CN108322888B - Indoor positioning method of mobile terminal - Google Patents
Indoor positioning method of mobile terminal Download PDFInfo
- Publication number
- CN108322888B CN108322888B CN201810087210.7A CN201810087210A CN108322888B CN 108322888 B CN108322888 B CN 108322888B CN 201810087210 A CN201810087210 A CN 201810087210A CN 108322888 B CN108322888 B CN 108322888B
- Authority
- CN
- China
- Prior art keywords
- particles
- positioning
- reference area
- particle
- fingerprint
- 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.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 239000002245 particle Substances 0.000 claims abstract description 159
- 238000001914 filtration Methods 0.000 claims abstract description 14
- 238000011156 evaluation Methods 0.000 claims abstract description 9
- 101100289995 Caenorhabditis elegans mac-1 gene Proteins 0.000 claims description 6
- 101100478969 Oryza sativa subsp. japonica SUS2 gene Proteins 0.000 claims description 6
- 238000012952 Resampling Methods 0.000 claims description 6
- 101100004663 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) BRR2 gene Proteins 0.000 claims description 6
- 101100504519 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) GLE1 gene Proteins 0.000 claims description 6
- 230000004807 localization Effects 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 5
- 239000000284 extract Substances 0.000 claims description 2
- 238000009827 uniform distribution Methods 0.000 claims description 2
- 238000000605 extraction Methods 0.000 claims 2
- 238000010295 mobile communication Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 14
- 238000010586 diagram Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000005562 fading Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
-
- 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
Abstract
The invention discloses an indoor positioning method of a mobile terminal, belonging to the field of mobile communication and comprising the following steps: 1. modifying the fingerprint format of the Wi-Fi positioning reference area in the server; 2. the client enters a positioning reference area, an inertial navigation module program reads the step number and direction information of the user detected by a sensor, and particle filtering is adopted to track the walking track of the user; calling a Wi-Fi positioning module based on RSS fingerprints to implement positioning; 3. globally searching a position P1 with the highest density by the particle filter module program of the client, 4, and if the density of P1 is smaller than a threshold value, implementing positioning by the particle filter module program of the client; 5. if the P1 density is greater than the threshold, the client's evaluation calibration module program performs the correction. The method improves the positioning precision and avoids the computation load of repeatedly acquiring data by updating the Wi-Fi offline fingerprint database.
Description
Technical Field
The invention belongs to the field of mobile communication, and particularly relates to a method for fusing Wi-Fi positioning and inertial navigation positioning by using particle filtering.
Background
The traditional positioning technology (satellite positioning, base station positioning) is limited by technical means, and cannot meet the indoor positioning requirement. Currently, indoor positioning technologies mainly include: the positioning method comprises the following steps of Ultra Wide Band (UWB) technology, Bluetooth technology, ultrasonic technology, infrared positioning technology, radio frequency identification technology and Wi-Fi technology, wherein the positioning technology based on Wi-Fi is low in cost, does not need additional equipment support, and is wide in application range.
Wi-Fi technology provides many algorithmic approaches, including those based on signal strength fading models and those based on received signal strength fingerprinting (RSSI). The fingerprint method based on Received Signal Strength (RSS) is divided into two stages: firstly, in an off-line stage, in a system coverage area formed by a server, a plurality of Reference Points (RP) are set to acquire position information thereof and signal strength information of a router, and a signal strength-position fingerprint database of the reference points is formed. And in the online stage, a user scans a group of Wi-Fi signal intensity information by using mobile equipment with a Wi-Fi function to form an online fingerprint, and the centers of the most similar K reference points are determined as positioning results by comparing the similarity of the offline fingerprint and the online fingerprint. The disadvantages of this method are: when the target area is large or the requirement on positioning accuracy is high, the workload of acquiring data in an offline stage is large, and meanwhile, the position and power of the router may change, so that the Wi-Fi offline database needs to be updated by acquiring data regularly to ensure the correctness and reliability of the offline fingerprint database.
Driven by micro-electro-mechanical systems (MEMS) technology, various sensors have been reduced in size and cost, and are widely used in personal intelligent mobile terminals. Sensor-based positioning technology, which has the outstanding advantage of the autonomy and continuity of navigational positioning, can record the trajectory of the user. The sensors commonly used in the mobile terminal include inertial sensors (accelerometer and gyroscope), electronic compass, etc., based on which an inertial navigation system can be implemented, the number of steps taken by the user is determined by detecting the periodic change of the accelerometer, the direction of the user is determined by the angular velocity of the user detected by the electronic compass and gyroscope, and the information (PDR) such as the walking track and position of the Pedestrian is calculated by measuring and counting the number of steps, step length and direction of the walking of the Pedestrian.
In summary, Wi-Fi positioning accuracy is easily affected by distribution density of wireless network Access Points (APs), under complex indoor conditions, there are many indoor obstacles, multipath effects are obvious, external signals interfere, Wi-Fi signals are very unstable, and positioning difficulty is increased; in addition, the offline acquisition stage is time-consuming and labor-consuming, repeated acquisition is needed to ensure the reliability of the offline fingerprint database, and the cost is unacceptable in a large-scale indoor environment.
The mobile terminal of the inertial navigation system comprises the sensor which is small in size, low in cost and widely available, can acquire the real-time motion state of a user, is high in positioning precision in a short time, and can accurately record the track of the user. But due to the hardware construction characteristics, the mobile terminal has accumulated errors in the using process, so that the mobile terminal is not suitable for long-time positioning and using.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an indoor positioning method of a mobile terminal, which has high positioning precision, can effectively overcome the problems that the existing inertial navigation system is large in accumulated error and not suitable for long-time positioning in the positioning process, can still estimate the position of a user by using the result of inertial navigation when a Wi-Fi signal is unstable and difficult to position, and can simultaneously avoid repeatedly acquiring data to update a Wi-Fi offline fingerprint library.
The conception of the invention is as follows: the particle filter is used, and the inertial navigation system and the Wi-Fi positioning method based on RSS fingerprints are combined to realize indoor positioning, so that the positioning precision is improved, and the problem that the conventional inertial navigation system is not suitable for long-time positioning due to large accumulated error in the positioning process is solved. The Wi-Fi offline fingerprint database can collect Wi-Fi signal intensity information scanned when a user positions, dynamically improves the Wi-Fi offline fingerprint database when the user uses the Wi-Fi offline fingerprint database, stores a part of online fingerprints into the Wi-Fi offline fingerprint database to serve as new fingerprints, does not use repeated collected data to update the Wi-Fi offline fingerprint database, and reduces data collection workload in an offline stage.
The invention provides an indoor positioning method of a mobile terminal, which comprises the following steps:
step 1, when a server acquires data offline, a reference point is expanded into an area called a positioning reference area; the location reference area fingerprint format is as follows: (RPID, (X, Y, range), { (Mac1, RSS1) … …, (Macn, RSSn) },
the RPID is the serial number of the location reference area, and (X, Y Range) represents the Range in which the fingerprint may appear, i.e. a circular area with (X, Y) as the center and Range as the radius; { (Mac1, RSS1) … …, (Macn, RSSn) } is the signal strength value of each router in the location reference area;
step 2, the client enters a positioning reference area, the inertial navigation module program reads the step number and direction information of the user detected by the sensor, and particle filtering is adopted to track the walking track of the user; calling a Wi-Fi positioning module based on RSS fingerprints to implement positioning; scanning the RSS online fingerprint of the current position by the Wi-Fi positioning module program, searching a most matched fingerprint from a Wi-Fi offline fingerprint library acquired from the server, and taking the range of a positioning reference area of the fingerprint as a Wi-Fi positioning result;
step 3, globally searching a position P1 with the highest density by a particle filter module program of the client, if the density of P1 exceeds a threshold value, executing step 5 by the program to start online calibration, and otherwise executing step 4;
step 4, extracting a certain number of existing particles according to a certain weight by a client particle filter module program, placing the existing particles into a Wi-Fi positioning reference area, searching whether a position P2 exists in and around the positioning reference area, wherein the particle density of the existing particles is higher than that of the position P1, and if the position P2 exists, taking the existing particles as a positioning result; otherwise, taking P1 as a positioning result;
step 5, measuring all the particle numbers entering the Wi-Fi positioning reference area by an evaluation calibration module program of the client, and if the number is larger than a threshold value, inserting the online fingerprint as a new fingerprint into a Wi-Fi offline fingerprint library; if the particle number is smaller than the threshold value, the range of the positioning reference area is properly reduced according to the particle number; and if no particle enters the Wi-Fi positioning reference area, properly increasing the range of the positioning reference area.
The invention has the technical effects that:
aiming at a complex indoor Wi-Fi environment, the operation amount of the server for repeatedly acquiring data is reduced, the collected Wi-Fi signal strength information is calibrated in real time, the Wi-Fi offline database does not need to be updated by manually acquiring data, and the influence of AP signal strength change on positioning is reduced. Meanwhile, the particle filter is used and two positioning results of an inertial navigation system and a Wi-Fi positioning method based on RSS fingerprints are combined, so that the advantages of two different positioning technologies are absorbed, the disadvantages of the two positioning technologies are effectively overcome, and the positioning accuracy is remarkably improved.
Drawings
The drawings of the invention are illustrated as follows:
FIG. 1 is a system block diagram of the present invention;
FIG. 2 is a flow chart of the present invention using Wi-Fi positioning and inertial navigation positioning;
FIG. 3 is a flow chart of the present invention for calibrating a Wi-Fi offline fingerprint library.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
For ease of understanding, a description will first be made of the overall structure of the system:
fig. 1 is a system structure diagram of the present invention, and the system is composed of a server side and a client side, wherein the server side stores a map U15 of a target area, and maintains a Wi-Fi offline fingerprint database U16. The client program is mainly divided into four parts, namely a Wi-Fi positioning module U12, an inertial navigation module U11, a particle filtering module U13 and an evaluation and calibration module U14.
After the user opens the client program after entering the target area, the client requests data from the server, and the server sends the map U15 information and the Wi-Fi offline fingerprint database U16 information to the client. After the client obtains the information, two methods are used for positioning: the inertial navigation module U11 reads data from sensors such as accelerometers, electronic compasses, and gyroscopes, calculates the direction and number of steps of the user, and transmits the data to the particle filter module U13. After obtaining the information, the particle filter module U13 tracks the user position using the particles; meanwhile, the Wi-Fi positioning module U12 carries out Wi-Fi positioning based on RSS fingerprints, and sends positioning results to the particle filtering module U13 to determine the final positioning results. And (3) gradually converging the particles of the particle filter along with the walking of the user, starting an evaluation and calibration module U14 by the client after the density reaches a certain threshold value, determining the range change of the positioning reference area, and uploading the result to a Wi-Fi offline database of the server.
The invention provides an indoor positioning method of a mobile terminal, which comprises the following steps:
step 1, deploying a Wi-Fi positioning hardware system in a target area, acquiring offline data by a server, averagely dividing the target area into a plurality of small squares with equal areas, calling an inscribed circle of the small squares as a positioning reference area, acquiring a group of Wi-Fi signal intensity data at the circle center of the positioning reference area to form a fingerprint of the positioning reference area, storing the position (X, Y) of the circle center of the positioning reference area and the radius Range of the inscribed circle to form the fingerprint of the positioning reference area, wherein the fingerprint is in the format of (RPID, (X, Y, Range), { Mac1, RSS1 … …, Macn, RSSn }).
The RPID is the serial number of the location reference area, and (X, Y, Range) represents the Range in which the fingerprint may appear, i.e. a circular area with (X, Y) as the center and Range as the radius; { (Mac1, RSS1) … …, (Macn, RSSn) } is the signal strength value of each router at the location reference area.
Step 2, the client enters a positioning reference area, the inertial navigation module reads the step number and direction information of the user detected by the sensor, and the position of the user is estimated by adopting particle filtering; and calling a Wi-Fi positioning module based on the RSS fingerprint to implement positioning.
Particle filtering is a mature and efficient class of filtering algorithms whose main idea is to express the posterior distribution of a series of posterior probabilities by extracting random state samples (called "particles" figuratively). It mainly consists of two important steps: random sampling and importance resampling.
At the beginning of positioning, the user's position cannot be known, and at this time, the positions of the estimated users by particle filtering are distributed in the whole target area with equal probability, so that all particles are randomly and uniformly distributed in the whole positioning area. As the user walks, all particles will have the same trajectory as the user, and many particles may behave unreasonably, such as crossing a wall, beyond the target area. The user positions represented by these particles will be eliminated. In the process of continuous walking of the user, more and more positions are eliminated, and the probability gradually converges to a smaller position. In this way, particle filtering accurately estimates the user's location.
After the user holds the mobile terminal device to enter the target area, the positioning flow steps shown in fig. 2 are executed:
in step S101, the client starts up;
in step S102, the client requests data from the server and receives map information of a target area and Wi-Fi offline fingerprint database information sent by the server; and simultaneously starting the inertial navigation module program and the Wi-Fi positioning module program.
Chinese patent document CN102419180A discloses an inertial navigation system at 18/4/2012, which can determine the direction of a user by using an accelerometer to count steps and using a gyroscope and an electronic compass.
In step S104, random sampling;
aiming at the scene of indoor positioning, the particle of particle filtering is improved. Namely, each particle has a common four-dimensional attribute, (x, y, direction, StepLength), x and y are positions of the particles and are used for estimating the position of the user, and the direction is the advancing direction of the particles and is used for estimating the walking direction of the user. StepLength is the distance of each advance and is used to estimate the step size of the user's walk. The particles are random state samples of the user location distribution. At the beginning of the localization we do not know where the user is, so the particle filter module program randomly samples the particles in all possible areas and all possible states of the user. The specific process is as follows:
a. the x and y values of all the particles are randomly selected in all possible ranges in the map coordinate;
b. the direction values of all the particles are randomly taken in the range after the error value of plus or minus 15 degrees is added to the user direction value measured by the electronic compass;
c. the StepLength values of all the particles are randomly taken in the step length range of 50cm to 80cm of a normal person.
At this point, the particle filter module process completes the random sampling step in particle filtering. The higher the number of particles, the greater the positioning accuracy, but the computational cost increases accordingly. The positioning precision and the calculation cost are comprehensively considered, and the number of the particles is set to 10000 in the particle filter module program of the system.
At step S105, the inertial navigation module program begins to monitor data from sensors, including: gyroscopes, electronic compasses, accelerometers;
once the data of the sensor changes, the direction of the user and whether the user walks one step are calculated. And when the user walks for one step, the inertial navigation module program informs the particle filter module and sends the direction value of the detected user.
In step S106, importance resampling;
when the user walks one step, the particle filter module program updates the directions of all the particles by using the received direction values, namely randomly taking values according to the direction values measured by the electronic compass and the error values of plus and minus 15 degrees.
The particle filter module program updates the positions of all particles using the following formula:
Xnew＝Xold+StepLength*sinθ
Ynew＝Yold+StepLength*cosθ
wherein θ is the direction of the particle, i.e. the direction value; xnew、YnewFor updated particle position coordinates, Xold、YoldIs the original position coordinate of the particle.
The particle filter module program detects whether the generated coordinates of the new position are legal, namely detects that:
whether the coordinates of the position are in a map, whether the position can appear in a pedestrian, and whether the position crosses a wall when the position is reached; the program will eliminate particles that are not legal in location, i.e. at this point the user is unlikely to be present at that location. Meanwhile, a part of particles are retained, the possibility that the positions of the particles exist in the user is increased, and the particle filtering module program performs resampling on the areas where the particles are located to generate new particles so as to supplement the eliminated particles and keep the total number of the particles unchanged.
Steps S105 and S106 are executed continuously, and the inertial navigation module updates the direction values of all the particles whenever it detects that the direction of the user changes. When the user is detected to walk one step, the positions of the particles are updated, and new particles are generated iteratively. As the user walks, the probability that the user has a location converges, the particles are more and more aggregated, and the density is more and more increased.
In step S108, the particle filter module program searches (called global search) the position P1 with the highest density in the whole target area, and determines whether the particle density at this position exceeds a threshold, if so, step S109 is executed, otherwise, step S110 is executed.
The threshold value is set differently depending on the size of the region, the number of particles used, and the threshold value should be set to 300 or more per square meter in a region of about 400 square meters of 10000 particles, taking the system as an example.
In step S109, an evaluation calibration module program is executed;
in step S103, calling a Wi-Fi positioning module based on the RSS fingerprint;
and scanning a group of Wi-Fi signal strength information of the current position to form an online fingerprint. And measuring the similarity degree of the online fingerprint and the fingerprints in the offline fingerprint library by using the Euclidean distance, finding the most similar fingerprint, and taking the positioning reference area of the most similar fingerprint as a positioning result.
In step S107, the particle filter module extracts a certain number of existing particles to the area of the Wi-Fi positioning result according to the weight, where the number of the particles is a fixed value Nrp. The particles were selected according to the following strategy: the selection is carried out according to the distance of each particle from the Wi-Fi positioning result, and the probability of the selection is higher the farther the particles are. Maximum and minimum normalization of the distance:
in the formula (d)newIs the weight of the particle, doldRefers to the distance, d, from each particle to the Wi-Fi positioning resultminIs the minimum value of all particles to Wi-Fi localization result, i.e. doldA minimum value of (d)maxIs the maximum value of Wi-Fi positioning results of all particles, i.e. doldIs measured.
The system randomly generates a fraction between 0 and 1 for each particle, and if the number is less than the weight, the particle is selected.
In step S110, the particle filter program module searches whether there is a position P2 in and around the Wi-Fi positioning result, where the particle density of the P2 position is higher than that of the P1 position, if so, the P2 position is used as the positioning result, otherwise, the P1 position is used as the positioning result.
A certain number NrpAfter the particles are placed in the Wi-Fi positioning reference area, the density may change, and a region with higher density may appear around the positioning reference area, so that the search is continued once in step S110.
The Wi-Fi positioning module program will periodically scan the Wi-Fi signal intensity around, and the above steps S103, S107, and S110 will also continuously loop until the user finishes positioning.
In step S111, the client is powered off, and the positioning is finished.
In the above step S109, a flowchart for performing the evaluation calibration is shown in fig. 3. To clearly illustrate this flow diagram, the following concepts are illustrated:
at the stage of estimating the user position by the particle filter module program, all the particles are uniformly distributed in the whole target area, and the particle density at the moment is calculated as the reference density rhobaseAnd calculating the number of particles allocated to each positioning reference region as Nrp＝ρbase*Srp，SrpTo locate the area of the reference region. The number of particles allocated to each positioning reference area is constant as N in the whole positioning processrpThe number of particles entering the positioning result area is N1.
In step S201, the evaluation calibration module program obtains a positioning result according to a Wi-Fi positioning method based on RSS fingerprint.
The result of the localization is a circular area whose radius is determined by the Range value in the fingerprint.
In step S202, it is determined whether particles enter the positioning result region; if yes, go to step S205; otherwise, executing step S203;
at step S203, a value is determined α based on the trustworthiness of the Wi-Fi fix and the inertial navigation fix.
While the confidence is measured by the density of the particles. Globally searching a position with the highest particle density outside the positioning reference area, and calculating the particle density rho max of an area taking the Range of the positioning reference area as the radius;
α＝(Nrp/Range)/ρmax。
in step S204, the distance d from the positioning reference region to the nearest particle is calculatedmin；
Determining a new Range of the location reference area based on the distance, the new Range being determined by the following formula:
in the formula, ρbaseFor a particle density distributed uniformly over the target area, NrpFor uniform distribution of the number of particles in the positioning reference area, the confidence α ═ NrpRange)/ρ max, ρ max is the global search for a highest particle density; dminIs the minimum of all particles to Wi-Fi localization results.
Then, step S208 is performed.
Step S205, judging whether the number of the particles entering the Wi-Fi positioning result is more than 50% of the number of the particles distributed by the Wi-Fi positioning result; if yes, go to step S206; otherwise, go to step S207;
in step S206, the average value of the x and y values of all the particles entering the positioning reference area is calculated to obtain the center P (x, y) of the particles, the center P (x, y) of all the particles entering the positioning reference area is used as the coordinate of the fingerprint, and the area S covered by the particles is calculatednew＝N1/ρbase,Then, then
N1Is the number of particles that enter the location result area.
Thereby obtaining a new positioning reference area.
In the step, the original fingerprints are not calibrated, and the Wi-Fi fingerprints scanned on line are stored in a Wi-Fi offline database of the server as a group of new fingerprints.
In step S207, the range size of the original positioning reference area is updated, and the new range is:
in step S208, the modified result is uploaded to the server for storage.
Through the evaluation and calibration process, the Wi-Fi offline fingerprint database of the server is improved by using the online scanned fingerprint of the user, the accuracy of the existing fingerprint can be adjusted, and the positioning accuracy and the offline acquisition efficiency are improved.
Claims (9)
1. An indoor positioning method of a mobile terminal is characterized by comprising the following steps:
step 1, when a server acquires data offline, a reference point is expanded into an area which is called a positioning reference area; the location reference area fingerprint format is as follows: (RPID, (X, Y, Range), { (Mac1, RSS1) … …, (Macn, RSSn) }), where RPID is the serial number of the location reference area, and (X, Y, Range) represents the Range in which the fingerprint may appear, i.e., a circular area with (X, Y) as the center and Range as the radius; { (Mac1, RSS1) … …, (Macn, RSSn) } is the signal strength value of each router in the location reference area;
step 2, the client enters a positioning reference area, the inertial navigation module program reads the step number and direction information of the user detected by the sensor, and particle filtering is adopted to track the walking track of the user; calling a Wi-Fi positioning module based on RSS fingerprints to carry out positioning, scanning the RSS online fingerprint of the current position by the Wi-Fi positioning module program, searching a most matched fingerprint from a Wi-Fi offline fingerprint library acquired from a server, and taking the range of a positioning reference area of the fingerprint as a Wi-Fi positioning result;
step 3, globally searching a position P1 with the highest density by a particle filter module program of the client, if the density of P1 exceeds a threshold value, executing step 5 by the program to start online calibration, and otherwise executing step 4;
step 4, a particle filter module program of the client extracts a certain number of existing particles according to a certain weight, the existing particles are placed in a Wi-Fi positioning reference area, whether a position P2 exists in and around the positioning reference area is searched, the particle density of the existing particles is higher than that of the position P1, and if the position P2 exists, the existing particles are used as a positioning result; otherwise, taking P1 as a positioning result;
step 5, measuring all the particle numbers entering the Wi-Fi positioning reference area by an evaluation calibration module program of the client, and if the number is larger than a threshold value, inserting the online fingerprint as a new fingerprint into a Wi-Fi offline fingerprint library; if the particle number is smaller than the threshold value, the range of the positioning reference area is properly reduced according to the particle number; and if no particle enters the Wi-Fi positioning reference area, properly increasing the range of the positioning reference area.
2. The indoor positioning method of the mobile terminal as claimed in claim 1, wherein: in step 2, the particle filter has a fixed number of particles, each particle has a four-dimensional attribute, (x, y, direction, StepLength), x and y are positions of the particles and are used for estimating the position of the user, direction is the advancing direction of the particle and is used for estimating the walking direction of the user, StepLength is the distance of each advance and is used for estimating the step size of the user walking, and the particle filter includes random sampling and importance resampling.
3. The indoor positioning method of the mobile terminal as claimed in claim 2, wherein: the random sampling comprises: a. the x and y values of all the particles are randomly selected in all possible ranges in the map coordinate; b. the direction values of all the particles are randomly taken in the range after the error value of plus or minus 15 degrees is added to the user direction value measured by the electronic compass; c. the StepLength values of all the particles are randomly taken in the step length range of 50cm to 80cm of a normal person.
4. The indoor positioning method of the mobile terminal as claimed in claim 3, wherein: the importance resampling updates the positions of all particles using the following formula:
Xnew＝Xold+StepLength*sinθ
Ynew＝Yold+StepLength*cosθ
wherein θ is the direction value of the particle; xnew、YnewFor updated particle position coordinates, Xold、YoldIs the original position coordinate of the particle.
5. The indoor positioning method of the mobile terminal as claimed in claim 4, wherein: detecting whether the generated coordinates of the new position are legal or not, including detecting whether the coordinates of the position are in a map or not, whether the coordinates are at positions where pedestrians can appear or not, whether the position crosses a wall or not, and eliminating illegal particles at the position; and resampling the areas where the particles are located to generate new particles, so that the total number of the particles is kept unchanged.
6. The indoor positioning method of the mobile terminal as claimed in claim 1, wherein: in step 4, the extraction weight of extracting a certain number of particles to the Wi-Fi positioning result area by the particle filter module program of the client according to a certain weight is:
in the formula (d)newIs the weight of the extraction of the particles, doldRefers to the distance, d, from each particle to the Wi-Fi positioning resultminIs doldMinimum value of dmaxIs doldIs measured.
7. The indoor positioning method of the mobile terminal as claimed in claim 1, wherein: in step 5, in case no particles enter the Wi-Fi positioning reference area, a new Range is determined using the following formula:
in the formula, ρbaseFor a particle density distributed uniformly over the target area, NrpFor uniform distribution of the number of particles in the positioning reference area, the confidence α ═ Nrp/Range)/ρmax，ρmaxSearching for a highest particle density for the global; dminIs-minimum of all particles to Wi-Fi localization result, RangenewThe updated radius of the reference area is located.
8. The indoor positioning method of the mobile terminal as claimed in claim 1, wherein: in step 5, in case all the number of particles entering the Wi-Fi positioning reference area is greater than the threshold, the online fingerprint is determined as follows: calculating the center P (x, y) of all the particles entering the positioning reference area as the coordinate of the fingerprint, and calculating the area S covered by the particlesnew＝N1/ρbaseThen, then
In the formula, N1Is the number of particles, p, entering the location result areabaseIs the density of particles uniformly distributed throughout the target area.
9. The indoor positioning method of the mobile terminal as claimed in claim 1, wherein: in step 5, in the case that the number of all particles entering the Wi-Fi positioning reference area is smaller than the threshold value, the range of the positioning reference area is narrowed as follows:
in the formula, N1Is the number of particles entering the location result area, Nrp is the number of particles uniformly distributed in the location reference area, ρbaseIs the density of particles uniformly distributed throughout the target area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810087210.7A CN108322888B (en) | 2018-01-30 | 2018-01-30 | Indoor positioning method of mobile terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810087210.7A CN108322888B (en) | 2018-01-30 | 2018-01-30 | Indoor positioning method of mobile terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108322888A CN108322888A (en) | 2018-07-24 |
| CN108322888B true CN108322888B (en) | 2020-05-12 |
Family
ID=62887441
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810087210.7A Expired - Fee Related CN108322888B (en) | 2018-01-30 | 2018-01-30 | Indoor positioning method of mobile terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108322888B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110967667A (en) * | 2019-11-19 | 2020-04-07 | 重庆大学 | Indoor track acquisition method based on crowdsourcing Wi-Fi fingerprint positioning |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104796866A (en) * | 2015-05-06 | 2015-07-22 | 北京我联科技有限公司 | Indoor positioning method and device |
| CN105447574A (en) * | 2015-11-10 | 2016-03-30 | 深圳大学 | Auxiliary truncation particle filtering method, device, target tracking method and device |
| CN106382931A (en) * | 2016-08-19 | 2017-02-08 | 北京羲和科技有限公司 | An indoor positioning method and a device therefor |
| US9609469B2 (en) * | 2013-09-19 | 2017-03-28 | Qualcomm Incorporated | Seeding an estimator when receiving assistance data on a slow link |
| CN107339989A (en) * | 2017-06-23 | 2017-11-10 | 江苏信息职业技术学院 | A kind of pedestrian's indoor orientation method based on particle filter |
| CN107517499A (en) * | 2016-06-16 | 2017-12-26 | 千寻位置网络有限公司 | The localization method and its device of mobile terminal |
| CN107548033A (en) * | 2016-06-24 | 2018-01-05 | 富士通株式会社 | Positioner, method and electronic equipment |
-
2018
- 2018-01-30 CN CN201810087210.7A patent/CN108322888B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9609469B2 (en) * | 2013-09-19 | 2017-03-28 | Qualcomm Incorporated | Seeding an estimator when receiving assistance data on a slow link |
| CN104796866A (en) * | 2015-05-06 | 2015-07-22 | 北京我联科技有限公司 | Indoor positioning method and device |
| CN105447574A (en) * | 2015-11-10 | 2016-03-30 | 深圳大学 | Auxiliary truncation particle filtering method, device, target tracking method and device |
| CN107517499A (en) * | 2016-06-16 | 2017-12-26 | 千寻位置网络有限公司 | The localization method and its device of mobile terminal |
| CN107548033A (en) * | 2016-06-24 | 2018-01-05 | 富士通株式会社 | Positioner, method and electronic equipment |
| CN106382931A (en) * | 2016-08-19 | 2017-02-08 | 北京羲和科技有限公司 | An indoor positioning method and a device therefor |
| CN107339989A (en) * | 2017-06-23 | 2017-11-10 | 江苏信息职业技术学院 | A kind of pedestrian's indoor orientation method based on particle filter |
Non-Patent Citations (1)
| Title |
|---|
| WiFi与MEMS-IMU融合定位方法在室内定位中的应用;雷雨伟等;《导航定位学报》;20170224;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108322888A (en) | 2018-07-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109298389B (en) | Indoor pedestrian combination pose estimation method based on multi-particle swarm optimization | |
| CN108632761B (en) | Indoor positioning method based on particle filter algorithm | |
| WO2017088196A1 (en) | Fusion navigation device and method based on wireless fingerprints and mems sensor | |
| CN107111641B (en) | Location estimation for updating a database of location data | |
| CN109413578B (en) | Indoor positioning method based on fusion of WIFI and PDR | |
| Kim et al. | Smartphone-based collaborative and autonomous radio fingerprinting | |
| US9244152B1 (en) | Determining device locations using movement, signal strength | |
| CN104796866B (en) | Indoor orientation method and device | |
| CN108534779B (en) | Indoor positioning map construction method based on track correction and fingerprint improvement | |
| CN108521627B (en) | Indoor positioning system and method based on WIFI and geomagnetic fusion of HMM | |
| CN108919177B (en) | Positioning map construction method based on virtual information source estimation and track correction | |
| CN110320495A (en) | A kind of indoor orientation method based on Wi-Fi, bluetooth and PDR fusion positioning | |
| CN107270889B (en) | Indoor positioning method and positioning system based on geomagnetic map | |
| Du et al. | CRCLoc: A crowdsourcing-based radio map construction method for WiFi fingerprinting localization | |
| Hao et al. | A multi-floor location method based on multi-sensor and WiFi fingerprint fusion | |
| CN113566820B (en) | Fused pedestrian positioning method based on position fingerprint and PDR algorithm | |
| Liu et al. | Indoor positioning using similarity-based sequence and dead reckoning without training | |
| Zhuang et al. | SMART: Simultaneous indoor localization and map construction using smartphones | |
| CN110967667A (en) | Indoor track acquisition method based on crowdsourcing Wi-Fi fingerprint positioning | |
| Liu et al. | Eyeloc: Smartphone vision-enabled plug-n-play indoor localization in large shopping malls | |
| CN108322888B (en) | Indoor positioning method of mobile terminal | |
| Ta | Smartphone-based indoor positioning using Wi-Fi, inertial sensors and Bluetooth | |
| CN105866729B (en) | A kind of indoor orientation method and device based on user behavior characteristics | |
| Chen et al. | Pedestrian positioning with physical activity classification for indoors | |
| Li et al. | Quantized rss based wi-fi indoor localization with room level accuracy |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200512 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |