probability calculate the reference probability that each reference point is point to be determined；
According to the reference probability that each reference point is point to be determined, determine that each reference point is the probability of point to be determined；
The reference point of maximum probability is determined as point to be determined.
Optionally, according to the reference probability that each reference point is point to be determined, determine that each reference point is the probability of point to be determined,
Including：
For each reference point, the corresponding product with reference to probability of detection signal strength of each AP, obtained calculating are calculated
As a result it is the probability that the reference point is point to be determined.
Machine readable storage medium may include RAM (Random Access Memory, random access memory), also may be used
To include NVM (Non-Volatile Memory, nonvolatile memory), for example, at least a magnetic disk storage.In addition, machine
Device readable storage medium storing program for executing can also be at least one storage device for being located remotely from aforementioned processor.
Above-mentioned processor can be general processor, including CPU (Central Processing Unit, central processing
Device), NP (Network Processor, network processing unit) etc.；Can also be DSP (Digital Signal Processing,
Digital signal processor), ASIC (Application Specific Integrated Circuit, application-specific integrated circuit),
FPGA (Field-Programmable Gate Array, field programmable gate array) or other programmable logic device are divided
Vertical door or transistor logic, discrete hardware components.
It should be noted that herein, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also include other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
Each embodiment in this specification is all made of relevant mode and describes, identical similar portion between each embodiment
Point just to refer each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality
For applying example, since it is substantially similar to the method embodiment, so description is fairly simple, related place is referring to embodiment of the method
Part explanation.
The foregoing is merely the preferred embodiments of the application, are not intended to limit the protection domain of the application.It is all
Any modification, equivalent replacement, improvement and so within spirit herein and principle are all contained in the protection domain of the application
It is interior.
Claims (10)
1. a kind of Bayes's fingerprint positioning method of ray tracing auxiliary, which is characterized in that the method includes：
According to the transmission power and preset ray-tracing algorithm of each wireless access points AP in area to be targeted, described in calculating
Reference signal strengths of each AP in the area to be targeted at each reference point；
For every AP, according to the composition parameter of gauss hybrid models of the AP got in advance at default collection point, it is somebody's turn to do
The reference signal strength and predetermined probabilities distributed model calculation formula of AP, calculates the signal strength probability Distribution Model of the AP, institute
It states gauss hybrid models and indicates signal strength probability distribution of the AP at the default collection point；
When receiving the Location Request for carrying detection signal strengths of each AP at point to be determined of terminal transmission, root
According to the signal strength probability Distribution Model and detection signal strengths of each AP at point to be determined of each AP, pass through pattra leaves
This algorithm determines the point to be determined position.
2. according to the method described in claim 1, it is characterized in that, described according to each wireless access points in area to be targeted
The transmission power of AP and preset ray-tracing algorithm calculate ginsengs of each AP in the area to be targeted at each reference point
Signal strength is examined, including：
For every AP, according to the transmission power of the AP, the signal strength that the AP is sent out is determined；
According to corresponding distribution of obstacles data at wireless signal path loss calculation formula, each reference point, the AP is calculated
Signal strength loss of the signal sent out in each reference point；
For the signal that the signal strength and the AP sent out according to the AP is sent out in the signal strength loss of each reference point, determining should
Signal strengths of the AP at each reference point.
3. according to the method described in claim 1, it is characterized in that, the method further includes：
It obtains in preset duration, presets the sampled data of each AP signal strengths at collection point；
For every AP, calculated according to the sampled data of the AP signal strengths of acquisition, gauss hybrid models formula and default estimation
Method determines the composition parameter of gauss hybrid models of the AP at the default collection point；The gauss hybrid models indicate should
Signal strength probability distribution of the AP at the default collection point.
4. according to the method described in claim 1, it is characterized in that, the signal strength probability distribution mould according to each AP
Type determines the point to be determined position with detection signal strengths of each AP at point to be determined by bayesian algorithm, wraps
It includes：
It is strong according to the signal strength probability Distribution Model of the AP and detection signals of the AP at point to be determined for every AP
Degree, the prior probability for the detection signal strength that the signal strength for calculating the AP at each reference point is the AP；According to pattra leaves
This criterion new probability formula and calculated each prior probability calculate the reference probability that each reference point is point to be determined；
According to the reference probability that each reference point is point to be determined, determine that each reference point is the probability of point to be determined；
The reference point of maximum probability is determined as point to be determined.
5. according to the method described in claim 4, it is characterized in that, described according to the reference that each reference point is point to be determined
Probability determines that each reference point is the probability of point to be determined, including：
For each reference point, the corresponding product with reference to probability of detection signal strength of each AP is calculated, is obtained
Result of calculation is the probability that the reference point is point to be determined.
6. a kind of Bayes's fingerprint location device of ray tracing auxiliary, which is characterized in that described device includes：
First computing module is chased after for the transmission power according to each wireless access points AP in area to be targeted by ray
Track method calculates, and obtains reference signal strengths of each AP in the area to be targeted at each reference point；
Second computing module, for being directed to every AP, according to Gaussian Mixtures of the AP got in advance at default collection point
The composition parameter of model, the reference signal strength of the AP and predetermined probabilities distributed model calculation formula, the signal for calculating the AP are strong
Spend probability Distribution Model；The gauss hybrid models indicate signal strength probability distribution of the AP at the default collection point；
Locating module, for carrying detection signal strengths of each AP at point to be determined when receive terminal transmission
When Location Request, according to the signal strength probability Distribution Model of each AP and detection signals of each AP at point to be determined
Intensity determines the point to be determined position by bayesian algorithm.
7. device according to claim 6, which is characterized in that first computing module, including：
First computing unit, for determining the signal strength that the AP is sent out according to the transmission power of the AP for every AP；
Second computing unit, for according to corresponding barrier at wireless signal path loss calculation formula, each reference point
Distributed data, calculate signal that the AP is sent out each reference point signal strength loss；
Third computing unit, letter of the signal that signal strength and the AP for being sent out according to the AP are sent out in each reference point
Number loss of intensity, determines signal strengths of the AP at each reference point.
8. device according to claim 6, which is characterized in that described device further includes gauss hybrid models computing module, institute
Gauss hybrid models computing module is stated to be specifically used for：
It obtains in preset duration, presets the sampled data of each AP signal strengths at collection point；
For every AP, calculated according to the sampled data of the AP signal strengths of acquisition, gauss hybrid models formula and default estimation
Method determines the composition parameter of gauss hybrid models of the AP at the default collection point；The gauss hybrid models indicate should
Signal strength probability distribution of the AP at the default collection point.
9. device according to claim 6, which is characterized in that the locating module, including：
First localization computation unit is being waited for according to the signal strength probability Distribution Model of the AP with the AP for being directed to every AP
Detection signal strength at anchor point, the signal strength for calculating the AP at each reference point is the detection signal strength of the AP
Prior probability；According to bayesian criterion new probability formula and calculated each prior probability, it is point to be determined to calculate each reference point
Reference probability；
Second localization computation unit, for according to the reference probability that each reference point is point to be determined, determining each reference
Point is the probability of point to be determined；
Anchor point determination unit, for the reference point of maximum probability to be determined as point to be determined.
10. device according to claim 9, which is characterized in that second localization computation unit is specifically used for：
For each reference point, the corresponding product with reference to probability of detection signal strength of each AP is calculated, is obtained
Result of calculation is the probability that the reference point is point to be determined.
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