CN110472644A - A kind of judgment method of indoor and outdoor and building - Google Patents
A kind of judgment method of indoor and outdoor and building Download PDFInfo
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- CN110472644A CN110472644A CN201810435794.2A CN201810435794A CN110472644A CN 110472644 A CN110472644 A CN 110472644A CN 201810435794 A CN201810435794 A CN 201810435794A CN 110472644 A CN110472644 A CN 110472644A
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- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000003066 decision tree Methods 0.000 claims abstract description 10
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000012790 confirmation Methods 0.000 claims description 6
- 101100289995 Caenorhabditis elegans mac-1 gene Proteins 0.000 claims description 3
- 230000004927 fusion Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 235000015170 shellfish Nutrition 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 7
- 238000010801 machine learning Methods 0.000 abstract description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
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Abstract
The invention discloses a kind of indoor and outdoor and the judgment methods of building, specific step is as follows: step 1, carries out indoor and outdoor judgement: including preparing training data, feature selecting, data prediction, model training, indoor and outdoor judgement and merging existing indoor and outdoor judgment method；Step 2, building judgement: including preparing data, data prediction, model training, building judgement and merging existing building judgment method.Method of the invention is judged using the decision Tree algorithms in the GPS satellite information and machine learning received, it can be promoted and be identified the effect judged outside using GPS feature, different signal sources can be made full use of to carry out comprehensive descision, indoor and outdoor judgement and building judge that effect is good, this method utilizes multilevel policy decision tree-model, adjudicates efficiency and effect is comprehensive better than fingerprint matching.
Description
Technical field
The present invention relates to positions to judge field, the judgment method of specifically a kind of indoor and outdoor and building.
Background technique
Position refers to that place, locating orientation, the near synonym of position are addresses.When people other places or
When somewhere is unfamiliar local, people can habitually determine the position of oneself, and people is facilitated to plan the arrangement of next step, mesh
Preceding people mostly use GPS positioning to determine the position of oneself.In location-based application, hard objectives are indoors or room
Any outside, and specifically it is of great significance in solitary building.People carry out in indoor and outdoor and which specific building at present
Judging means it is relatively simple, do not make full use of source signal；The effect of identification is poor；The speed of identification is slower, this is just
The use of people brings inconvenience.
Summary of the invention
The purpose of the present invention is to provide a kind of indoor and outdoor and the judgment methods of building, to solve in above-mentioned background technique
The problem of proposition.
To achieve the above object, the invention provides the following technical scheme:
A kind of judgment method of indoor and outdoor and building, the specific steps are as follows:
Step 1, carry out indoor and outdoor judgement: including prepare training data, feature selecting, data prediction, model training,
Indoor and outdoor judges and merges existing indoor and outdoor judgment method；
1) prepare training data: respectively indoors and taken outdoors data as training, house data acquisition method be
Open acquisition software in the building in selected target region, acquire the GPS information in building, outdoor data acquisition method be
Acquisition software is opened outside the building in selected target region, acquires the GPS information outside building；
2) the characteristics of according to GPS, number of satellites of the carrier-to-noise ratio greater than 10, carrier-to-noise ratio defending greater than 15 feature selecting: are selected
Star number, carrier-to-noise ratio greater than 20 number of satellites, carrier-to-noise ratio greater than 25 number of satellites, carrier-to-noise ratio greater than 30 number of satellites,
Number of satellites, GPS horizon location error radius and GPS location elevation, respectively symbolically be g10, g15, g20, g25,
G30, lln, llr and llh；
3) data prediction: regarding indoor and outdoor judgement as two classification problems, and 1 indicates it is indoor, -1 expression outdoor,
Respectively by indoor and outdoor acquisition data conversion；
4) model training: the data after conversion are trained (max_depth=3) using decision-tree model, obtain class
Like decision-tree model, by the corresponding rule in every road of root node to leaf node, meet certain rule be judged to positioning it is indoor or
It is outdoor；
5) indoor and outdoor judges: according to the decision-tree model come is trained, to the progress classification judgement of every data of input；
6) merge existing indoor and outdoor judgment method: confirmation GPS feature, the confidence level of sensor, WIFI and BLE are led to
GPS feature, sensor, WIFI and BLE are crossed to judge indoor and outdoor；
Step 2, building judgement: including preparing data, data prediction, model training, building judgement and fusion
Existing building judgment method；
1) prepare data: each layer in each solitary building acquires corresponding wifi finger print information as training data；
2) finger print data of acquisition data prediction: is processed into following format: < mac { i }, building { j }, rssi
{ i, j, k } >, i belongs to [1, mac number], and j belongs to [1, building number], and k belongs to [the fingerprint number of 1, certain mac in certain building]；
3) model training: assuming that some mac probability that different location occurs in some building meets Gaussian Profile,
Gauss is carried out to the data in second step to model to obtain such as drag: < mac { i }, N (i, j) < mu (j), sigma (j),
Building (j) > >, i belongs to [1, mac number], and j belongs to [1, include the building number of mac (i)]；
4) building judgement is carried out using model-naive Bayesian；
5) merge existing building judgment method: the confidence level of confirmation GPS, WIFI, BLE and principle of scoring pass through
GPS, WIFI, BLE and score principle carry out the judgement of building.
As a further solution of the present invention: the judgement of indoor and outdoor and building is all made of weighting method calculating.
As a further solution of the present invention: indoor and outdoor judgement further includes being judged by bright intensity.
As a further solution of the present invention: indoor and outdoor and building judge to further include judging by WIFI fingerprint matching.
As a further solution of the present invention: model-naive Bayesian carries out building judgement, and specific step is as follows: primary
It include N number of mac in Location Request query, respectively<mac1, mac2 ..., macN>, then it goes to search this N number of mac first corresponding
Model information<model1, model2 ..., modelN>, take out the owned building set that includes in these models<
Building1, building2 ..., buildingM >, then to the owned building in set, utilize naive Bayesian standard
Then go the probability<P1, P2 ..., the PM that predict this query in the building>, the target of the person that takes maximum probability as prediction
Building argmax Pi | i belongs to [1, M] }.
Compared with prior art, the beneficial effects of the present invention are: method of the invention utilizes the GPS satellite information received
And the decision Tree algorithms in machine learning are judged, can be promoted and be identified the effect judged outside using GPS feature, it can be sufficiently
Comprehensive descision is carried out using different signal sources, indoor and outdoor judgement and building judge that effect is good, and this method utilizes multilevel policy decision
Tree-model, adjudicates efficiency and effect is comprehensive better than fingerprint matching.
Detailed description of the invention
Fig. 1 is the flow chart of model training when indoor and outdoor judges in indoor and outdoor and the judgment method of building.
Fig. 2 is flow chart when being judged when indoor and outdoor judges in indoor and outdoor and the judgment method of building.
Fig. 3 is to merge existing indoor and outdoor judgment method when indoor and outdoor judges in indoor and outdoor and the judgment method of building
Flow chart.
Fig. 4 is flow chart when being judged when building judges in indoor and outdoor and the judgment method of building.
Fig. 5 is to merge existing building judgment method when building judges in indoor and outdoor and the judgment method of building
Flow chart.
Specific embodiment
The technical solution of the patent is explained in further detail With reference to embodiment.
Please refer to Fig. 1-5, the judgment method of a kind of indoor and outdoor and building, the specific steps are as follows:
Step 1, carry out indoor and outdoor judgement: including prepare training data, feature selecting, data prediction, model training,
Indoor and outdoor judges and merges existing indoor and outdoor judgment method；
1) prepare training data: difference is indoors and taken outdoors data are as training, and house data acquisition method is edge
Each floor in each building of Nanjing Road East opens acquisition software, records GPS characteristic information, and outdoor data acquisition method is along street
It walks up and down twice, records GPS characteristic information；
2) the characteristics of according to GPS, number of satellites of the carrier-to-noise ratio greater than 10, carrier-to-noise ratio defending greater than 15 feature selecting: are selected
Star number, carrier-to-noise ratio greater than 20 number of satellites, carrier-to-noise ratio greater than 25 number of satellites, carrier-to-noise ratio greater than 30 number of satellites,
Number of satellites, GPS horizon location error radius and GPS location elevation, respectively symbolically be g10, g15, g20, g25,
G30, lln, llr and llh；
3) data prediction: regarding indoor and outdoor judgement as two classification problems, and 1 indicates it is indoor, -1 expression outdoor,
Respectively by indoor and outdoor acquisition data conversion at the format of table 1: totally 16290, wherein 11776 indoor, outdoor 4514
Item；
Table 1
| SeqNo | g10 | g15 | g20 | g25 | g30 | lln | llr | llh | Classification |
| 1 | 3 | 2 | 1 | 0 | 0 | 3 | 48 | 51 | 1 |
| 2 | 10 | 10 | 6 | 0 | 0 | 10 | 64 | 21 | 1 |
| ...... | ...... | ...... | ...... | ...... | ...... | ...... | ...... | ...... | ...... |
| 16289 | 15 | 15 | 13 | 12 | 5 | 15 | 12 | 122 | -1 |
| 16290 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
4) model training: the data after conversion are trained (max_depth=3) using decision-tree model, obtain class
Like decision-tree model, by the corresponding rule in every road of root node to leaf node, meet certain rule be judged to positioning it is indoor or
It is outdoor；
5) indoor and outdoor judges: according to training the decision-tree model come, to the progress classification judgement of every data of input,
Such as: input<16,15,10,4,2,17,24,70>, output is judged to outdoor (- 1)；
6) merge existing indoor and outdoor judgment method: confirmation GPS feature, the confidence level of sensor, WIFI and BLE are led to
GPS feature, sensor, WIFI and BLE are crossed to judge indoor and outdoor；
Step 2, building judgement: including preparing data, data prediction, model training, building judgement and fusion
Existing building judgment method；
1) prepare data: each layer in each solitary building acquires corresponding wifi finger print information as training data；
2) finger print data of acquisition data prediction: is processed into following format: < mac { i }, building { j }, rssi
{ i, j, k } >, i belongs to [1, mac number], and j belongs to [1, building number], and k belongs to [the fingerprint number of 1, certain mac in certain building],
It is shown in Table 2；
Table 2
| The address mac | Build name | The signal intensity profile of acquisition |
| mac{1} | building{1} | rssi{1；1；1,2,3... } |
| mac{1} | building{2} | rssi{1；2；1,2,3... } |
| mac{1} | building{3} | rssi{1；3；1,2,3... } |
| mac{2} | building{1} | rssi{2；1；1,2,3... } |
| ...... | ...... | ...... |
| mac{i} | building{j} | rssi{i；j；1,2,3... } |
3) model training: assuming that some mac probability that different location occurs in some building meets Gaussian Profile,
Gauss is carried out to the data in second step to model to obtain such as drag: < mac { i }, N (i, j) < mu (j), sigma (j),
Building (j) > >, i belongs to [1, mac number], and j belongs to [1, include the building number of mac (i)], is shown in Table 3；
Table 3
| The address mac | Model information model { i } |
| mac{1} | Model { 1 }=(mu { 1, j }, sigma { 1, j }, building { 1, j }) ..., () |
| mac{2} | Model { 2 }=(mu { 2, j }, sigma { 2, j }, building { 2, j }) ..., () |
| ...... | ...... |
| mac{i} | Model { i }=(mu { i, j }, sigma { i, j }, building { i, j }) ..., () |
4) building judgement is carried out using model-naive Bayesian, includes N number of mac in a Location Request query, respectively
For<mac1, mac2 ..., macN>, then remove to search the corresponding model information<model1 of this N number of mac first, model2 ...,
ModelN>, take out the owned building set for including in these models<building1, building2 ..., buildingM
>, then to the owned building in set, go to predict that this query is general in the building using naive Bayesian criterion
Rate<P1, P2 ..., PM>, the target construction argrnax { Pi | i belongs to [1, M] } of the person that takes maximum probability as prediction；
5) merge existing building judgment method: the confidence level of confirmation GPS, WIFI, BLE and principle of scoring pass through
GPS, WIFI, BLE and score principle carry out the judgement of building.The judgement of indoor and outdoor and building is all made of weighting method meter
It calculates.Indoor and outdoor judgement further includes being judged by bright intensity.Indoor and outdoor and building judgement further include by WIFI fingerprint matching
Judgement.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (5)
1. the judgment method of a kind of indoor and outdoor and building, which is characterized in that specific step is as follows:
Step 1 carries out indoor and outdoor judgement: including preparing training data, feature selecting, data prediction, model training, interior
It is outer to judge and merge existing indoor and outdoor judgment method；
1) prepare training data: difference is indoors and taken outdoors data are as training, and house data acquisition method is selected
Acquisition software is opened in the building of target area, acquires the GPS information in building, and outdoor data acquisition method is selected
Acquisition software is opened outside the building of target area, acquires the GPS information outside building；
2) the characteristics of according to GPS, number of satellites of the carrier-to-noise ratio greater than 10, the carrier-to-noise ratio satellite greater than 15 feature selecting: are selected
The number of satellites of number, carrier-to-noise ratio greater than 20, number of satellites of the carrier-to-noise ratio greater than 25, number of satellites of the carrier-to-noise ratio greater than 30, satellite
Number, GPS horizon location error radius and GPS location elevation, respectively symbolically be g10, g15, g20, g25, g30,
Lln, llr and llh；
3) data prediction: indoor and outdoor judgement is regarded as two classification problems, 1 indicates it is indoor, -1 expression outdoor, respectively
By indoor and outdoor acquisition data conversion；
4) model training: being trained (max_depth=3) using decision-tree model for the data after conversion, obtains similar determine
Plan tree-model is met certain rule and is judged to position indoor or outdoors by the corresponding rule in every road of root node to leaf node；
5) indoor and outdoor judges: according to the decision-tree model come is trained, to the progress classification judgement of every data of input；
6) merge existing indoor and outdoor judgment method: confirmation GPS feature, the confidence level of sensor, WIFI and BLE pass through GPS
Feature, sensor, WIFI and BLE judge indoor and outdoor；
Step 2, building judgement: existing including preparing data, data prediction, model training, building judgement and fusion
Building judgment method；
1) prepare data: each layer in each solitary building acquires corresponding wifi finger print information as training data；
2) finger print data of acquisition data prediction: is processed into following format: < mac { i }, building { j }, rssi i, j,
K } >, i belongs to [1, mac number], and j belongs to [1, building number], and k belongs to [the fingerprint number of 1, certain mac in certain building]；
3) model training: assuming that some mac probability that different location occurs in some building meets Gaussian Profile, to the
Data in two steps carry out Gauss and model to obtain such as drag: < mac { i }, N (i, j) < mu (j), sigma (j), building
(j) > >, i belongs to [1, mac number], and j belongs to [1, include the building number of mac (i)]；
4) building judgement is carried out using model-naive Bayesian；
5) merge existing building judgment method: the confidence level of confirmation GPS, WIFI, BLE and principle of scoring, by GPS,
WIFI, BLE and score principle carry out the judgement of building.
2. the judgment method of indoor and outdoor according to claim 1 and building, which is characterized in that the indoor and outdoor and building
The judgement of object is all made of weighting method calculating.
3. the judgment method of indoor and outdoor according to claim 1 or 2 and building, which is characterized in that the indoor and outdoor is sentenced
Disconnected further includes being judged by bright intensity.
4. the judgment method of indoor and outdoor according to claim 1 to 3 and building, which is characterized in that the indoor and outdoor
It further include being judged by WIFI fingerprint matching with building judgement.
5. the judgment method of indoor and outdoor according to claim 1 to 4 and building, which is characterized in that the simplicity shellfish
This model of leaf carry out building judgement specific step is as follows: in a Location Request query include N number of mac, respectively < mac1,
Mac2 ..., macN>, then go to search first this N number of mac corresponding model information<model1, model2 ..., modelN>,
Take out the owned building set for including in these models<building1,building2,...,buildingM>, then right
Owned building in set goes to predict probability < P1 of this query in the building using naive Bayesian criterion,
P2 ..., PM >, the target construction argmax { Pi | i belongs to [1, M] } of the person that takes maximum probability as prediction.
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Cited By (1)
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| CN112987064A (en) * | 2021-02-09 | 2021-06-18 | 北京百度网讯科技有限公司 | Building positioning method, device, equipment, storage medium and terminal equipment |
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