CN109341682A - A method of improving earth's magnetic field positioning accuracy - Google Patents

A method of improving earth's magnetic field positioning accuracy Download PDF

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Publication number
CN109341682A
CN109341682A CN201811337078.7A CN201811337078A CN109341682A CN 109341682 A CN109341682 A CN 109341682A CN 201811337078 A CN201811337078 A CN 201811337078A CN 109341682 A CN109341682 A CN 109341682A
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magnetic field
point
probability
data
earth
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CN109341682B (en
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张烨
郭艺玲
樊超
樊一超
许艇
程康
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Zhejiang University of Technology ZJUT
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/04—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means
    • G01C21/08—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means involving use of the magnetic field of the earth
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Environmental & Geological Engineering (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geology (AREA)
  • Navigation (AREA)

Abstract

A method of earth's magnetic field positioning accuracy is improved, includes the following steps: that step 1. establishes the earth's magnetic field fingerprint database based on statistical distribution；Step 2 measured value is located at the confidence calculations method of each grid of fingerprint database；Step 3. is in positioning stage, the coordinate of current location is calculated using the method for route matching, the step of measurement and calculating is: the part of most critical is that the probability of the possible position of each turning point is calculated according to historical path data and prior probability using Bayes classifier in the above-mentioned location algorithm of step 4..A series of general unique methods of the present invention establish the confidence interval fingerprint database in earth's magnetic field, in position fixing process, according to the prior probability of historical location data, the probability for being currently at each possible position is calculated using Bayes classifier, compare with traditional the methods of K weighting nearest neighbour method, position error is considerably reduced, the accuracy rate of earth's magnetic field positioning is improved.

Description

A method of improving earth's magnetic field positioning accuracy
Technical field
The invention mainly includes a kind of methods for improving earth's magnetic field positioning accuracy, are a series of general unique methods The confidence interval fingerprint database for establishing earth's magnetic field, in position fixing process, according to the prior probability of historical location data, using shellfish Leaf this classifier calculates the probability for being currently at each possible position, main to apply to improve the accuracy rate of earth's magnetic field positioning The internet of things field such as positioning indoors.
Background technique
Carrying out positioning using earth magnetic field is one of indoor positioning schemes numerous at present, this locating scheme and at present ratio The main distinction of the more popular mode positioned based on multiple radio-frequency signal sources is without prior deployment positioning signal source Emitter.When the place that need to be positioned is uncertain, or the substantial amounts in place need to be positioned, cause that positioning signal source can not be disposed When, it may be considered that use this locating scheme.
Summary of the invention
The present invention will overcome the disadvantages mentioned above of the prior art, provide a kind of method for improving earth's magnetic field positioning accuracy.
The present invention has been established by combining confidence level fingerprint database, route matching method and Naive Bayes Classifier Kind earth's magnetic field location model, also achieves preferable positioning accuracy, while it is with higher feasible to demonstrate earth's magnetic field positioning Property and application prospect.
A method of earth's magnetic field positioning accuracy is improved, is included the following steps:
1. establishing the earth's magnetic field fingerprint database based on statistical distribution.
Earth magnetic field vector can generally decompose under three component directions of reference axis: with geographic direct north, Due east direction and with horizontal plane in downward direction, be decomposed into the due north component, due east component and vertical component of total magnetic field.Ground The projection of signal magnetic field in the horizontal plane, referred to as horizontal component, always point at earth magnetism due north.The direction of horizontal component is exactly compass The direction of arctic meaning, commonly known as magnetic north；The angle that horizontal component and geographical direct north are formed, referred to as magnetic declination.Ground The angle that signal magnetic field and horizontal plane are formed, referred to as magnetic dip angle.
The parameters in earth's magnetic field can be marked in summary are as follows: magnetic field overall strength B, due north component Bθ, due east component BΦ, vertical component Bγ, horizontal component BH, declination D, inclination obliquity I, as shown in Figure 2.
It is first the grid of i row j column by Parcel division to be measured, the precision that the size of grid should be less than geomagnetic field measuring is wanted It asks, then measures the geomagnetic field intensity of each grid as follows:
WhereinThe component on the direction of three, earth's magnetic field is respectively indicated,Indicate the overall strength in earth's magnetic field. It is checked due to passing through,Normal distribution is statistically substantially conformed to, therefore we can make its distribution characteristics It is stored for fingerprint characteristic.Therefore the data format of fingerprint database are as follows:
(μAx,μAy,μAz,σAx,σAy,σAz,xA,yA) (2)
Wherein μAx, μAy, μAzRespectively three direction magnetic field strengths are by the mean value after gaussian filtering, σAx, σAy, σAzPoint Not Wei three direction magnetic field strengths standard deviation, xA, yAFor position coordinates.
2. the confidence calculations method that measured value is located at each grid of fingerprint database.
It, can be by calculating its minimum when needing to judge the matching degree of the data in location data and fingerprint database The corresponding confidence level of confidence interval, it may be assumed that
P(θ1<θ<θ2)=1- α (3)
When there is one group of stochastic variable X (x1,x2,x3,…,xn) it is one group of sample, if X~N (μ, σ2), random interval if it exists [θ1,θ2], make to meet above formula for given α (0 < α < 1), then claims random interval [θ1,θ2] be θ confidence level be 1- α Confidence interval, θ1Referred to as confidence lower limit, θ2Referred to as confidence upper limit, probability 1- α are known as confidence level or confidence level, confidence interval Meaning be it with the probability of 1- α include unknown parameter θ.
It, can be by with sample variance S when sample size is greater than 502Estimate σ, carry out confidence interval of mean calculating, Have at this time:
WhereinFor sample average, μ is population mean, and S is sample standard deviation, and n is sample size.For given confidence 1- α is spent, there is probability:
The substitution of preceding formula can be obtained:
It is easy to get in this wayValue, just obtain at this time μ confidence level be 1- α confidence interval, if there is determination at this time Sample can calculate, just obtain the constant expression-form of confidence interval:
3. the step of calculating the coordinate of current location using the method for route matching in positioning stage, measuring and calculate It is:
(1) in straight trip section, the direction of motion and movement velocity are measured by direction sensor and acceleration transducer, and call Geomagnetic sensor measures 10 groups of absolute force data；
(2) bearing data is acquired using inertial sensor, whether is substantially changed by angle, judges whether to turn；
(3) in turning point, geomagnetic sensor is called, measures 10 groups of absolute force data；
(4) every straight trip segment length is calculated according to inertial sensor data and time, and length is depicted as with direction Vector；
(5) each vector is linked in sequence, obtains path locus；
(6) by trajectory shape, several possible walking paths are obtained；
(7) combine walking path obtained in the previous step, the geomagnetic data by resolving turning point calculates confidence level, and uses Bayes classifier acquires the probability of several path turning points according to historical path data and prior probability；
It is using the specific method that Bayes classifier acquires several path turning point probability:
The basic model of NB Algorithm is, if event a1,a2,a3... one self-contained mode of composition, i.e. these things Part is mutually indepedent, and P (ai) > 0, then having total probability formula for any one event b:
P (b)=∑iP(ai)P(b|ai) (8)
If P (b) > 0 has:
In measurement process, prior probability refers to that the historical data of acquisition, posterior probability refer to that present node is located at some The probability of position, in the case where known prior probability P (A=a) P (B=b) and P (B=b | A=a), calculate P (A=a | B= B) formula are as follows:
When actual location calculates, sometime in the collected one group of magnetic field strength date of certain point ε, then ε point is calculated It is as follows with the method for the matching probability of point (i, j):
Here, it is " certain point is (i, j) in training example set " that event H is practical, and attribute D is three sides in training example set To magnetic field data it is approximately equal with the magnetic field data in three directions of ε point respectively.Since magnetic field data meets normal distribution, at this In it is approximately equal between two data mean, the intensity size and the difference for the point data being matched of ε point a direction are small The size of Mr. Yu's confidence level ξ, ξ are set as being matched the 5% of a standard deviation for corresponding direction data herein, i.e. the σ of ξ=0.05. Using Bayes classifier classifier formula, can be obtained:
Wherein xε、yε、zεIt is the magnetic field strength on three directions that ε point measures respectively, x, y, z is trained example respectively The magnetic field strength on three directions that the point of concentration measures.Since the magnetic field strength in three directions in any point is mutually indepedent, Make | xε- x |, | yε- y |, | zε- z | it is mutually indepedent, just have:
P(|xε-x|<ξx,|yε-y|<ξy,|zε-z|<ξz)=P (| xε-x|<ξx)×P(|yε-y|<ξy)×P(|zε-z|< ξz) (12)
And:
It is hereby achieved that the matching probability of ε point and point (i, j), then matching probabilities of ε point and other points are calculated, obtained Point with maximum probability is the position where ε point most probable.Then in entire measured zone, measurement is on all the nodes Magnetic field data is measured, and carries out average value filtering, obtained data characteristics is positioned according to processing ε point methods, it must To the positioning result of each node.
(8) it is matched to most probable path, to be positioned in the probability value of turning point by comparing several ways diameter Position.
Advantages of the present invention:
The present invention is for the angle that earth's magnetic field positions, its emitter without deployment positioning signal source in advance can be Reach efficient utilization in the scene of certain features.Compared to traditional localization method, worked as using Bayes classifier to calculate Position error can be greatly lowered in the preceding probability in each possible position, can be improved the accuracy of earth's magnetic field positioning.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is the schematic diagram of the component parameters in all directions of earth's magnetic field.
Note: the variable declaration in attached drawing 2, B indicate magnetic field overall strength, BθIndicate the due north component of magnetic field strength, BΦIt indicates The due east component of magnetic field strength, BγIndicate the vertical component of magnetic field strength, BHIndicate the horizontal component of magnetic field strength, D indicates magnetic Drift angle, I indicate magnetic dip angle.
Specific embodiment
With reference to the accompanying drawing, technical solution of the present invention is further illustrated.
A method of earth's magnetic field positioning accuracy is improved, is included the following steps:
1. establishing the earth's magnetic field fingerprint database based on statistical distribution.
Earth magnetic field vector can generally decompose under three component directions of reference axis: with geographic direct north, Due east direction and with horizontal plane in downward direction, be decomposed into the due north component, due east component and vertical component of total magnetic field.Ground The projection of signal magnetic field in the horizontal plane, referred to as horizontal component, always point at earth magnetism due north.The direction of horizontal component is exactly compass The direction of arctic meaning, commonly known as magnetic north；The angle that horizontal component and geographical direct north are formed, referred to as magnetic declination.Ground The angle that signal magnetic field and horizontal plane are formed, referred to as magnetic dip angle.
The parameters in earth's magnetic field can be marked in summary are as follows: magnetic field overall strength B, due north component Bθ, due east component BΦ, vertical component Bγ, horizontal component BH, declination D, inclination obliquity I, as shown in Figure 2.
It is first the grid of i row j column by Parcel division to be measured, the precision that the size of grid should be less than geomagnetic field measuring is wanted It asks, then measures the geomagnetic field intensity of each grid as follows:
WhereinThe component on the direction of three, earth's magnetic field is respectively indicated,Indicate the overall strength in earth's magnetic field. It is checked due to passing through,Normal distribution is statistically substantially conformed to, therefore we can make its distribution characteristics It is stored for fingerprint characteristic.Therefore the data format of fingerprint database are as follows:
(μAx,μAy,μAz,σAx,σAy,σAz,xA,yA) (2)
Wherein μAx, μAy, μAzRespectively three direction magnetic field strengths are by the mean value after gaussian filtering, σAx, σAy, σAzPoint Not Wei three direction magnetic field strengths standard deviation, xA, yAFor position coordinates.
2. the confidence calculations method that measured value is located at each grid of fingerprint database.
It, can be by calculating its minimum when needing to judge the matching degree of the data in location data and fingerprint database The corresponding confidence level of confidence interval, it may be assumed that
P(θ1<θ<θ2)=1- α (3)
When there is one group of stochastic variable X (x1,x2,x3,…,xn) it is one group of sample, if X~N (μ, σ2), random interval if it exists [θ1,θ2], make to meet above formula for given α (0 < α < 1), then claims random interval [θ1,θ2] be θ confidence level be 1- α Confidence interval, θ1Referred to as confidence lower limit, θ2Referred to as confidence upper limit, probability 1- α are known as confidence level or confidence level, confidence interval Meaning be it with the probability of 1- α include unknown parameter θ.
It, can be by with sample variance S when sample size is greater than 502Estimate σ, carry out confidence interval of mean calculating, Have at this time:
WhereinFor sample average, μ is population mean, and S is sample standard deviation, and n is sample size.For given confidence 1- α is spent, there is probability:
The substitution of preceding formula can be obtained:
It is easy to get in this wayValue, just obtain at this time μ confidence level be 1- α confidence interval, if there is determination at this time Sample can calculate, just obtain the constant expression-form of confidence interval:
3. the step of calculating the coordinate of current location using the method for route matching in positioning stage, measuring and calculate It is:
(1) in straight trip section, the direction of motion and movement velocity are measured by direction sensor and acceleration transducer, and call Geomagnetic sensor measures 10 groups of absolute force data；
(2) bearing data is acquired using inertial sensor, whether is substantially changed by angle, judges whether to turn；
(3) in turning point, geomagnetic sensor is called, measures 10 groups of absolute force data；
(4) every straight trip segment length is calculated according to inertial sensor data and time, and length is depicted as with direction Vector；
(5) each vector is linked in sequence, obtains path locus；
(6) by trajectory shape, several possible walking paths are obtained；
(7) combine walking path obtained in the previous step, the geomagnetic data by resolving turning point calculates confidence level, and uses Bayes classifier acquires the probability of several path turning points according to historical path data and prior probability；
It is using the specific method that Bayes classifier acquires several path turning point probability:
The basic model of NB Algorithm is, if event a1,a2,a3... one self-contained mode of composition, i.e. these things Part is mutually indepedent, and P (ai) > 0, then having total probability formula for any one event b:
P (b)=∑iP(ai)P(b|ai) (8)
If P (b) > 0 has:
In measurement process, prior probability refers to that the historical data of acquisition, posterior probability refer to that present node is located at some The probability of position, in the case where known prior probability P (A=a) P (B=b) and P (B=b | A=a), calculate P (A=a | B= B) formula are as follows:
When actual location calculates, sometime in the collected one group of magnetic field strength date of certain point ε, then ε point is calculated It is as follows with the method for the matching probability of point (i, j):
Here, it is " certain point is (i, j) in training example set " that event H is practical, and attribute D is three sides in training example set To magnetic field data it is approximately equal with the magnetic field data in three directions of ε point respectively.Since magnetic field data meets normal distribution, at this In it is approximately equal between two data mean, the intensity size and the difference for the point data being matched of ε point a direction are small The size of Mr. Yu's confidence level ξ, ξ are set as being matched the 5% of a standard deviation for corresponding direction data herein, i.e. the σ of ξ=0.05. Using Bayes classifier classifier formula, can be obtained:
Wherein xε、yε、zεIt is the magnetic field strength on three directions that ε point measures respectively, x, y, z is trained example respectively The magnetic field strength on three directions that the point of concentration measures.Since the magnetic field strength in three directions in any point is mutually indepedent, Make | xε- x |, | yε- y |, | zε- z | it is mutually indepedent, just have:
P(|xε-x|<ξx,|yε-y|<ξy,|zε-z|<ξz)=P (| xε-x|<ξx)×P(|yε-y|<ξy)×P(|zε-z|< ξz) (12)
And:
It is hereby achieved that the matching probability of ε point and point (i, j), then matching probabilities of ε point and other points are calculated, obtained Point with maximum probability is the position where ε point most probable.Then in entire measured zone, measurement is on all the nodes Magnetic field data is measured, and carries out average value filtering, obtained data characteristics is positioned according to processing ε point methods, it must To the positioning result of each node.
(8) it is matched to most probable path, to be positioned in the probability value of turning point by comparing several ways diameter Position.
Content described in this specification embodiment is only enumerating to the way of realization of inventive concept, protection of the invention Range should not be construed as being limited to the specific forms stated in the embodiments, and protection scope of the present invention is also and in art technology Personnel conceive according to the present invention it is conceivable that equivalent technologies mean.

Claims (1)

1. a kind of method for improving earth's magnetic field positioning accuracy, includes the following steps:
Step 1. establishes the earth's magnetic field fingerprint database based on statistical distribution；
Earth magnetic field vector can generally decompose under three component directions of reference axis: with geographic direct north, due east Direction and with horizontal plane in downward direction, be decomposed into the due north component, due east component and vertical component of total magnetic field；Earth magnetic The projection of field in the horizontal plane, referred to as horizontal component, always point at earth magnetism due north；The direction of horizontal component is exactly the compass arctic Signified direction, commonly known as magnetic north；The angle that horizontal component and geographical direct north are formed, referred to as magnetic declination；Earth magnetic The angle that field is formed with horizontal plane, referred to as magnetic dip angle；
The parameters in earth's magnetic field can be marked in summary are as follows: magnetic field overall strength B, due north component Bθ, due east component BΦ, hang down Straight component Bγ, horizontal component BH, declination D, inclination obliquity I；
It is first the grid of i row j column by Parcel division to be measured, the size of grid should be less than the required precision of geomagnetic field measuring, Then the geomagnetic field intensity of each grid is measured as follows:
WhereinThe component on the direction of three, earth's magnetic field is respectively indicated,Indicate the overall strength in earth's magnetic field；Due to By checking,Statistically substantially conform to normal distribution, therefore we can be using its distribution characteristics as referring to Line feature is stored；Therefore the data format of fingerprint database are as follows:
(μAx, μAy, μAz, σAx, σAy, σAz, xA, yA) (2)
Wherein μAx, μAy, μAzRespectively three direction magnetic field strengths are by the mean value after gaussian filtering, σAx, σAy, σAzRespectively The standard deviation of three direction magnetic field strengths, xA, yAFor position coordinates；
Step 2. measured value is located at the confidence calculations method of each grid of fingerprint database；
It, can be by calculating its minimum confidence when needing to judge the matching degree of the data in location data and fingerprint database The corresponding confidence level in section, it may be assumed that
P(θ1< θ < θ2)=1- α (3)
When there is one group of stochastic variable X (x1, x2, x3..., xn) it is one group of sample, if X~N (μ, σ2), random interval [θ if it exists1, θ2], make to meet above formula for given α (0 < α < 1), then claims random interval [θ1, θ2] be θ confidence level be 1- α Confidence interval, θ1Referred to as confidence lower limit, θ2Referred to as confidence upper limit, probability 1- α are known as confidence level or confidence level, confidence interval It includes unknown parameter θ that meaning, which is it with the probability of 1- α,；
It, can be by with sample variance S when sample size is greater than 502Estimate σ, carries out confidence interval of mean calculating, at this time Have:
WhereinFor sample average, μ is population mean, and S is sample standard deviation, and n is sample size；For given confidence level 1- α has probability:
The substitution of preceding formula can be obtained:
It is easy to get in this wayValue, just obtain at this time μ confidence level be 1- α confidence interval, if there is determining sample at this time It can calculate, just obtain the constant expression-form of confidence interval:
The step of step 3. calculates the coordinate of current location using the method for route matching in positioning stage, measures and calculates It is:
(1) in straight trip section, the direction of motion and movement velocity are measured by direction sensor and acceleration transducer, and call earth magnetism Sensor measures 10 groups of absolute force data；
(2) bearing data is acquired using inertial sensor, whether is substantially changed by angle, judges whether to turn；
(3) in turning point, geomagnetic sensor is called, measures 10 groups of absolute force data；
(4) every straight trip segment length is calculated according to inertial sensor data and time, and by length be depicted as with it is directive to Amount；
(5) each vector is linked in sequence, obtains path locus；
(6) by trajectory shape, several possible walking paths are obtained；
(7) combine walking path obtained in the previous step, the geomagnetic data by resolving turning point calculates confidence level, and uses pattra leaves This classifier acquires the probability of several path turning points according to historical path data and prior probability；
It is using the specific method that Bayes classifier acquires several path turning point probability:
The basic model of NB Algorithm is, if event a1,a2,a3... one self-contained mode of composition, i.e. these event phases It is mutually independent, and P (ai) > 0, then having total probability formula for any one event b:
P (b)=∑iP(ai)P(b|ai) (8)
If P (b) > 0 has:
In measurement process, prior probability refers to that the historical data of acquisition, posterior probability refer to that present node is located at some position Probability calculate P's (A=a | B=b) in the case where known prior probability P (A=a) P (B=b) and P (B=b | A=a) Formula are as follows:
When actual location calculates, sometime in the collected one group of magnetic field strength date of certain point ε, then ε point and point are calculated The method of the matching probability of (i, j) is as follows:
Here, it is " certain point is (i, j) in training example set " that event H is practical, and attribute D is three directions in training example set Magnetic field data is approximately equal with the magnetic field data in three directions of ε point respectively；Since magnetic field data meets normal distribution, herein two It is approximately equal between a data to mean, the small Mr. Yu of difference of the intensity size of ε point a direction and the point data being matched The size of confidence level ξ, ξ are set as being matched the 5% of a standard deviation for corresponding direction data herein, i.e. the σ of ξ=0.05；Using Bayes classifier classifier formula can be obtained:
Wherein xε、yε、zεIt is the magnetic field strength on three directions that ε point measures respectively, x, y, z is in trained example set respectively Three directions measuring of point on magnetic field strength；Since the magnetic field strength in three directions in any point is mutually indepedent, make | xε- x |, | yε- y |, | zε- z | it is mutually indepedent, just have:
P(|xε-x|<ξx,|yε-y|<ξy,|zε-z|<ξz)=
P(|xε-x|<ξx)×P(|yε-y|<ξy)×P(|zε-z|<ξz) (12)
And:
It is hereby achieved that the matching probability of ε point and point (i, j), then the matching probability of ε point and other points is calculated, it is general to obtain matching The maximum point of rate is the position where ε point most probable；Then in entire measured zone, measurement measures on all the nodes Magnetic field data, and average value filtering is carried out, obtained data characteristics is positioned according to processing ε point methods, is just obtained every The positioning result of a node；
(8) it is matched to most probable path, to obtain sprocket bit in the probability value of turning point by comparing several ways diameter It sets.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112261583A (en) * 2019-07-22 2021-01-22 腾讯科技（深圳）有限公司 Passenger flow thermodynamic diagram generation method and related device
CN112362044A (en) * 2020-11-03 2021-02-12 北京无限向溯科技有限公司 Indoor positioning method, device, equipment and system
CN114268919A (en) * 2021-11-12 2022-04-01 北京航空航天大学 Channel state information fingerprint positioning method based on weighted k nearest neighbor

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120136604A1 (en) * 2010-11-30 2012-05-31 Industrial Technology Research Institute Method and apparatus for 3d attitude estimation
CN106323275A (en) * 2015-06-17 2017-01-11 中国科学院上海高等研究院 Walker indoor positioning method based on Bayes estimation and map aided calibration
CN107250727A (en) * 2014-09-15 2017-10-13 牛津大学创新有限公司 Determine position of the mobile device in geographic area
CN107702712A (en) * 2017-09-18 2018-02-16 哈尔滨工程大学 Indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases
CN108225304A (en) * 2018-01-26 2018-06-29 青岛美吉海洋地理信息技术有限公司 Based on method for rapidly positioning and system in Multiple Source Sensor room

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120136604A1 (en) * 2010-11-30 2012-05-31 Industrial Technology Research Institute Method and apparatus for 3d attitude estimation
CN107250727A (en) * 2014-09-15 2017-10-13 牛津大学创新有限公司 Determine position of the mobile device in geographic area
CN106323275A (en) * 2015-06-17 2017-01-11 中国科学院上海高等研究院 Walker indoor positioning method based on Bayes estimation and map aided calibration
CN107702712A (en) * 2017-09-18 2018-02-16 哈尔滨工程大学 Indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases
CN108225304A (en) * 2018-01-26 2018-06-29 青岛美吉海洋地理信息技术有限公司 Based on method for rapidly positioning and system in Multiple Source Sensor room

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112261583A (en) * 2019-07-22 2021-01-22 腾讯科技（深圳）有限公司 Passenger flow thermodynamic diagram generation method and related device
CN112261583B (en) * 2019-07-22 2022-05-24 腾讯科技（深圳）有限公司 Passenger flow thermodynamic diagram generation method and related device
CN112362044A (en) * 2020-11-03 2021-02-12 北京无限向溯科技有限公司 Indoor positioning method, device, equipment and system
CN114268919A (en) * 2021-11-12 2022-04-01 北京航空航天大学 Channel state information fingerprint positioning method based on weighted k nearest neighbor
CN114268919B (en) * 2021-11-12 2022-10-18 北京航空航天大学 Channel state information fingerprint positioning method based on weighted k nearest neighbor

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