CN107167129B - Cloud step-size estimation method - Google Patents
Cloud step-size estimation method Download PDFInfo
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- CN107167129B CN107167129B CN201710310312.6A CN201710310312A CN107167129B CN 107167129 B CN107167129 B CN 107167129B CN 201710310312 A CN201710310312 A CN 201710310312A CN 107167129 B CN107167129 B CN 107167129B
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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
- G01C21/10—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
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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
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
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Abstract
The present invention provides a kind of cloud step-size estimation methods, which comprises the following steps: the acceleration measuring of step S1, first mobile phone measure acceleration；The time interval between each step, i.e. cadence are calculated according to pedometer principle；Calculate accelerometer standard deviation and accelerometer variance in 1 second time；Step S2 calculates the distance between two o'clock according to the position location of satellite navigation system；Step S3 calculates the step number detected in any two time interval according to pedometer principle；Calculate the average step length in two time intervals；Step S4, mobile phone send cloud server by network for cadence, accelerometer variance and average step length；Cloud server calculates step-length model, and the parameter of step-length model is sent to mobile phone.The object of the invention calculates the step-size estimation model for adapting to different people, is finally reached the trueness error of step-size estimation less than 5%.
Description
Technical field
The present invention relates to pedestrian's dead reckoning fields, and in particular, to a kind of cloud step-size estimation method.
Background technique
PDR (Pedestrian Dead Reckoning, pedestrian's dead reckoning) is a kind of positioning skill of pedestrian's dead reckoning
Art.Next position location is extrapolated from direction of travel according to each step pitch that people walks, is highly suitable in satellite-signal
(such as market, hospital) continues to position in disconnected environment, promotes the effective percentage of positioning.The technology mainly utilizes MEMS built in mobile phone
Sensor (three-axis gyroscope, three axis accelerometer and magnetic sensor), estimates pedestrian side by Data Fusion of Sensor
To and pedestrian per step by step growing, indoor positioning may be implemented.Due to everyone height, the differences such as weight use unification
Step-length model can not accurately estimate the step-length of different people, so that PDR positioning accuracy substantially reduces.The method of step-size estimation at present
The linear relationship of step-length and cadence is mainly estimated by the method for linear fit.But this general linear model can not fit
For owner.The present invention mainly passes through the cadence and kinergety that mobile phone acquires everyone, is sent to cloud, instructs beyond the clouds
Practise the step-length model suitable for mobile phone holder.
The prior art is unified step-length model to be estimated using the cadence of pedestrian, but such method is not particularly suited for institute
Someone, main cause are everyone height, and weight, walking step state is all different, cannot go adaptation all with unified model
People.
Summary of the invention
In view of the shortcomings of the prior art, the invention proposes a kind of cloud step-size estimation method, using low on smart phone
The accelerometer and cloud server of cost, solve the technical issues of step-size estimation.
The technical solution adopted by the present invention is that:
A kind of cloud step-size estimation method, which comprises the following steps:
The acceleration measuring of step S1, mobile phone measure acceleration；According to pedometer principle calculate between each step when
Between be spaced, i.e. cadence；Calculate accelerometer standard deviation and accelerometer variance in 1 second time；
Step S2 calculates the distance between two o'clock according to the position location of satellite navigation system；
Step S3 calculates the step number detected in any two time interval according to pedometer principle；When calculating two
Between interval in average step length；
Step S4, mobile phone send cloud server by network for cadence, accelerometer variance and average step length；Cloud
Server calculates step-length model, and the parameter of step-length model is sent to mobile phone.
Further, accelerometer standard deviation and accelerometer variance are calculated by sliding window in the step S1.
Further, the accelerometer standard deviation is s, and calculation formula is as follows:
Wherein, axi, ayiAnd aziRespectively represent the x-axis that acceleration measuring measures, the acceleration of y-axis and z-axis；μ is the earth
Acceleration of gravity, value 9.8m/s2；N indicates the quantity for the acceleration that acceleration measuring measures；
The accelerometer variance is s2。
Further, the step S2 Satellite navigation system includes GPS or Beidou.
Further, two o'clock is respectively P1 and P2 in the step S2, and P1 point longitude and latitude is (N1, E1), P2 point longitude and latitude
For (N2, E2), the distance between P1 point and P2 point calculation formula are as follows:
Wherein, a=6378137.0, b=6356752.3142, dis are the distance between P1 point and P2 point.
Further, P1 point is T1 moment position location, and the step number of pedometer output is L1；P2 point is fixed for the T2 moment
Position position, and the step number of pedometer output is L2；The calculation formula of average step length is as follows in the step S3:
Wherein, Stride is average step length.
Further, network includes wifi, 2G, 3G or 4G in the step S4, and cloud server utilization receives
Data calculate step-length model by gradient descent algorithm.
Further, the calculation formula of the step-length model is as follows:
Y=a0+a1*f+a2*f2+a3*v+a4*v2+a5*e-f
Wherein f indicates cadence；V indicates accelerometer variance；A0, a1, a2, a3, a4, a5 are obtained by gradient descent algorithm
Coefficient out.
Further, the calculation formula replacement of the average step length is as follows:
The beneficial effects of the invention are that calculating the step-size estimation model for adapting to different people, it is finally reached step-size estimation
Trueness error less than 5%.
Detailed description of the invention
Fig. 1 is technical solution of the present invention figure.
Specific embodiment
Hereinafter, the present invention is further elaborated in conjunction with the accompanying drawings and embodiments.As shown in Figure 1, the present invention leads to mobile phone
The characteristic cadence of low-pass filtering acquisition is crossed, accelerometer variance and average step length are sent to cloud service by network
Device, server estimates step-length model by gradient descent algorithm beyond the clouds, and step-length model parameter is then sent to mobile phone, real
Existing cloud server integration, the specific steps are as follows:
Step S1, the accelerometer of mobile phone are used to measure acceleration.When user be in walk or running state under, with
Trunk or arms swing and generate regularity signal.By traditional pedometer principle can calculate each step it
Between time interval, i.e. cadence；Accelerometer standard deviation s and accelerometer variance in 1 second time are calculated by sliding window
(accelerometer variance is s2), wherein accelerometer standard deviation s calculation formula is as follows:
Wherein, axi, ayiAnd aziRespectively represent the x-axis that acceleration measuring measures, the acceleration of y-axis and z-axis；μ is the earth
Acceleration of gravity, value 9.8m/s2；N indicates the quantity for the acceleration that acceleration measuring measures.
Step S2, according to the position location of satellite navigation system (GPS or BD (Beidou)) calculate two o'clock P1 and P2 it
Between distance, it is assumed that P1 point longitude and latitude be (N1, E1), P2 point longitude and latitude be (N2, E2), the distance between P1 point and P2 point calculate
Formula is as follows:
Wherein, a=6378137.0, b=6356752.3142, dis are the distance between P1 point and P2 point.
Step S3 calculates the step number detected in any two time interval according to pedometer principle, if at the T1 moment
Position location is P1 point, and the step number of pedometer output is L1；It is P2 point in the moment position location T2, and pedometer exports
Step number be L2, then the average step length in T1 and T2 time interval can be calculated, calculation formula is as follows:
Wherein, Stride is average step length.
Optionally, the calculation formula replacement of the average step length is as follows:
Optionally, the calculation formula of the average step length replaces with, and calculates cadence according to pedometer, utilizes least square
Fit the linear relationship of cadence and average step length.
Step S4, on mobile phone by cadence, accelerometer variance and average step length by cell phone network (wifi, 2G, 3G or
Person 4G) it is sent to cloud server.Cloud server calculates step-length mould by gradient descent algorithm using the data received
Type, and the parameter of step-length model is sent to mobile phone, step-length model calculation formula is as follows:
Y=a0+a1*f+a2*f2+a3*v+a4*v2+a5*e-f
Wherein f indicates cadence；V indicates accelerometer variance；A0, a1, a2, a3, a4, a5 are obtained by gradient descent algorithm
Coefficient out.
In the present invention, algorithm realization is carried out using C language, and is handled by test data and demonstrates the performance of algorithm.
Although the invention has been described by way of example and in terms of the preferred embodiments, but it is not for limiting the present invention, any this field
Technical staff without departing from the spirit and scope of the present invention, may be by the methods and technical content of the disclosure above to this hair
Bright technical solution makes possible variation and modification, therefore, anything that does not depart from the technical scheme of the invention, and according to the present invention
Technical spirit any simple modifications, equivalents, and modifications to the above embodiments, belong to technical solution of the present invention
Protection scope.
Claims (5)
1. a kind of cloud step-size estimation method, which comprises the following steps:
The acceleration measuring of step S1, mobile phone measure acceleration；It is calculated according to pedometer principle between the time between each step
Every i.e. cadence；Calculate accelerometer standard deviation and accelerometer variance in 1 second time；
Step S2 calculates the distance between two o'clock according to the position location of satellite navigation system；
Step S3 calculates the step number detected in any two time interval according to pedometer principle；It calculates between two times
Every interior average step length；
Step S4, mobile phone send cloud server by network for cadence, accelerometer variance and average step length；Cloud service
Device calculates step-length model, and the parameter of step-length model is sent to mobile phone；
Network includes wifi, 2G, 3G or 4G in the step S4, and cloud server is passed through under gradient using the data received
Drop algorithm calculates step-length model；
The calculation formula of the step-length model is as follows:
Y=a0+al*f+a2*f2+a3*v+a4*v2+a5*e-f
Wherein f indicates cadence；V indicates accelerometer variance；A0, a1, a2, a3, a4, a5 are obtained by gradient descent algorithm
Coefficient；
The calculation formula of the average step length are as follows:
2. a kind of cloud step-size estimation method as described in claim 1, which is characterized in that accelerometer mark in the step S1
Quasi- difference and accelerometer variance are calculated by sliding window.
3. a kind of cloud step-size estimation method as claimed in claim 2, which is characterized in that the accelerometer standard deviation is s,
Calculation formula is as follows:
Wherein, axi, ayiAnd aziRespectively represent the x-axis that acceleration measuring measures, the acceleration of y-axis and z-axis；μ is terrestrial gravitation
Acceleration, value 9.8m/s2；N indicates the quantity for the acceleration that acceleration measuring measures；
The accelerometer variance is s2。
4. a kind of cloud step-size estimation method as described in claim 1, which is characterized in that step S2 Satellite navigation system
System includes GPS or Beidou.
5. a kind of cloud step-size estimation method as described in claim 1, which is characterized in that two o'clock is respectively in the step S2
P1 and P2, P1 point longitude and latitude are (N1, E1), and P2 point longitude and latitude is (N2, E2), and the distance between P1 point and P2 point calculation formula are such as
Under:
Wherein, a=6378137.0, b=6356752.3142, dis are the distance between P1 point and P2 point.
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Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108692738A (en) * | 2018-03-30 | 2018-10-23 | 四川斐讯信息技术有限公司 | The computational methods and system of the bearing calibration of step-length and device, distance of running |
| CN110866419A (en) * | 2018-08-28 | 2020-03-06 | 北京嘀嘀无限科技发展有限公司 | Step length determination method, system and computer readable storage medium |
| CN109459028A (en) * | 2018-11-22 | 2019-03-12 | 东南大学 | A kind of adaptive step estimation method based on gradient decline |
| CN109959375A (en) * | 2019-02-27 | 2019-07-02 | 浙江大学 | A kind of acoustics amendment localization method based on error triggering calibration |
| CN111883226A (en) * | 2019-11-07 | 2020-11-03 | 马上消费金融股份有限公司 | Information processing and model training method, device, equipment and storage medium |
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Address after: 200438 9 / F, 10 / F, 11 / F, 12 / F, 38 Lane 1688, Guoquan North Road, Yangpu District, Shanghai Patentee after: QIANXUN SPATIAL INTELLIGENCE Inc. Address before: Room j165, 1st floor, building 64, 1436 Jungong Road, Yangpu District, Shanghai, 200433 Patentee before: QIANXUN SPATIAL INTELLIGENCE Inc. |