CN104180805B - Smart phone-based indoor pedestrian positioning and tracking method - Google Patents
Smart phone-based indoor pedestrian positioning and tracking method Download PDFInfo
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- CN104180805B CN104180805B CN201410438351.0A CN201410438351A CN104180805B CN 104180805 B CN104180805 B CN 104180805B CN 201410438351 A CN201410438351 A CN 201410438351A CN 104180805 B CN104180805 B CN 104180805B
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- 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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Abstract
The invention discloses a smart phone-based indoor pedestrian positioning and tracking method, comprising the following steps: leading in indoor map information by a smart phone, wherein an indoor map is a vector map; collecting current acceleration information, angular speed information and direction information of the smart phone, and collecting the strength of all indoor WIFI RSS signals; on the basis of PDR, calculating pedestrian steps, pedestrian step lengths and walking directions according to the sampled data; estimating the positions of pedestrians by using a particle filtering algorithm, judging and identifying a swerving scene, a room identification scene and a door detection scene on the basis of WIFI RSS to carry out auxiliary correcting and positioning; and displaying the positions of the pedestrians on the indoor map. The method is high in positioning accuracy and strong in anti-jamming capability, a decimeter-level positioning result can be obtained, the effect on the pedestrian action, caused by the positioning accuracy, is small, the functions of pedestrian swerving, room identification and door detection are effectively achieved, the excessive dependence on the deployment position of a WIFI router is avoided, and the labor consumption in the deployment process is reduced.
Description
Technical field
The present invention describes a kind of indoor pedestrian's positioning based on smart mobile phone and tracking.
Background technology
Gps satellite fix is the most common way obtaining positional information at present, but because satellite-signal is easily subject to respectively
Plant building to block or other factorses interference, gps location technology is not particularly suited for indoor scenarios or built-up complex environment.
With the development of wireless electron mobile communication technology, indoor positioning technologies will become the strong supplement of gps outdoor positioning, and it is permissible
Carry for various different application scenarios such as indoor rescue, market marketing place large-scale from passenger flow analysing, airport and hospital etc. navigation
For critical positions information service (lbs).
The indoor orientation method of current main-stream can be divided into two classes: the indoor positioning technologies based on pedestrian's dead reckoning (pdr)
(inertial navigation positioning) and the indoor positioning technologies Ji Yu rss (wireless reception of signals intensity).
Inertial navigation localization method is to gather monitoring sensing number using sensors such as accelerometer, gyroscope and magnetometers
According to, and the information such as estimating step length, speed and direction, and then realize indoor positioning and tracking.In the method implementation process, sensor
Easily affected by no intention actions various in pedestrian's walking process (as arm is shaken, turned round) and led to larger accumulation by mistake
Difference.
Indoor positioning technologies based on rss typically have two kinds: location technology based on rss model and based on rss fingerprint
Location technology.Uncertain influence factor based on rss model indoor positioning is more, and position error is larger, and needs to obtain
The accurate coordinates of wifi router (ap)；Localization method based on rss fingerprint is then fixing in wifi router (ap) position
In indoor environment, by the offline rss information gathering batch coordinate points, learning training draws wifi rss signal intensity and interior
Mapping library between coordinate position (distance), and then come according to the rss signal intensity real-time query mapping library collecting in walking
Obtain pedestrian's coordinate position indoors, the method depends on the precise deployment coordinate of ap, and positioning precision is subject to rss signal intensity
De-stabilising effect larger, rss signals collecting, off-line training and deployment phase all need larger manpower consumption.
Paper " a reliable and delivering in the famous international conference in general fit calculation field ubicomp 2012
Accurate indoor localization method using phone inertial sensors " employ particle filter
Wave method corrects the inertial navigation position error brought of smart mobile phone low side inertial sensor using cartographic information it is achieved that average
Precision the indoor pedestrian between 1.5-2 rice position and follow the trail of precision.The present invention, on the basis of this paper method, further will
Corresponding relation is set up in the behaviors such as the turning in indoor radio signal intensity change and pedestrian's gait processes relatively, entrance room, uses
In the inertial navigation position error correcting further in gait processes it is achieved that the positioning precision of decimeter grade.
However, existing indoor positioning technologies positioning precision is not high, and capacity of resisting disturbance is poor, and the deployment relying on router is close
Degree and concrete coordinate position, data acquisition and deployment need higher manpower consumption.
Content of the invention
It is an object of the present invention to provide a kind of indoor pedestrian's positioning based on smart mobile phone and tracking, the method is with particle
It is filtered into core, inertial navigation positioning correcting is completed based on the relative variation relation of wifi network wireless signal strength (rss), obtains
Decimeter grade hi-Fix result, thus the problems such as overcome existing indoor positioning technologies precision high.
A kind of indoor pedestrian's positioning based on smart mobile phone and tracking, comprising:
Step 1: smart mobile phone imports indoor map information, described indoor map is map vector；
Step 2: gather this smart mobile phone and work as using the embedded accelerometer of smart mobile phone, gyroscope, magnetometer sensor
Front acceleration information, angular velocity information and directional information, and gather indoor all wifi rss signal intensitys；
It is characterized in that comprising the following steps as follows:
Step 3: based on pdr (inertial navigation), according to the sensor sampled data calculate pedestrian's step, pedestrian's step-length and
Direction of travel；
Step 4: using the position of particle filter algorithm estimation pedestrian:
First, pedestrian's step-length, step and the direction of travel being calculated based on pdr, further according to cartographic information, wifi rss signal
The relative geological information of Strength Changes carries out particle weights distribution, completes pedestrian position eventually through resampling and estimates；
Step 5: pedestrian position is shown in indoor map.
Above-mentioned steps (4) particle filter algorithm estimates that the detailed process of the position of pedestrian is as follows:
(4.1) pedestrian's step, step-length and direction of travel information transmission that pdr (inertial navigation) estimates are filtered to particle
Ripple；
(4.2) particle moves according to the step-length of pdr and direction；
(4.3) particle filter obtains current step particle weights set, and the phase based on wifi rss change in signal strength
Auxiliary corrective positioning is effectively carried out to geometrical relationship；
(4.4) particle filter regenerates new particle.
In above-mentioned steps (4.3), positioned specific as follows based on wifi rss auxiliary corrective:
(4.3.1) judge correction scene,
If " turning scene ", then execute (4.3.2)；
If " room identification scene ", then execute (4.3.3)；
If " door detection scene ", then execute (4.3.4)；
(4.3.2) run to turn and confirm algorithm；
(4.3.3) run room recognizer；
(4.3.4) run door detection algorithm.
In above-mentioned wifi rss auxiliary corrective positioning, with running to turn, described " turning scene " identification confirms that algorithm is concrete such as
Under:
(1) when pedestrian's direction of travel that pdr (inertial navigation) estimates changes than the direction of front step, triggering turns
Curved confirmation, executes (2)～(5)；
(2) obtain the wifi rss vector direction between current step and previous step, be defined as rssdirection1；
(3) obtain the wifi rss vector direction between current step and front second step, be defined as
rssdirection2；
(4) calculate the angle changing between two vector direction of rssdirection1 and rssdirection2；
(5) judge whether the angle changing in (3) is less than predefined left-hand bend threshold value or is more than right-hand bend threshold value；If
Angle changing is less than left-hand bend threshold value or more than right-hand bend threshold value then it is assumed that current step does not occur real touch turn,
The direction of current step is replaced in direction using front second step；Otherwise then it is assumed that current step there occurs touch turn really,
Continue to keep the direction of current step；
In above-mentioned wifi rss auxiliary corrective positioning, described " room identification scene " identification has with running room recognizer
Body is as follows:
(1) pedestrian's step number, step-length and the direction of travel that particle filter deduces according to pdr (inertial navigation), and ground
Figure information, judges that whether current step (has door, whether judge the prediction run trace of current step through certain door above map
Pass through the door above map, through then thinking through certain door, that is, room detection identification scene is to see that current step inertial navigation is pre-
Whether the track surveyed passes through certain door, if passing through door, illustrates that pedestrian enters room, triggering room recognizer judges pedestrian
Which room enter is)；If passing through, illustrate that pedestrian is just entering room, now triggering room recognizer, ensuing
3 step scope interior circulation execution (2)～(6), beyond 3 step scopes, execute (7)；Otherwise, illustrate that pedestrian does not enter into the action in room
Behavior, keeps current step state, executes (6) (7)；
(2) travel through the wifi rss signal strength readings of all ap (router) that current step scans, search wifi
The maximum ap of rss reading, is defined as ap1, and temporarily thinks that pedestrian enters the room room1 at ap1 place；
(3) travel through the wifi rss signal strength readings of all ap that current step scans, search more previous step
The maximum ap of wifi rss reading increment, orientates ap2 as, and the room simultaneously defining ap2 place is room2；
(4) if ap2 is identical with ap1, judge that pedestrian enters the room at ap1 place, keep the state of current particle,
Execution (6)；Otherwise, the room that pedestrian is located is reaffirmed in execution (5)；
(5) particle filter combining cartographic information, corresponding with ap2 according to ap1 corresponding wifi rss absolute signal strengths
All particles are carried out weights distribution by the proportionate relationship of wifi rss absolute signal strengths again:
5.1 information according to the map, find the coincidence body of wall in room room1 and room2, with it, particle filter is all
Particle is divided into two set set1 and set2, and wherein set1 refers to the particle assembly in room1, and set2 refers to be located at
Particle assembly in room2；
During 5.2 definition current step, ap1 corresponding wifi rss signal intensity is rss1；Define the corresponding wifi of ap2
Rss signal intensity is rss2；
In 5.3 definition set set1, the weighting ratio coefficient ratio1 of particle is: (90+rss1)/(180+rss1+
rss2)；
In 5.4 definition set set2, the weighting ratio coefficient ratio2 of particle is (90+rss2)/(180+rss1+rss2)；
5.5 traversal particle assembly set1, the weight w of each particle are reset to: w*ratio1；
5.6 traversal particle assembly set2, the weight w of each particle are reset to: w*ratio2；
(6) particle filter regenerates new particle；
(7) terminate algorithm；
In above-mentioned wifi rss auxiliary corrective positioning, described " door detection scene " identification has with running door detection algorithm
Body is as follows:
(1) if the accumulation step number of pedestrian is more than or equal to 3 steps, (2)～(8) are executed；Otherwise, (8) are executed；
(2) obtain the particle filter predicted position coordinate points of current step and front 3 steps, be defined as estpos0 successively respectively,
Estpos1, estpos2, estpos3；
(3) estpos0 and estpos1, estpos1 and estpos2 and estpos2 and estpos3 position coordinateses are judged
Point the distance between whether all in predefined threshold range, if three above distance values are all in threshold range, and
And estpos0～estpos3 position coordinateses point is all located at the edge line of room wall nearby (within 0.5 meter), then execution (4)～
(8)；Otherwise execute (8)；
(4) traversal searches current step and wifi rss signal strength readings increment maximum during nearest front three step
Ap；
(5) entrance center position one weights of establishment in the room that particle filter is located in ap are 1 particle, and based on
The step-length of nearly three steps and direction of travel (being obtained by pdr) extrapolate action vector (run trace) of nearest three steps；
(6) particle filter regenerates new particle；
(7) particle filter execution room recognizer, further confirms that the room residing for pedestrian's particle；
(8) terminate algorithm.
Above turning confirms that the acquisition wifi rss vector direction described in algorithm is specific as follows:
(1) the wifi rss signal strength readings of smart mobile phone collection wifi router ap are stored in timestamp for index
In the data form of<rss1, rss2 ... rssn, time>, the present invention is called a rss vector；
(2) travel through the rss vector in current step moment, search corresponding in wherein rss reading more previous step rss vector
The ap set that rss reading increases, is defined as set (increase)；Search corresponding in rss reading more previous step rss vector
The ap set that rss reading reduces, is defined as set (decrease)
(3) positional information based on each ap place room, calculates set (increase) and set (decrease) respectively
Geographical geometric center；
(4) calculate between set (increase) geography geometric center and set (decrease) geography geometric center
Vector direction, i.e. current step corresponding rss vector direction.
Described predefined left-hand bend threshold value be (0,90] degree in numerical value；Described right-hand bend threshold value is [270,360]
Numerical value in degree.
Between described coordinate points, the threshold range of distance is any value in [0,0.5 meter].
The present invention has substantive distinguishing features and marked improvement, solves the deficiency of background technology, is embedded using smart mobile phone
Accelerometer, gyroscope and magnetometer carry out detecting pedestrian step, estimate step-length and speculate direction of travel, be input to particle filter
Carry out pedestrian position estimation, and then information, the geological information of rss signal intensity change relatively carry out particle weights and divide according to the map
Join, the resampling eventually through particle filter completes pedestrian position estimation, obtains hi-Fix result.The method effectively solves
The inertial navigation cumulative error that inertial navigation of having determined positioning is led to by pedestrian's action (as arm is shaken, turned round) impact, and
And effectively realize pedestrian turn, the function such as room identification and door detection, it is to avoid to wifi router (ap) deployed position
Depend on unduly, and reduce the manpower consumption of deployment process.
Compared with prior art, the present invention has a following significant advantage:
Propose neodoxy inertial navigation-particle filter method being corrected using rss reading variation tendency；
Design and Implement turning and confirm algorithm, room recognizer and door detection algorithm；Smart mobile phone achieves lightweight,
Indoor pedestrian's positioning of decimetre class precision and tracking.Positioning precision is high, can obtain the positioning precision of decimeter grade.
Strong antijamming capability, positioning precision affected by pedestrian's action less, effectively solve inertial navigation positioning be subject to
Pedestrian's action (as arm is shaken, turned round) affects and the inertial navigation cumulative error that leads to, and effectively realize pedestrian turn,
The function such as room identification and door detection；Take full advantage of the relative geometrical relation of rss signal intensity, rather than rss is absolute
Numerical value；Reduce the manpower consumption of deployment process；Avoid and wifi router (ap) deployed position is depended on unduly；Preferably
Protect personal (position) privacy.
Brief description
Fig. 1 localization method of the present invention frame diagram.
Fig. 2 localization method of the present invention flow chart.
Particle filter flow chart in Fig. 3 the inventive method.
Turn in Fig. 4 the inventive method and confirm algorithm flow chart.
Recognizer flow chart in room in Fig. 5 the inventive method.
Door detection algorithm flow chart in Fig. 6 the inventive method.
Specific embodiment
As shown in Fig. 16, in conjunction with the content offer following examples of the inventive method:
Pedestrian's hand-held intelligent mobile phone is walked in environment indoors, specific as follows:
(1) smart mobile phone loads indoor cartographic information, defines the direction that direct north is 0 degree of coordinate system, defines left-hand rotation threshold
It is worth for 45, right-hand rotation threshold value is 315；
(2) smart mobile phone embeds accelerometer, gyroscope and magnetometer sensor collection sensing data, and gather interior
The wifi rss signal strength readings of all-router；
(3) pdr module is according to sensor sample data, detecting pedestrian step, estimation pedestrian's step-length, reckoning direction of travel；
(4) step estimating, step-length and direction are inputed to particle filter module by pdr module；
(5) all particles of particle filter move according to the step-length of pdr and direction, and effectively auxiliary based on (6) (7) (8)
Assisted correction pedestrian positions, and estimates pedestrian position, finally position is shown in indoor map in real time；
(6) because in pedestrian's walking process, actions such as (such as arm shake) actively turning leads to often due to no intention action
Pdr inertial navigation module thinks that pedestrian there occurs action of swerving (in fact not having), so pedestrian is in the process of walking, such as
Fruit pdr inertial navigation detects pedestrian's direction of travel and there occurs change, then particle filter can utilize the positioning school of wifi module
Orthofunction, execution is turned and is confirmed algorithm, and further whether the turning of confirmation pedestrian.For example: during current step, pdr detects
Direction of travel there occurs change, calculates corresponding rssdirection1 angle and is 45 degree, calculates rssdirection2 angle and is
80 degree, rssdirection2 has changed 35 degree compared with rssdirection1, less than left-hand rotation threshold value 45, therefore assert that pedestrian does not occur to turn
Body action, keeps the particle state of current step, continues on.And work as a certain step corresponding rssdirection1 angle and be
45 degree, the rssdirection2 angle that the previous step of calculating obtains is 100 degree, and two steps corresponding rss vector there occurs 55 degree
Change, more than left-hand rotation threshold value, so assert that pedestrian there occurs turning action, current step direction is changed to the walking of front second step
Direction
(7) when pedestrian gets in a certain room, in order to prevent leading to pedestrian to enter the feelings in wrong room due to position error
Shape occurs, and particle filter utilizes the positioning correcting function of wifi module, execution room recognizer, more accurately confirms pedestrian's
Room location, for example: during current step, step-length and directional information that particle filter is estimated according to pdr, combining cartographic information obtains
Know, this pedestrian is just passing through the door in certain room, now triggering room recognizer, for example: obtain the corresponding wifi of current step
The maximum ap1 of rss signal intensity, and define ap1 place room for room1, obtain current step corresponding wifi rss signal
The maximum ap2 of intensity more previous step change, corresponding room is room2, if ap1 is identical with ap2 then it is assumed that pedestrian is in room1
In room；Otherwise according to ap1 corresponding wifi rss absolute signal strengths wifi rss absolute signal strengths corresponding with ap2
All particles are carried out weights distribution, first all particle assemblies in room1 are set to set1 by proportionate relationship again,
All particle assemblies in room2 are set to set2, then redistribute weights, example respectively to all particles in set1 and set2
As: in wherein set1, the weights of a certain particle are 0.1, ap1 corresponding wifi rss for the weights of a certain particle in 0.3, set2
Signal intensity is -70, and ap2 corresponding wifi rss signal intensity is -50, then the weights of that particle in set1 will be by
Redistribute as 03* (90+ (- 70))/(180+ (- 70)+(- 50))=0.3*1/3；The weights of that particle in set2 are then
For: 0.1* (90+ (- 50))/(180+ (- 70)+(- 50))=0.1*2/3.Remaining particle redistributes power respectively by same procedure
Value, final particle filter is again estimated the position of pedestrian according to weights set, is further confirmed that the room residing for pedestrian；
(8) when particle filter detects pedestrian all the time door is outer or hovers in wall side or during long-time transfixion, execution
Whether the door detection algorithm of wifi module, more effectively detection pedestrian pass through door to enter room, for example: when pedestrian opens already
When beginning to four steps, obtain first three and walk corresponding four coordinate points, respectively (20,30), (20.5,29.5), (20,30.5)
(19.8,29.2), can be calculated and know this four coordinate points range difference each other all less than 0.5, in conjunction with cartographic information,
Find that these coordinate points are respectively positioned on the body of wall edge line in certain room, now traversal rss vector obtains current step earlier above the
The maximum ap of three step changes, creating, in the door mouth center in this ap place room, the particle that weights are 1 (will particle
Filtering has entirely been placed on door mouth position), pdr estimation information (step-length, the step and direction of travel) input then first three being walked
To particle filter, particle filter is thus again estimate the run trace of nearest three steps；Meanwhile, particle filter thinks that now pedestrian is just
Through door, therefore start room recognizer afterwards and further confirm that the concrete room residing for pedestrian.
Sum it up, the present invention has implementation result in detail below: 1, positioning precision is high, decimeter grade positioning knot can be obtained
Really；2nd, strong antijamming capability, positioning precision is affected less by pedestrian's action；3rd, effectively realize pedestrian turn, room identification with
And door detection etc. function；3rd, avoid and wifi router (ap) deployed position is depended on unduly；4th, reduce deployment process
Manpower consumption.
Claims (4)
1. a kind of indoor pedestrian's positioning based on smart mobile phone and tracking, comprising:
Step 1: smart mobile phone imports indoor map information, described indoor map is map vector；
Step 2: the accelerometer that embedded using smart mobile phone, gyroscope, that magnetometer sensor gathers this smart mobile phone is current
Acceleration information, angular velocity information and directional information, and gather indoor all wifi rss signal intensitys；
It is characterized in that comprising the following steps as follows:
Step 3: based on pdr inertial navigation, pedestrian's step, pedestrian's step-length and walking side are calculated according to the sensor sampled data
To；
Step 4: using the position of particle filter algorithm estimation pedestrian:
First, pedestrian's step-length, step and the direction of travel being calculated based on pdr, further according to cartographic information, wifi rss signal intensity
The relative geological information of change carries out particle weights distribution, completes pedestrian position eventually through resampling and estimates；
Step 5: pedestrian position is shown in indoor map；
Above-mentioned steps 4 particle filter algorithm estimates that the detailed process of the position of pedestrian is as follows:
(4.1) pedestrian's step, step-length and the direction of travel information transmission estimating pdr inertial navigation is to particle filter；
(4.2) particle moves according to the step-length of pdr and direction；
(4.3) particle filter obtains current step particle weights set, and relatively several based on wifi rss change in signal strength
What relation effectively carries out auxiliary corrective positioning；
(4.4) particle filter regenerates new particle；
In above-mentioned steps (4.3), positioned specific as follows based on wifi rss auxiliary corrective:
(4.3.1) judge correction scene,
If " turning scene ", then execute (4.3.2)；
If " room identification scene ", then execute (4.3.3)；
If " door detection scene ", then execute (4.3.4)；
(4.3.2) run to turn and confirm algorithm；
(4.3.3) run room recognizer；
(4.3.4) run door detection algorithm.
In above-mentioned wifi rss auxiliary corrective positioning, with running to turn, described " turning scene " identification confirms that algorithm is specific as follows:
(1) when pedestrian's direction of travel of pdr inertial navigation estimation changes than the direction of front step, triggering is turned really
Recognize, execute (2)～(5)；
(2) obtain the wifi rss vector direction between current step and previous step, be defined as rssdirection1；
(3) obtain the wifi rss vector direction between current step and front second step, be defined as rssdirection2；
(4) calculate the angle changing between two vector direction of rssdirection1 and rssdirection2；
(5) judge whether the angle changing in (4) is less than predefined left-hand bend threshold value or is more than right-hand bend threshold value；If change
Angle is less than left-hand bend threshold value or more than right-hand bend threshold value then it is assumed that current step does not occur real touch turn, uses
The direction of current step is replaced in the direction of front second step；Otherwise then it is assumed that current step there occurs touch turn really, continue
Keep the direction of current step；
In above-mentioned wifi rss auxiliary corrective positioning, described " room identification scene " identification is concrete such as with operation room recognizer
Under:
(1) pedestrian's step number, step-length and the direction of travel that particle filter deduces according to pdr inertial navigation, and cartographic information,
Judge whether current step passes through certain door；If passing through, illustrate that pedestrian is just entering room, now triggering room recognizer,
In ensuing 3 step scope interior circulation execution (2)～(6), beyond 3 step scopes, execute (7)；Otherwise, illustrate that pedestrian does not enter into
The action behavior in room, keeps current step state, executes (6) (7)；
(2) travel through the wifi rss signal strength readings of all ap routers that current step scans, search wifi rss and read
The maximum ap of number, is defined as ap1, and temporarily thinks that pedestrian enters the room room1 at ap1 place；
(3) travel through the wifi rss signal strength readings of all ap that current step scans, search the wifi of more previous step
The maximum ap of rss reading increment, orientates ap2 as, and the room simultaneously defining ap2 place is room2；
(4) if ap2 is identical with ap1, judge that pedestrian enters the room at ap1 place, keep the state of current particle, execution
(6)；Otherwise, the room that pedestrian is located is reaffirmed in execution (5)；
(5) particle filter combining cartographic information, according to ap1 corresponding wifi rss absolute signal strengths wifi corresponding with ap2
All particles are carried out weights distribution by the proportionate relationship of rss absolute signal strengths again:
5.1 information according to the map, find the coincidence body of wall in room room1 and room2, with it by all particles of particle filter
It is divided into two set set1 and set2, wherein set1 refers to the particle assembly in room1, and set2 refers to positioned at room2
Interior particle assembly；
During 5.2 definition current step, ap1 corresponding wifi rss signal intensity is rss1；Define ap2 corresponding wifi rss letter
Number intensity is rss2；
In 5.3 definition set set1, the weighting ratio coefficient ratio1 of particle is: (90+rss1)/(180+rss1+rss2)；
In 5.4 definition set set2, the weighting ratio coefficient ratio2 of particle is (90+rss2)/(180+rss1+rss2)；
5.5 traversal particle assembly set1, the weight w of each particle are reset to: w*ratio1；
5.6 traversal particle assembly set2, the weight w of each particle are reset to: w*ratio2；
(6) particle filter regenerates new particle；
(7) terminate algorithm；
In above-mentioned wifi rss auxiliary corrective positioning, described " door detection scene " identification is concrete with operation door detection algorithm such as
Under:
(1) if the accumulation step number of pedestrian is more than or equal to 3 steps, (2)～(8) are executed；Otherwise, (8) are executed；
(2) obtain the particle filter predicted position coordinate points of current step and front 3 steps, be defined as estpos0 successively respectively,
Estpos1, estpos2, estpos3；
(3) judge estpos0 and estpos1, estpos1 and estpos2 and estpos2 and estpos3 position coordinateses point it
Between distance whether all in predefined threshold range, if three above distance values are all in threshold range, and
Estpos0～estpos3 position coordinateses point is all located near the edge line of room wall, and described edge line is nearby away from edge
Within 0.5 meter, then execute (4)～(8)；Otherwise execute (8)；
(4) traversal searches current step and wifi rss signal strength readings increment maximum during nearest front three step
ap；
(5) entrance center position one weights of establishment in the room that particle filter is located in ap are 1 particle, and are based on nearest three
Step step-length and the direction of travel being obtained by pdr extrapolate nearest three steps action vector；
(6) particle filter regenerates new particle；
(7) particle filter execution room recognizer, further confirms that the room residing for pedestrian's particle；
(8) terminate algorithm.
2. indoor pedestrian's positioning based on smart mobile phone as claimed in claim 1 and tracking are it is characterised in that above turn
Acquisition wifi rss vector direction described in curved confirmation algorithm is specific as follows:
(1) smart mobile phone gather wifi router ap wifi rss signal strength readings with timestamp for index be stored in <
Rss1, rss2 ... rssn, time > data form in, be called a rss vector；
(2) travel through the rss vector in current step moment, search corresponding rss in wherein rss reading more previous step rss vector
The ap set that reading increases, is defined as set (increase)；Search corresponding rss in rss reading more previous step rss vector
The ap set that reading reduces, is defined as set (decrease)；
(3) positional information based on each ap place room, calculates the ground of set (increase) and set (decrease) respectively
Reason geometric center；
(4) calculate the vector between set (increase) geography geometric center and set (decrease) geography geometric center
Direction, i.e. current step corresponding rss vector direction.
3. indoor pedestrian's positioning based on smart mobile phone as claimed in claim 1 and tracking are it is characterised in that described
Predefined left-hand bend threshold value be (0,90] degree in numerical value；Described right-hand bend threshold value is the numerical value in [270,360] degree.
4. indoor pedestrian's positioning based on smart mobile phone as claimed in claim 1 and tracking are it is characterised in that described
Between coordinate points, the threshold range of distance is [0,0.5] rice.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410438351.0A CN104180805B (en) | 2014-08-29 | 2014-08-29 | Smart phone-based indoor pedestrian positioning and tracking method |
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