CN110487273A - A kind of indoor pedestrian track projectional technique of level meter auxiliary - Google Patents
A kind of indoor pedestrian track projectional technique of level meter auxiliary Download PDFInfo
- Publication number
- CN110487273A CN110487273A CN201910634626.0A CN201910634626A CN110487273A CN 110487273 A CN110487273 A CN 110487273A CN 201910634626 A CN201910634626 A CN 201910634626A CN 110487273 A CN110487273 A CN 110487273A
- Authority
- CN
- China
- Prior art keywords
- pedestrian
- mobile phone
- course
- range
- angle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Navigation (AREA)
Abstract
The invention belongs to indoor positioning fields, it is related to the technologies such as sensor signal processing, particle filter algorithm, pedestrian's dead reckoning, specially a kind of indoor pedestrian track projectional technique of level meter auxiliary, to overcome the problems, such as that mobile phone unsteady attitude bring course information is not available in traditional PDR method.The invention firstly uses level meters built in mobile phone to detect mobile phone posture, obtain reliable pedestrian's course information, then the Wi-Fi received signal strength for analyzing each AP obtains pedestrian's rough position, then whether turning action occurs using mobile phone Geomagnetism Information detection pedestrian, and practical map information update pedestrian course is combined, final pedestrian's cadence, step-length, three kinds of the course information of merging calculates pedestrian movement track；For relatively general mobile phone inertial navigation, the present invention does not need to keep mobile phone posture constant with respect to human body attitude during the navigation process, and real-time update pedestrian course is technically simple, practical, is suitable for the interior field crowdsourcing fingerprint Jian Ku.
Description
Technical field
The invention belongs to indoor positioning fields, are related to sensor signal processing, particle filter algorithm, pedestrian's dead reckoning
Technologies such as (Pedest rian Dead Reckoning, PDR), specially a kind of indoor pedestrian track of level meter auxiliary calculate
Method.
Background technique
In PDR technology, cadence, step-length, three kinds of course information are most important；Wherein, cadence, step-length research more at
It is ripe, and the accuracy of course information acquisition how is improved, it is still research hotspot.Currently, mainstream thinking is by Quaternion Method, knot
It closes gyroscope information and resolves course, this method requires mobile phone to be always maintained at the fixed flush end posture of opposite human body, and operand is big, firmly
Part requires height, is easy accumulated error, causes course information unavailable, and then PDR algorithm is caused to fail.
The course measuring method of mainstream usually requires that mobile phone is always maintained at fixed flush end posture at present, to guarantee ground magnetic number
According to gyro data is accurate, the calculating suitable for quaternary counting method.But under reality, mobile phone posture multiplicity may beat electricity
Words, may put in packet, may lay flat and see video, and this restrictive condition has seriously affected user experience, so that mobile phone PDR is calculated at present
Method does not have practical application value.
Indoors in environment, pedestrian movement's state is generally divided into the walking of long-time straight line and short time right-angled bend.Research
It was found that it is fuselage is laid flat, head is facing forward posture that mobile phone is the most common, at this point, the Geomagnetism Information that mobile phone obtains can be preferable
Characterization pedestrian course.Document " A.Rai, K.K.Chintalapudi, V.N.Padmanabhan, and R.Sen, " Zee:
Zero-effort crowdsourcing for indoor localization,”in Proc.ACM MobiCom,2012,
Pp.293-304. " is pointed out, in indoor environment, wall, corridor can be with auxiliary positionings.According to above-mentioned background, the invention proposes one
The pedestrian movement track projectional technique of kind fusion actual environment information.
Summary of the invention
It is an object of the invention to not available for mobile phone unsteady attitude bring course information in tradition PDR method
Problem proposes a kind of indoor pedestrian track projectional technique of level meter auxiliary；This method is first with level meter built in mobile phone
Mobile phone posture is detected, reliable pedestrian's course information is obtained, then analyzes the Wi-Fi received signal strength (Received of each AP
Signal St rength, RSS) pedestrian's rough position is obtained, then whether occur to turn using mobile phone Geomagnetism Information detection pedestrian
Curved movement, and practical map information update pedestrian course is combined, final pedestrian's cadence, step-length, three kinds of the course information of merging calculates
Pedestrian movement track.
To achieve the above object, the technical solution adopted by the present invention are as follows:
A kind of indoor pedestrian track projectional technique of level meter auxiliary, includes step in detail below:
Step 1. data prediction
Data are obtained by acceleration transducer, gyro sensor, geomagnetic sensor, gravity sensor, and by gained
To data do filtering processing；
Step 2. calculates cadence
The pretreatment of 2-1. acceleration information, is arranged sliding time window Δ T, sample frequency f；
Each sampled point is calculated:Wherein, ax、ay、azThe respectively acceleration of X-axis, Y-axis, Z axis
Degree, is obtained by acceleration transducer measurement；
2-2. searches for all wave crests in sliding time window Δ T, and successively marks；
2-3. removes spurious peaks, obtains all Valid peaks in sliding time window Δ T: location=[l1,l2,
l3,...,li,...,ln,]T, n is Valid peak sum, then the step number in sliding time window Δ T is n；Using three threshold methods
Spurious peaks are removed, specifically:
2-3-1. setting resultant acceleration threshold value is 1.2g: as resultant acceleration a < 1.2g, regarding as spurious peaks；
Wave crest threshold value is arranged in 2-3-2.: calculating the average value A of three maximum values in time windowmax, three minimum values
Average value Amin, take AmaxAnd AminAverage value average be wave crest threshold value, when be less than wave crest threshold value when, regard as spurious peaks；
Sampled point threshold value M:M=0.2f is arranged in 2-3-3.；When sampling number is less than M between two neighboring wave crest, the is assert
Two wave crests are spurious peaks；Otherwise, two wave crests are Valid peak；
Step 3. material calculation
3-1. calculatingThe starting point of the i-th step is obtained, while as the (i-1)-th step
Terminating point；And then obtain all starting points, the set of terminating point:
RANGE=[range_1, range_2 ..., range_i ..., range_n, range_L]T；
Wherein, range_1 indicates the 1st sampled point, the starting point as step 1 in sliding time window Δ T, range_
L indicates last 1 sampled point in sliding time window Δ T, the terminating point as the n-th step；
3-2. calculates the step-length of each step:
Wherein, QiFor the total number of sample points of the i-th step, aqIndicate the resultant acceleration of q-th of sampled point in the i-th step, k, b are pre-
If constant；
Step 4. is based on level meter auxiliary and calculates pedestrian's course angle
4-1. is by corresponding timestamp t of each moment, azimuth angle alpha, pitch angle β, roll angle χ, X-axis weight component Gx, Y-axis
Weight component Gy, Z axis weight component GzForm vector: GRAND=[t, α, β, χ, Gx,Gy,Gz]T；Wherein, azimuth angle alpha is by earth magnetism
Sensor measurement obtains, and pitch angle β, roll angle χ are obtained by gyrosensor measurement, X-axis weight component Gx, Y-axis weight component
Gy, Z axis weight component GzIt is obtained by gravity sensor measurement；
For moment t, if meeting condition: pitch angle β, roll angle χ are respectively less than preset threshold βth, and gravity X-axis component Gx
Less than preset threshold Gth, then assert that moment t mobile phone is in and lay flat posture；It is detected since zero moment, at continuously detection mobile phone
It is more than time threshold t in laying flat postureth, then realize efficiently sampling, moment all in the period azimuthal average value made
To predict initial pedestrian's course angle；
4-2. combination indoor map and AP are laid out to obtain all possible courses；To predict initial pedestrian's course angle and it is all can
Energy course is compared, and is obtained and is predicted the immediate possible course of initial pedestrian's course angle, using the possible course as initial pedestrian
Direction；
5. turning detection
5-1. sets acquisition window Δ Torien, calculate each acquisition window Δ TorienThe average value of range inner orientation angle α
orien；
5-2. calculates the amplitude of variation of former and later two orien, if more than 80 degree of threshold value being judged to that turning action occurs；
The signal strength of all AP in 5-3. real-time detection pedestrian position selectes the inflection point where the maximum AP of signal strength
It for the immediate inflection point of moment pedestrian of turning, is turned to closest at inflection point to the maximum AP of signal strength, and updates pedestrian's boat
To angle；
Cadence that step 6. is obtained according to step 2~step 5, step-length, pedestrian course angle information, using particle filter algorithm
Obtain pedestrian track reckoning.
The beneficial effects of the present invention are:
The present invention provides a kind of indoor pedestrian track projectional technique of level meter auxiliary, is detected by level meter built in mobile phone
Mobile phone posture, when detect mobile phone be in lay flat posture when, record geomagnetic data at this time as pedestrian course, and edge always
It uses before detecting that turning action occurs for pedestrian；It is strong first with AP signal after detecting that turning action occurs for pedestrian
Information is spent, it is rough to obtain pedestrian's approximate location, it is matched to the turning that pedestrian is currently located, then combining cartographic information, it will be current
Direction after the turning at turning where pedestrian course is automatically matched to pedestrian, and be in use to next turning action always and it occurs
Before, such iteration, real-time update pedestrian's course information.For relatively general mobile phone inertial navigation, the present invention is in navigation procedure
In do not need to keep mobile phone posture constant with respect to human body attitude, by merging RSS signal strength and particle filter algorithm, in real time more
New pedestrian course, it is technically simple, it is practical, it is suitable for the interior field crowdsourcing fingerprint Jian Ku.
Detailed description of the invention
Fig. 1 is the flow diagram of the indoor pedestrian track projectional technique of level meter of the present invention auxiliary.
Fig. 2 is that the present invention is based on the courses of level meter to update algorithm flow chart.
Fig. 3 is level meter detection performance figure in the embodiment of the present invention.
Fig. 4 is experiment scene schematic diagram in the embodiment of the present invention.
Fig. 5 is mobile phone posture schematic diagram in the embodiment of the present invention.
Specific embodiment
Technical solution of the present invention is described in further detail with example with reference to the accompanying drawing.
The present embodiment provides a kind of indoor pedestrian track projectional technique of level meter auxiliary, process is as shown in Figure 1, specific
The following steps are included:
1. data prediction
Interface registration acceleration transducer, gyro sensor, geomagnetic sensor, the gravity provided using Android system is passed
The monitoring of sensor monitors the data variation for obtaining three sensors in real time, and obtained data is done filtering processing；
When the user is mobile, using acceleration transducer, gyro sensor, geomagnetic sensor, obtain user cadence,
Step-length and direction angle information, to obtain being used as a result, merging each data finally by particle filter algorithm for PDR positioning
The best orientation result at family；
2. three threshold methods survey cadence
The pretreatment of 2-1. acceleration information, setting Δ T is a sliding time window, hereafter in the time of a Δ T
Analyze step number, step-length, course information in range, in the present embodiment, Δ T=3 seconds；
It takesFor treated acceleration information, wherein ax、ay、azRespectively X-axis, Y-axis, Z axis
Acceleration is obtained by acceleration transducer measurement；The time range that normal person takes a step forward is 0.2s to 1s, many experiments hair
It is existing, sample frequency is set as 50Hz, pedestrian's cadence state can be effectively portrayed by acceleration information, avoids step number missing inspection problem
Appearance, therefore in the present embodiment, sample frequency is set as 50Hz；
2-2. successively analyzes each acceleration sample point data in Δ T range, if current acceleration sampled point is greater than
It is previous and be less than the latter sampled point, then remember current sampling point position be a wave crest, successively obtain in Δ T range
All crest locations；
2-3. analyzes all wave crest data by three threshold methods, removes spurious peaks；Specifically:
2-3-1. setting resultant acceleration threshold value is 1.2g: as resultant acceleration a < 1.2g, regarding as spurious peaks；
Wave crest threshold value is arranged in 2-3-2.: shaking interference is avoided, by the average value of three maximum values in a time window
It is set as Amax, the average value of three minimum values is set as Amin, take AmaxAnd AminAverage value average be wave crest threshold value, when being less than
When wave crest threshold value, spurious peaks are regarded as；
It is sample frequency that sampled point threshold value M:M=0.2f, f, which is arranged, in 2-3-3.；From previous wave crest to next wave crest,
It is exactly at least to be spaced 0.2 second between 2 steps, i.e. corresponding 10 sampled points in the case where 50Hz sample rate；When two neighboring wave crest
Between sampling number be less than M, assert second wave crest be spurious peaks；Otherwise, two wave crests are Valid peak；
2-4. obtains all Valid peaks in Δ T time section: location=[l by " three threshold methods "1,l2,l3,...,
li,...,ln,]T, n is Valid peak sum, then the step number in Δ T time section is n；
3. equation, which surveys step-length, analyzes the starting and end time of each step by Valid peak position；
3-1. calculatingThe starting point of the i-th step is obtained, while as the (i-1)-th step
Terminating point；
RANGE=[range_1, range_2 ..., range_i ..., range_n, range_L]T；
It should be noted that range_1 indicates the 1st sampled point, the starting point as step 1 in Δ T time section, range_
2 be the terminating point of step 1, also as the starting point of step 2, and so on, starting point of the range_n as the n-th step,
Range_L indicates last 1 sampled point in Δ T time section, the terminating point as the n-th step；
3-2. calculates the step-length of each step, and step-length measurement mainly utilizes the linear dependence between step-length and acceleration:
Wherein, QiFor the total number of sample points (including two-end-point) of the i-th step, aqIndicate that the conjunction of q-th of sampled point in the i-th step adds
Speed, k, b are fitted to obtain for preset constant, by least square method；
4. pedestrian's course angle algorithm based on level meter auxiliary
During the motion, mobile phone attitudes vibration amplitude is very big by pedestrian, would be likely placed at pocket, electricity may be answered in one's ear
Words, may be difficult to the accurate actual direction of advance of real-time characterization pedestrian with arms swing, geomagnetic data；As shown in fig. 5-1, divide
Different mobile phone posture discoveries are analysed, for pedestrian when reading screen using mobile phone, mobile phone, which is generally in, lays flat posture, and head is facing forward, this
When geomagnetic data absolute course value can be provided, and need not move through integral operation, it is actual can preferably to characterize pedestrian
Direction of advance；And indoors in environment, pedestrian movement's state can be divided into the walking of long range straight line and two kinds of short time right-angled bend
Principal states, if can detect, mobile phone lays flat posture, and bearing data at this time, which can be in use to always, detects that pedestrian occurs to turn
Before curved movement, accordingly, limited in conjunction with objective condition such as indoor wall, corridors, by particle filter algorithm, can preferably with
Track pedestrian movement track；
Level meter effect is whether detection mobile phone is in horizontal positioned posture during exercise, if so, earth magnetism at this time
Bearing data can preferably symbolize the direction of motion of pedestrian, and detailed process is as follows:
Android can call directly geomagnetic sensor measurement azimuth, and gyrosensor measures pitch angle, roll angle,
Wherein, course angle is the direction of advance (- 180 degree to 180 degree) of geomagnetic data, and pitch angle indicates that mobile phone narrow side is lifted or put down
Angle (- 90 degree to 90 degree), roll angle indicates the angle that mobile phone broadside is lifted or put down (- 90 spend to 90 degree)；
Mobile phone can also call directly gravity in the three axis weight components that terrestrial coordinates is fastened by the regulation of Android and pass
Sensor measurement obtains, if mobile phone is in the state fully horizontally placed, X-axis, the weight component of Y-axis are zero, the gravity point of Z axis
Amount is g (taking 9.8)；Mobile phone narrow side is erect, and upward, then Y-axis weight component is g to head, remaining two durection component is zero；Similarly,
The machine width avris that can go smoothly is vertical, and X-axis weight component is g, remaining two axis component is zero (opposite direction is exactly-g)；
As a result, in order to detect that mobile phone lays flat posture, the present invention proposes two data threshold, time threshold standards:
Data threshold: when mobile phone is laid flat, roll angle, pitch angle should be less than βth, in terms of three axis weight components, user is being used
When mobile phone, it is impossible to it is completely level, such as Fig. 5-1,5-3, but mostly mobile phone narrow side and horizontal direction generate angular error,
Mobile phone broadside is less to generate angular error with horizontal direction, such as Fig. 5-2,5-4, so, the X-axis of weight component, i.e. mobile phone narrow side
The influence for swinging up and down angle and gravity being generated, the judgement for laying flat posture for mobile phone is very sensitive；So X-axis weight component
It should be less than threshold value Gth；When simultaneously meet roll angle, pitch angle is respectively less than βthIt should be less than threshold value G with X-axis weight componentthWhen, it is believed that
Mobile phone is in and lays flat posture；In the present embodiment, βthFor 10-20 degree, GthIt is 3；
Time threshold: mobile phone in pocket, with arms swing when, in fact it could happen that it is of short duration to lay flat posture, the duration compared with
Short, due to the influence of sampling error, magnetic azimuth data at this time are insincere；So only detect mobile phone be in lay flat
Posture, and after continue for a period of time, magnetic azimuth data at this time could preferably characterize pedestrian movement direction；Therefore,
Set duration threshold tth；In the present embodiment, tthIt is 3 seconds；As shown in figure 3, in the present embodiment, in sampled point 210 to sampling
In the period of point 500, mobile phone posture is detected as the placement of horizontal flush end, and earth magnetism course information at this time also maintains stabilization can
With, it was demonstrated that detection is effective；
4-1. collects each moment, corresponding timestamp t, azimuth angle alpha, pitch angle β, roll angle χ, gravity X-axis component Gx、
Y-axis component Gy, z-component Gz；Form vector GRAND=[t, α, β, χ, Gx,Gy,Gz]T；
If the pitch angle β of 4-2. moment t, roll angle χ are less than threshold value betath, gravity X-axis component GxLess than threshold value Gth, then clock
Carve the mark flag of tt=1；
4-3. is with sample frequency 50Hz times, if detecting the continuous 150 (flag of 1000t/ Δ t) sampled pointt=1, then
It is denoted as efficiently sampling, calculates the azimuth average value Δ α of this time as the initial pedestrian's deflection of prediction；
4-4. combination indoor map and AP are laid out to obtain all possible courses, and cache；
In the present embodiment, set environment is as shown in figure 4, be arranged four AP in four inflection points of rectangle；Each edge corresponds to positive and negative
Both direction, i.e. pedestrian cache this four kinds of courses there may be four kinds of courses；When detecting that user's level meter information can be used,
Initial pedestrian's deflection will be predicted compared with caching four kinds of courses, obtain immediate possible course, using the possible course as
Initial pedestrian direction；
5. turning detection
5-1. setting time window delta Torien, real-time acquisition window Δ TorienAzimuth angle alpha in range, takes mean value to obtain
orien；
5-2. calculates the amplitude of variation of former and later two orien, if more than 80 degree of threshold value being judged to that turning action occurs；
As shown in figure 3, earth magnetism compass data have the variation of noticeable steps formula near sampled point 900, it is judged as generation turning action；
The signal strength of four inflection point AP in 5-3. real-time detection pedestrian position, turning where the maximum AP of signal strength
Point, the as immediate inflection point of pedestrian at this time, closest to being turned to the maximum AP of signal strength at inflection point；And update pedestrian side
To；
6. the PDR dead reckoning based on particle filter algorithm has obtained user movement process by the data of above-mentioned collection
In complete and corresponding with moment cadence, step-length, pedestrian's directional information, calculation is merged with map below with particle filter algorithm
Method draws user movement track；Detailed process are as follows:
Particle carries 5 parameters, p=(x, y, t, w, l, θ), wherein x, y respectively represent cross of the particle in map,
Ordinate；T represents the moment；A possibility that w represents the weight of the particle, and weight is bigger, represents pedestrian in the position is bigger；L generation
Table moment pedestrian's moving step length；θ represents pedestrian direction this moment；
6-1. is carved at the beginning, and the weight for setting all particles is identical；After pedestrian enters the room space, particle filter is opened
Beginning iteration, the physical location of the particle of different location, weight are according to the continuous iteration of following algorithm；
The variation of 6-2. particle position is determined according to cadence, step number, pedestrian's directional information:
Wherein, lkIndicate the step-length of kth step, θkIndicate that kth walks the course value in range, xk、ykIndicate kth step in map X
Displacement on axis, Y direction；
6-3. is since the interior space limits, and variation track and wall lines have intersection point, weight meeting before and after particle position
By zero setting immediately:
After each iteration of 6-4., to particle carry out resampling, the particle of low weight be considered apart from real user position compared with
Far, limited particle can be concentrated in the higher region of confidence level by redistributing particle, obtain the new weight of all particles, meter
It calculatesAbscissa after obtaining iteration, ordinate is similarly；The bigger place of weight is exactly that pedestrian is more likely to occur
Place；
6-5. binding time information, obtains the motion track of pedestrian.
It should be noted that above-mentioned steps 3, step 4 can be with parallel processings.
The above description is merely a specific embodiment, any feature disclosed in this specification, except non-specifically
Narration, can be replaced by other alternative features that are equivalent or have similar purpose；Disclosed all features or all sides
Method or in the process the step of, other than mutually exclusive feature and/or step, can be combined in any way.
Claims (1)
1. a kind of indoor pedestrian track projectional technique of level meter auxiliary, includes step in detail below:
Step 1. data prediction
Data are obtained by acceleration transducer, gyro sensor, geomagnetic sensor, gravity sensor, and will be obtained
Data do filtering processing；
Step 2. calculates cadence
The pretreatment of 2-1. acceleration information, is arranged sliding time window Δ T, sample frequency f；
Each sampled point is calculated:Wherein, ax、ay、azRespectively X-axis, Y-axis, Z axis acceleration,
It is obtained by acceleration transducer measurement；
2-2. searches for all wave crests in sliding time window Δ T, and successively marks；
2-3. removes spurious peaks, obtains all Valid peaks in sliding time window Δ T: location=[l1,l2,l3,...,
li,...,ln,]T, n is Valid peak sum, then the step number in sliding time window Δ T is n；Pseudo wave is removed using three threshold methods
Peak, specifically:
2-3-1. setting resultant acceleration threshold value is 1.2g: as resultant acceleration a < 1.2g, regarding as spurious peaks；
Wave crest threshold value is arranged in 2-3-2.: calculating the average value A of three maximum values in time windowmax, three minimum values are averaged
Value Amin, take AmaxAnd AminAverage value average be wave crest threshold value, when be less than wave crest threshold value when, regard as spurious peaks；
Sampled point threshold value M:M=0.2f is arranged in 2-3-3.；When sampling number is less than M, identification second between two neighboring wave crest
Wave crest is spurious peaks；Otherwise, two wave crests are Valid peak；
Step 3. material calculation
3-1. calculatingObtain the starting point of the i-th step, while the end as the (i-1)-th step
Stop；And then obtain all starting points, the set of terminating point:
RANGE=[range_1, range_2 ..., range_i ..., range_n, range_L]T；
Wherein, range_1 indicates the 1st sampled point, the starting point as step 1 in sliding time window Δ T, range_L table
Show last 1 sampled point, the terminating point as the n-th step in sliding time window Δ T；
3-2. calculates the step-length of each step:
Wherein, QiFor the total number of sample points of the i-th step, aqIndicate the resultant acceleration of q-th of sampled point in the i-th step, k, b are default normal
Number；
Step 4. is based on level meter auxiliary and calculates pedestrian's course angle
4-1. is by corresponding timestamp t of each moment, azimuth angle alpha, pitch angle β, roll angle χ, X-axis weight component Gx, Y-axis gravity
Component Gy, Z axis weight component GzForm vector: GRAND=[t, α, β, χ, Gx,Gy,Gz]T；Wherein, azimuth angle alpha is sensed by earth magnetism
Device measurement obtains, and pitch angle β, roll angle χ are obtained by gyrosensor measurement, X-axis weight component Gx, Y-axis weight component Gy, Z axis
Weight component GzIt is obtained by gravity sensor measurement；
For moment t, if meeting condition: pitch angle β, roll angle χ are respectively less than preset threshold βth, and gravity X-axis component GxIt is less than
Preset threshold Gth, then assert that moment t mobile phone is in and lay flat posture；It is detected since zero moment, when continuously detection mobile phone is in flat
Putting posture is more than time threshold tth, then efficiently sampling is realized, using moment all in the period azimuthal average value as pre-
Survey initial pedestrian's course angle；
4-2. combination indoor map and AP are laid out to obtain all possible courses；It will predict initial pedestrian's course angle and all possible boats
To comparing, obtains and predict the immediate possible course of initial pedestrian's course angle, using the possible course as initial pedestrian direction；
Step 5. turning detection
5-1. sets acquisition window Δ Torien, calculate each acquisition window Δ TorienThe average value orien of range inner orientation angle α；
5-2. calculates the amplitude of variation of former and later two orien, if more than 80 degree of threshold value being judged to that turning action occurs；
The signal strength of all AP in 5-3. real-time detection pedestrian position, the inflection point where selecting the maximum AP of signal strength are to turn
The curved immediate inflection point of moment pedestrian turns to the maximum AP of signal strength closest at inflection point, and updates pedestrian's course angle；
Cadence that step 6. is obtained according to step 2~step 5, step-length, pedestrian course angle information, are obtained using particle filter algorithm
Pedestrian track calculates.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910634626.0A CN110487273B (en) | 2019-07-15 | 2019-07-15 | Indoor pedestrian trajectory calculation method assisted by level gauge |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910634626.0A CN110487273B (en) | 2019-07-15 | 2019-07-15 | Indoor pedestrian trajectory calculation method assisted by level gauge |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110487273A true CN110487273A (en) | 2019-11-22 |
| CN110487273B CN110487273B (en) | 2022-11-08 |
Family
ID=68546966
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910634626.0A Active CN110487273B (en) | 2019-07-15 | 2019-07-15 | Indoor pedestrian trajectory calculation method assisted by level gauge |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110487273B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111189443A (en) * | 2020-01-14 | 2020-05-22 | 电子科技大学 | Pedestrian navigation method for online step length calibration, motion deviation angle correction and adaptive energy management |
| CN111595344A (en) * | 2020-06-01 | 2020-08-28 | 中国矿业大学 | Multi-posture downlink pedestrian dead reckoning method based on map information assistance |
| CN113114850A (en) * | 2021-03-18 | 2021-07-13 | 电子科技大学 | Online fusion positioning method based on surveillance video and PDR |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005091113A2 (en) * | 2004-03-12 | 2005-09-29 | Vectronix Ag | Apparatus and method of determining 3d pedestrian motion by using projection planes |
| JP2006194725A (en) * | 2005-01-13 | 2006-07-27 | Hitachi Metals Ltd | Attitude detection method using biaxial magnetometric sensor, and its program |
| US20100145654A1 (en) * | 2007-11-19 | 2010-06-10 | Jin Sang Hwang | Apparatus of chasing posture of moving material object, method of chasing posture of moving material object, apparatus of chasing posture of toothbrush and method of chasing posture of toothbrush using the samelectric toothbrush and method for controlling thereof |
| CN104266646A (en) * | 2014-09-11 | 2015-01-07 | 北京邮电大学 | Method and device for determining movement direction of terminal |
| CN105241478A (en) * | 2015-10-13 | 2016-01-13 | 中国船舶重工集团公司第七一七研究所 | Uniaxial modulation laser gyroscope inertial navigation system multi-coordinate system calibration method |
| CN106370178A (en) * | 2015-07-21 | 2017-02-01 | 阿里巴巴集团控股有限公司 | Mobile terminal equipment attitude measurement method and mobile terminal equipment attitude measurement apparatus |
| CN106643711A (en) * | 2016-11-25 | 2017-05-10 | 北京邮电大学 | Indoor positioning method and indoor positioning system based on hand-held device |
| CN106705968A (en) * | 2016-12-09 | 2017-05-24 | 北京工业大学 | Indoor inertial navigation algorithm based on posture recognition and step length model |
| CN107084718A (en) * | 2017-04-14 | 2017-08-22 | 桂林电子科技大学 | Indoor orientation method based on pedestrian's reckoning |
| CN108225324A (en) * | 2017-12-27 | 2018-06-29 | 中国矿业大学 | A kind of indoor orientation method merged based on the geomagnetic matching of intelligent terminal with PDR |
| CN108632761A (en) * | 2018-04-20 | 2018-10-09 | 西安交通大学 | A kind of indoor orientation method based on particle filter algorithm |
| CN108917760A (en) * | 2018-04-25 | 2018-11-30 | 西安交通大学 | A kind of pedestrian's dead reckoning course corner correcting method based on compensating of the earth magnetic field |
| CN109211229A (en) * | 2018-08-26 | 2019-01-15 | 桂林电子科技大学 | A kind of personnel's indoor orientation method based on mobile phone sensor and WiFi feature |
| CN109460040A (en) * | 2018-12-28 | 2019-03-12 | 珠海凯浩电子有限公司 | It is a kind of that map system and method are established by mobile phone shooting photo array floor |
-
2019
- 2019-07-15 CN CN201910634626.0A patent/CN110487273B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005091113A2 (en) * | 2004-03-12 | 2005-09-29 | Vectronix Ag | Apparatus and method of determining 3d pedestrian motion by using projection planes |
| JP2006194725A (en) * | 2005-01-13 | 2006-07-27 | Hitachi Metals Ltd | Attitude detection method using biaxial magnetometric sensor, and its program |
| US20100145654A1 (en) * | 2007-11-19 | 2010-06-10 | Jin Sang Hwang | Apparatus of chasing posture of moving material object, method of chasing posture of moving material object, apparatus of chasing posture of toothbrush and method of chasing posture of toothbrush using the samelectric toothbrush and method for controlling thereof |
| CN104266646A (en) * | 2014-09-11 | 2015-01-07 | 北京邮电大学 | Method and device for determining movement direction of terminal |
| CN106370178A (en) * | 2015-07-21 | 2017-02-01 | 阿里巴巴集团控股有限公司 | Mobile terminal equipment attitude measurement method and mobile terminal equipment attitude measurement apparatus |
| CN105241478A (en) * | 2015-10-13 | 2016-01-13 | 中国船舶重工集团公司第七一七研究所 | Uniaxial modulation laser gyroscope inertial navigation system multi-coordinate system calibration method |
| CN106643711A (en) * | 2016-11-25 | 2017-05-10 | 北京邮电大学 | Indoor positioning method and indoor positioning system based on hand-held device |
| CN106705968A (en) * | 2016-12-09 | 2017-05-24 | 北京工业大学 | Indoor inertial navigation algorithm based on posture recognition and step length model |
| CN107084718A (en) * | 2017-04-14 | 2017-08-22 | 桂林电子科技大学 | Indoor orientation method based on pedestrian's reckoning |
| CN108225324A (en) * | 2017-12-27 | 2018-06-29 | 中国矿业大学 | A kind of indoor orientation method merged based on the geomagnetic matching of intelligent terminal with PDR |
| CN108632761A (en) * | 2018-04-20 | 2018-10-09 | 西安交通大学 | A kind of indoor orientation method based on particle filter algorithm |
| CN108917760A (en) * | 2018-04-25 | 2018-11-30 | 西安交通大学 | A kind of pedestrian's dead reckoning course corner correcting method based on compensating of the earth magnetic field |
| CN109211229A (en) * | 2018-08-26 | 2019-01-15 | 桂林电子科技大学 | A kind of personnel's indoor orientation method based on mobile phone sensor and WiFi feature |
| CN109460040A (en) * | 2018-12-28 | 2019-03-12 | 珠海凯浩电子有限公司 | It is a kind of that map system and method are established by mobile phone shooting photo array floor |
Non-Patent Citations (9)
| Title |
|---|
| A. RAI等: "Zero-effort crowdsourcing for indoor localization", 《PROCEEDINGS OF THE 18TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING》 * |
| BOYUAN WANG等: "Posture Recognition and Adaptive Step Detection Based on Hand-held Terminal", 《2018 UBIQUITOUS POSITIONING, INDOOR NAVIGATION AND LOCATION-BASED SERVICES (UPINLBS)》 * |
| XUMENG GUO等: "WiMag: Multimode Fusion Localization System based on Magnetic/WiFi/PDR", 《2016 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN)》 * |
| 周瑞等: "基于粒子滤波和地图匹配的融合室内定位", 《电子科技大学学报》 * |
| 庄翠翠等: "基于Android系统的多传感器移动体感应用", 《计算机系统应用》 * |
| 李一帆: "基于惯性传感器的行人室内轨迹推算与定位算法研究", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》 * |
| 毛旭: "基于移动终端的行人惯性导航定位研究", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》 * |
| 王亚娜等: "基于粒子滤波的多信息融合室内定位算法研究", 《电子技术应用》 * |
| 赵玉乐: "基于MEMS惯性器件的室内行人定位方法研究", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111189443A (en) * | 2020-01-14 | 2020-05-22 | 电子科技大学 | Pedestrian navigation method for online step length calibration, motion deviation angle correction and adaptive energy management |
| CN111595344A (en) * | 2020-06-01 | 2020-08-28 | 中国矿业大学 | Multi-posture downlink pedestrian dead reckoning method based on map information assistance |
| CN111595344B (en) * | 2020-06-01 | 2023-02-24 | 中国矿业大学 | Multi-posture downlink pedestrian dead reckoning method based on map information assistance |
| CN113114850A (en) * | 2021-03-18 | 2021-07-13 | 电子科技大学 | Online fusion positioning method based on surveillance video and PDR |
| CN113114850B (en) * | 2021-03-18 | 2021-09-21 | 电子科技大学 | Online fusion positioning method based on surveillance video and PDR |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110487273B (en) | 2022-11-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105588566B (en) | A kind of indoor locating system merged based on bluetooth with MEMS and method | |
| US9696165B2 (en) | Last-mile navigation using smartphones | |
| CN108632761B (en) | Indoor positioning method based on particle filter algorithm | |
| CN105910601B (en) | A kind of indoor ground magnetic positioning method based on Hidden Markov Model | |
| CN104121905B (en) | Course angle obtaining method based on inertial sensor | |
| CN110487273A (en) | A kind of indoor pedestrian track projectional technique of level meter auxiliary | |
| CN106908060A (en) | A kind of high accuracy indoor orientation method based on MEMS inertial sensor | |
| CN110118549A (en) | A kind of Multi-source Information Fusion localization method and device | |
| CN105516929B (en) | Establish method, indoor orientation method and the corresponding intrument of indoor map data | |
| CN108225310A (en) | A kind of Gravity-aided navigation path planning method | |
| Yu et al. | Comparison of pedestrian tracking methods based on foot-and waist-mounted inertial sensors and handheld smartphones | |
| CN108844533A (en) | A kind of free posture PDR localization method based on Multi-sensor Fusion and attitude algorithm | |
| CN111829516B (en) | Autonomous pedestrian positioning method based on smart phone | |
| Chen et al. | Sensing strides using EMG signal for pedestrian navigation | |
| Park et al. | Robust pedestrian dead reckoning for multiple poses in smartphones | |
| Rehman et al. | Accurate and direct GNSS/PDR integration using extended Kalman filter for pedestrian smartphone navigation | |
| Gao et al. | XINS: The anatomy of an indoor positioning and navigation architecture | |
| CN104202818B (en) | A kind of floor recognition methods distance weighted based on building open edge | |
| CN110440746A (en) | A kind of no-dig technique subterranean drill bit posture fusion method based on the decline of quaternary number gradient | |
| Huang et al. | Smartphone-based indoor position and orientation tracking fusing inertial and magnetic sensing | |
| CN113325455B (en) | Method and system for tracking and determining indoor position of object | |
| CN111121758B (en) | Rapid modeling and credible positioning method for indoor magnetic map | |
| CN109099922A (en) | A kind of indoor orientation method based on physical field | |
| CN109725284B (en) | Method and system for determining a direction of motion of an object | |
| Chen et al. | An integrated GPS and multi-sensor pedestrian positioning system for 3D urban navigation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |