CN109581285A - A kind of fingerprinting localization algorithm based on the filtering of motor behavior discrete data - Google Patents
A kind of fingerprinting localization algorithm based on the filtering of motor behavior discrete data Download PDFInfo
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- CN109581285A CN109581285A CN201811527714.2A CN201811527714A CN109581285A CN 109581285 A CN109581285 A CN 109581285A CN 201811527714 A CN201811527714 A CN 201811527714A CN 109581285 A CN109581285 A CN 109581285A
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- discrete data
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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
Abstract
The invention discloses a kind of fingerprinting localization algorithms based on the filtering of motor behavior discrete data, comprising: wireless signal transmitter position mark is layouted, and the specific coordinate data of each wireless signal launch point is obtained；It is calculated to form the relevant atom fingerprint base of map vector according to the radiation patterns of wireless signal；The first user is positioned using atom fingerprint base, computed user locations；The filtering of motor behavior discrete data is carried out to the location information of generation, generates atom deviation factor；Deviation factor is accumulated according to user movement, forms new fingerprint cluster；User is positioned using fingerprint cluster, while being updated maintenance to fingerprint cluster after generating location information filtering.Using the atom fingerprint base of wireless signal model, motor behavior discrete data filter, and the method for big data and artificial intelligence more newly increases fingerprint base, and generate fingerprint cluster, each user is several acquisition persons, after filtering by behavior, forms new big data, new fingerprint base is formed after big data convergence, and updates iteration fingerprint base.
Description
Technical field
The invention belongs to bluetooth indoor positioning technologies fields, specifically, being related to a kind of based on motor behavior discrete data
The fingerprinting localization algorithm of filtering.
Background technique
Fingerprint location is generally used in the not strong interior of the signals of communication such as underground parking, basement.It is existing to utilize wifi
Or low-power consumption bluetooth using fingerprint algorithm carry out indoor scene positioning when, specific method step are as follows: 1, scene draw number arrow
Quantity map；2, bluetooth is layouted installation；3, using map vector, data are acquired by expert data acquisition person；4, working as by acquisition
Preceding data generate fingerprint base；5, it positions.In existing scheme, when data acquisition person acquires data, need to acquire number at the uniform velocity gait
According to so being typically chosen the less scene of flow of the people.And radio signal and flow of the people, battery capacity or even air humidity are all
Deposit stronger correlation.
Summary of the invention
For deficiency above-mentioned in the prior art, the present invention provides a kind of fingerprint based on the filtering of motor behavior discrete data
Location algorithm, using the atom fingerprint base of wireless signal model, motor behavior discrete data filter, and using big data and
The method of artificial intelligence more newly increases fingerprint base, and generates the mechanism of fingerprint cluster, and each user is data acquisition person, passes through row
After filtering, new big data is formed, forms new fingerprint base after big data convergence, and iteration is updated by correlation mechanism
Fingerprint base.When correlation is lower, new fingerprint base is formed, at this point, fingerprint base becomes fingerprint cluster.To cover, the stream of people
Amount variation, the factors such as weather conditions.Fingerprint base is updated, it is more efficient.
In order to achieve the above object, the solution that the present invention uses is: one kind is filtered based on motor behavior discrete data
Fingerprinting localization algorithm, include the following steps:
S1: wireless signal transmitter position mark is layouted, and the specific coordinate data of each wireless signal launch point is obtained；
S2: according to the radiation patterns of wireless signalCalculating forms vector
The relevant atom fingerprint base of map；
S3: the first user is positioned using atom fingerprint base, computed user locations；
S4: the filtering of motor behavior discrete data is carried out to the location information of generation, generates atom deviation factor；
S5: deviation factor is accumulated according to user movement, forms new fingerprint cluster；
S6: user is positioned using fingerprint cluster, while being updated maintenance to fingerprint cluster after generating location information filtering；
Wherein p (r) is radiant power, and a is constant, and a=1.2, p0 are wireless signal transmission power, and z is parameter,E is natural constant, and m is parameter,ε is the opposite of air
Dielectric constant, ε=1, r are distance, and P is constant, p=0.85.
Further, in the step S2, according to layer, high, user's height and distance calculate distance r, by distance
R and wireless signal transmission power substitute into the radiation patterns of wireless signal, calculate p (r), form atom fingerprint base.
Further, the user location, which calculates, includes:
Using the two-dimensional coordinate of the strongest wireless signal transmitter installation site of the signal received as polar origin；
Probability of the locatee on each point (ri, θ i) on the polar coordinates is calculated, maximum probability position is obtained,
P (ri, θ i)=pi0×pi1×pi2+ ...,
Wherein p (ri, θ i) is the probability for being located in each point on polar coordinates, pi0For probability of this in polar coordinate system,
pi1For second wireless signal transmitter position target distance of the distance and its corresponding probability of signal strength, pi2For this point away from
With a distance from the target of third wireless signal transmitter position and its corresponding probability of signal strength；
The probability of each point of assortment from big to small selects first three to be used as optimal location p (r1, θ 1), p (r2, θ 2), p (r3, θ
3) polar coordinates, are converted into position coordinates.
Further, the motor behavior discrete data, which filters, includes:
If the direction of motion be D, speed V,
First, it is assumed that the error in direction is ± π/6, error ± 25% of speed, it is assumed that last accurate positioning, according to upper
A region is calculated in primary position, and the time interval positioned twice, if current location in this error band,
Then Ps=0.5；
If current location exceeds above-mentioned zone, error is set again as ± π/3, error ± 50% of speed, it is assumed that upper one
Secondary accurate positioning, similarly calculate to Two Areas, if current location in this region, Ps=0.4；
If current location exceeds Two Areas, Ps=0.1；
Three optimal points are weighted according to the probability of current location region: pi=p (ri, θ i) × Ps.
Further, the new fingerprint cluster of the formation includes: the point to strongest signal strength, according to positioning result, shape
At (di,θi,rssiij) table, calculate each anchor point (di,θi) corresponding rssiijMean value EiAnd variances sigmai, by mean value Ei
And variances sigmaiIt is saved as fingerprint cluster, wherein [1,100000] j ∈.
Further, the described fingerprint cluster positioning includes: and sets the distribution of the rssi that location point (d, θ) receives to meet normal state point
Cloth, then
The probability of each (d, θ) point is p=f (rssi1) × f (rssi2) × f (rssi3) × Ps, probability in polar coordinates at this time
Maximum point is current location.
Further, the wireless signal transmitter is 2.4GHz signal projector.
Further, the wireless signal transmitter includes bluetooth transmitters, wifi transmitter and ZigBee transmitter
In any one.
The beneficial effects of the present invention are:
(1) the atom fingerprint base of wireless signal model, motor behavior discrete data filter are used, and uses big data
And the method for artificial intelligence more newly increases fingerprint base, and generates the mechanism of fingerprint cluster, each user is data acquisition person, is passed through
After behavior filtering, new big data is formed, forms new fingerprint base after big data convergence, and update repeatedly by correlation mechanism
For fingerprint base.When correlation is lower, new fingerprint base is formed, at this point, fingerprint base becomes fingerprint cluster.To cover, people
Changes in flow rate, the factors such as weather conditions.Fingerprint base is updated, it is more efficient.
(2) it is layouted using 2.4GHz signal projector as position mark, meets the demand of layouting of user.
Detailed description of the invention
Fig. 1 is location algorithm method flow diagram of the present invention；
Fig. 2 is location probability schematic diagram of the present invention.
Specific embodiment
Below in conjunction with attached drawing, the invention will be further described:
As shown in Figure 1, a kind of fingerprinting localization algorithm based on the filtering of motor behavior discrete data, includes the following steps:
S1: wireless signal transmitter position mark is layouted, and the specific coordinate data of each wireless signal launch point is obtained；
S2: according to the radiation patterns of wireless signalCalculating forms vector
The relevant atom fingerprint base of map；
S3: the first user is positioned using atom fingerprint base, computed user locations；
S4: the filtering of motor behavior discrete data is carried out to the location information of generation, generates atom deviation factor；
S5: deviation factor is accumulated according to user movement, forms new fingerprint cluster；
S6: user is positioned using fingerprint cluster, while being updated maintenance to fingerprint cluster after generating location information filtering；
Wherein p (r) is radiant power, and a is constant, and a=1.2, p0 are wireless signal transmission power, and z is parameter,E is natural constant, and m is parameter,ε is the opposite of air
Dielectric constant, ε=1, r are distance, and P is constant, p=0.85.
Further, in the step S2, according to layer, high, user's height and distance calculate distance r, by distance
R and wireless signal transmission power substitute into the radiation patterns of wireless signal, calculate p (r), form atom fingerprint base.According to building
Internal layer height is built, wireless signal transmission power p0 obtains 5 sections, this 5 sections are with launch point underface for the center of circle, with 1
Rice, 2 meters, 3 meters, 4 meters, 5 meters, 6.5 meters, 8 meters of concentric circles for radius form initial dimension.As shown in table 1,
Table 1
Apart from section, corresponding signal intensity profile probability satisfaction is just being distributed very much, and table 2 is a height of 2 meters of layer, wireless signal hair
Penetrate probability distribution table when power is -40dBm.
Table 2
Table 3 is a height of 2.5 meters of layer, probability distribution table when wireless signal transmission power is -40dBm.
Table 3
Table 4 is a height of 3 meters of layer, probability distribution table when wireless signal transmission power is -40dBm.
Table 4
Table 5 is a height of 4 meters of layer, probability distribution table when wireless signal transmission power is -40dBm.
Table 5
Further, the user location, which calculates, includes:
Using the two-dimensional coordinate of the strongest wireless signal transmitter installation site of the signal received as polar origin；
Probability of the locatee on each point (ri, θ i) on the polar coordinates is calculated, maximum probability position is obtained,
P (ri, θ i)=pi0×pi1×pi2+ ...,
Wherein p (ri, θ i) is the probability for being located in each point on polar coordinates, pi0For probability of this in polar coordinate system,
pi1For second wireless signal transmitter position target distance of the distance and its corresponding probability of signal strength, pi2For this point away from
With a distance from the target of third wireless signal transmitter position and its corresponding probability of signal strength；
The probability of each point of assortment from big to small selects first three to be used as optimal location p (r1, θ 1), p (r2, θ 2), p (r3, θ
3) polar coordinates, are converted into position coordinates.
When calculating, probability is denoted as 0.01 for 0.In practical applications, the 4th wireless signal transmitter position target signal
Fainter, reference significance is smaller, thus only selects 3 points as reference.
As shown in Fig. 2, position is the probability of black region in figure in primary positioning are as follows:
P=p0 (r, rssi_A0) × p0 (d1, rssi_A1) × p0 (d2, rssi_A2).
Further, the motor behavior discrete data, which filters, includes:
If the direction of motion be D, speed V,
First, it is assumed that the error in direction is ± π/6, error ± 25% of speed, it is assumed that last accurate positioning, according to upper
A region is calculated in primary position, and the time interval positioned twice, if current location in this error band,
Then Ps=0.5；
If current location exceeds above-mentioned zone, error is set again as ± π/3, error ± 50% of speed, it is assumed that upper one
Secondary accurate positioning, similarly calculate to Two Areas, if current location in this region, Ps=0.4；
If current location exceeds Two Areas, Ps=0.1；
Three optimal points are weighted according to the probability of current location region: pi=p (ri, θ i) × Ps.
Further, the new fingerprint cluster of the formation includes: the point to strongest signal strength, according to positioning result, shape
At (di,θi,rssiij) table, calculate each anchor point (di,θi) corresponding rssiijMean value EiAnd variances sigmai, by mean value Ei
And variances sigmaiIt is saved as fingerprint cluster, wherein [1,100000] j ∈.
Point (d centered on each launch pointi,θi), it is located in the rssi of the pointijValue reaches 100000 data
When, calculate rssii1, rssii2... rssii100000Mean value EiAnd variances sigmai, by mean value EiAnd variances sigmaiAs fingerprint cluster
It saves.
Further, the described fingerprint cluster positioning includes: and sets the distribution of the rssi that location point (d, θ) receives to meet normal state point
Cloth, then
The probability of each (d, θ) point is p=f (rssi1) × f (rssi2) × f (rssi3) × Ps, probability in polar coordinates at this time
Maximum point is current location.
Further, the wireless signal transmitter is 2.4GHz signal projector.
Further, the wireless signal transmitter includes bluetooth transmitters, wifi transmitter and ZigBee transmitter
In any one.
A specific embodiment of the invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.
Claims (8)
1. a kind of fingerprinting localization algorithm based on the filtering of motor behavior discrete data, characterized by the following steps:
S1: wireless signal transmitter position mark is layouted, and the specific coordinate data of each wireless signal launch point is obtained；
S2: according to the radiation patterns of wireless signalCalculating forms map vector phase
The atom fingerprint base of pass；
S3: the first user is positioned using atom fingerprint base, computed user locations；
S4: the filtering of motor behavior discrete data is carried out to the location information of generation, generates atom deviation factor；
S5: deviation factor is accumulated according to user movement, forms new fingerprint cluster；
S6: user is positioned using fingerprint cluster, while being updated maintenance to fingerprint cluster after generating location information filtering；
Wherein p (r) is radiant power, and a is constant, and a=1.2, p0 are wireless signal transmission power, and z is parameter,E is natural constant, and m is parameter,ε is the opposite of air
Dielectric constant, ε=1, r are distance, and P is constant, p=0.85.
2. a kind of fingerprinting localization algorithm based on the filtering of motor behavior discrete data according to claim 1, feature exist
In: in the step S2, according to layer, high, user's height and distance calculate distance r, will distance r and wireless signal
Transmission power substitutes into the radiation patterns of wireless signal, calculates p (r), forms atom fingerprint base.
3. a kind of fingerprinting localization algorithm based on the filtering of motor behavior discrete data according to claim 1, feature exist
In: the user location calculating includes:
Using the two-dimensional coordinate of the strongest wireless signal transmitter installation site of the signal received as polar origin；
Probability of the locatee on each point (ri, θ i) on the polar coordinates is calculated, maximum probability position is obtained,
P (ri, θ i)=pi0×pi1×pi2+ ...,
Wherein p (ri, θ i) is the probability for being located in each point on polar coordinates, pi0For probability of this in polar coordinate system, pi1For
Second wireless signal transmitter position target distance of the distance and its corresponding probability of signal strength, pi2It is the point apart from third
A wireless signal transmitter position target distance and its corresponding probability of signal strength；
The probability of each point of assortment from big to small selects first three to be used as optimal location p (r1, θ 1), p (r2, θ 2), p (r3, θ 3),
Polar coordinates are converted into position coordinates.
4. a kind of fingerprinting localization algorithm based on the filtering of motor behavior discrete data according to claim 1, feature exist
In: the motor behavior discrete data filtering includes:
If the direction of motion be D, speed V,
First, it is assumed that the error in direction is ± π/6, error ± 25% of speed, it is assumed that last accurate positioning, according to the last time
Position, and a region is calculated in the time interval positioned twice, if current location in this error band, Ps
=0.5；
If current location exceeds above-mentioned zone, set error again as ± π/3, error ± 50% of speed, it is assumed that it is last calmly
Level is true, similarly calculate to Two Areas, if current location in this region, Ps=0.4；
If current location exceeds Two Areas, Ps=0.1；
Three optimal points are weighted according to the probability of current location region: pi=p (ri, θ i) × Ps.
5. a kind of fingerprinting localization algorithm based on the filtering of motor behavior discrete data according to claim 1, feature exist
In: the new fingerprint cluster of the formation includes: that the point to strongest signal strength forms (d according to positioning resulti,θi,rssiij)
Table calculates each anchor point (di,θi) corresponding rssiijMean value EiAnd variances sigmai, by mean value EiAnd variances sigmaiAs finger
Line cluster saves, wherein [1,100000] j ∈.
6. a kind of fingerprinting localization algorithm based on the filtering of behavior discrete data according to claim 1, feature exist
It include: to set the distribution of the rssi that location point (d, θ) receives to meet normal distribution in: the described fingerprint cluster positioning, then
The probability of each (d, θ) point is p=f (rssi1) × f (rssi2) × f (rssi3) × Ps, maximum probability in polar coordinates at this time
Point be current location.
7. a kind of fingerprinting localization algorithm based on the filtering of behavior discrete data according to claim 1, feature exist
In: the wireless signal transmitter is 2.4GHz signal projector.
8. a kind of fingerprinting localization algorithm based on the filtering of behavior discrete data according to claim 7, feature exist
In: the wireless signal transmitter includes any one in bluetooth transmitters, wifi transmitter and ZigBee transmitter.
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