CN110345939A - A kind of indoor orientation method merging fuzzy logic judgement and cartographic information - Google Patents
A kind of indoor orientation method merging fuzzy logic judgement and cartographic information Download PDFInfo
- Publication number
- CN110345939A CN110345939A CN201910587333.1A CN201910587333A CN110345939A CN 110345939 A CN110345939 A CN 110345939A CN 201910587333 A CN201910587333 A CN 201910587333A CN 110345939 A CN110345939 A CN 110345939A
- Authority
- CN
- China
- Prior art keywords
- deflection
- particle
- formula
- angle
- magnetometer
- 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
- G01C21/04—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means
- G01C21/08—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means involving use of the magnetic field of the earth
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—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
- G01C21/18—Stabilised platforms, e.g. by gyroscope
-
- 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
-
- 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
- H04W4/029—Location-based management or tracking services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- Life Sciences & Earth Sciences (AREA)
- Navigation (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a kind of fusion fuzzy logic judgement and the indoor orientation methods of cartographic information, are related to indoor positioning field.The indoor orientation method of fusion fuzzy logic judgement and cartographic information includes the following steps that step 1: indoor pedestrian track calculates；Step 2: the adaptive weighting deflection blending algorithm based on fuzzy logic judgement, it is first determined whether enabling blending algorithm or the simple direction angular data using a certain optimum state；If judgement enables after blending algorithm, according to the data of the actual acquisition of gyroscope and magnetometer, adaptive adjustment weight is to select the blending algorithm for being suitble to current state, by the relevance threshold for giving gyroscope and magnetometer, the deflection of magnetometer is recycled to judge walking states after determining the two correlation, determine whether that last moment deflection is added, is finally merged according to fixed weight；Step 3: the particle filter based on map match is added the terrestrial reference identified by WiFi peak point, is modified to particle position.
Description
Technical field
The present invention relates to indoor positioning fields, and in particular to the interior of a kind of fusion fuzzy logic judgement and cartographic information is fixed
Position method.
Background technique
With the arrival of digital age, location based service (Location Based Service, LBS) becomes
Increasingly important is owned by wide application prospect in terms of fire rescue, medical services, parking.Global positioning system
System (global positioning systems, GPS) can satisfy pedestrian in outdoor location requirement substantially, but due to room
The meeting of blocking caused by GPS signal of interior complex environment is so that GPS positioning can not be applied indoors, therefore indoor positioning becomes
New research hotspot.Typical indoor positioning technologies mainly include the fingerprint location based on wireless network at present, are led based on inertia
Pedestrian's dead reckoning (Pedestrian Dead of boat system (Inertial Navigation System, INS)
Reckoning, PDR) method, three side localization method of triangle, based on infrared positioning and based on radio frequency (radio
Frequency, RF) positioning etc..It is equipped with along with the sensor of smart phone perfect, utilizes INS to solve indoor positioning difficult
Topic has become possibility.However, the PDR system positioned using INS can be comprising a large amount of during acquiring data due to sensor
Noise, and positioning result of the calculating based on last moment of PDR positioning, therefore will lead to the accumulation of final position error.How
Inhibiting sensing data error accumulation is current urgent problem.
Common calibration method has the deflection by gyroscope and magnetometer data fusion to calibrate to improve subsequent positioning
Precision, the positioning result that PDR is merged to WiFi positioning result or corrected in real time using map datum PDR.But it is existing
PDR calibration method has the following problems:
Current deflection fusion method is merged using gyroscope and magnetometer merely, is not accounted in different feelings
Whether need to start different deflection blending algorithms under condition to adapt to current state.
Simple addition terrestrial reference is modified PDR positioning result, and what the precision of positioning result can be excessive depends on terrestrial reference
Quantity, more terrestrial reference acquisition needs to expend a large amount of manpower and material resources, and less terrestrial reference may result in the position after calibration
Setting has biggish alternate position spike with previous moment position, causes final positioning accuracy undesirable.
Although WiFi positioning result, without cumulative errors, WiFi signal is easy the fluctuation that is interfered, positioning is caused to be tied
Fruit is poor, to reduce overall fusion positioning accuracy.
Summary of the invention
The purpose of the present invention is in view of the above deficiencies, propose merging for a kind of fusion of integrated multi-sensor and cartographic information
The indoor orientation method of fuzzy logic judgement and cartographic information.
The present invention specifically adopts the following technical scheme that
A kind of indoor orientation method merging fuzzy logic judgement and cartographic information, comprising the following steps:
Step 1: indoor pedestrian track calculates, using two-dimensional vector [x, y]TIndicate the position of pedestrian, and according to formula (1)
The position of pedestrian is updated:
Wherein, [xi-1,yi-1]TRepresent the position of the (i-1)-th walking people, θiWith siRespectively represent walking direction and the step of the i-th step
It is long；
Step 2: the adaptive weighting deflection blending algorithm based on fuzzy logic judgement melts it is first determined whether enabling
Hop algorithm or the simple direction angular data using a certain optimum state；After if judgement enables blending algorithm, according to gyro
The data cases of the actual acquisition of instrument and magnetometer, adaptive adjustment weight to select the blending algorithm of suitable current state,
By giving the relevance threshold of gyroscope and magnetometer, the deflection of magnetometer is recycled to judge row after determining the two correlation
Walk state, it is determined whether last moment deflection is added, is finally merged according to adaptive weighting；
Step 3: the terrestrial reference identified by WiFi peak point is added, to particle position in the particle filter based on map match
It is modified.
Preferably, in the step 2,
The shift state of gyroscope and magnetometer is divided into three kinds, corresponding three sections are logical to input data is needed later
Formula (2) is crossed to be standardized:
Wherein, st indicates the standardized value of output, valuetFor current input value, min (value), max (value)
Respectively represent the minimum value and maximum value in current data；
Five kinds of output valves are defined, five kinds of output valves respectively correspond respective Fusion Strain；
The If-Then dicision rules of given fuzzy logic；
Corresponding blending algorithm is selected according to given range output valve；
Whether pedestrian is detected in the straight line stage by formula (3):
||θmi|-|θmi-1| | > Th1 ∪ | | θmi|-|θmi-1| | < Th2 (3)
Wherein, θmIt indicates the measured value of deflection, uses the deflection θ at current time respectivelymiWith the deflection of last moment
θmi-1Difference is sought, the difference is made to be less than given threshold value Th1, while the θ at i momentmiNeed the angle, θ with the i+1 momentmi+1Difference
Value is less than threshold value Th2, is limited by above-mentioned two condition, so that it is determined that pedestrian's walking states.
Preferably, determine in pedestrian movement's state procedure using formula (3), in turning, fusion deflection is used
Formula (4) calculates:
anglet=ωog(ωoriangleori t-1+ωorianglegyor t-1) (4)
Direction fusion angle under straight-going state is calculated using formula (5):
anglet=ωpog(ωpreanglet-1+ωorianglet-1 ori+ωgyoanglet-1 gyor) (5)
Wherein,
anglet、anglet-1The deflection of respectively fused deflection and last moment, ωpre、ωori、ωgyoPoint
Not Wei a moment deflection weight, magnetometer weight and gyroscope weight, magnetometer offset at this time is serious and gyroscope exists
Tolerance interval, then the ratio between last moment weight is ωpre: ωori: ωgyo=2:1:2, on the contrary it is 2:2:1.
Preferably, in the step 3, the particle filter based on map match is carried out using formula (8),
Wherein, p (x0) be dynamical system always prior probability distribution, k moment system mode xkPosterior probability distribution be
p(x0:k|z1:k), N represents number of particles, x0:kSystem mode of the expression system from 0 to k moment, z1:kThe observation of system is represented,
It indicates at a distance from PD result in the application, the weight of particle meets normalizing condition shown in formula (9)
Weight is updated as shown in formula (10),
Constraint of the right value update of particle by cartographic information, when particle updates weight by particle right value update through walls
It is 0, whereinFor pedestrian position posterior probability, it is expressed as formula (11)
When pedestrian is in straight-going state, according to formula (11) more new particle output position, when system detection is to anchor point or
When person detects pedestrian's turn condition, the data stored in database are updated to particle output position at this time, this up-to-date style
(12) indicate that i-th of particle at a distance from terrestrial reference, is used for the location updating of particle next time at time k later:
The invention has the following beneficial effects:
The localization method uses fuzzy logic inference system, and the concrete condition of deflection is judged using fuzzy logic, then
According to the weight for the deflection blending algorithm that the degree of membership of output determines to use at this time, and whether trigger blending algorithm；
The improvement that the method that WiFi signal peak value carries out terrestrial reference detection is combined with particle filter is examined based on WiFi peak value
On the basis of geodetic mark method, particle resampling the output of process to landmark point predicts pedestrian track, it is fixed to reduce
Jump and fluctuation between site, promote final positioning accuracy.
The localization method is without spending human and material resources a large amount of cartographic informations of acquisition；Fuzzy logic judgement considers deflection
A variety of situations, and weight is adaptively changed, is more suitable for actual conditions；The combination of landmark point and particle filter increases fixed
The stability of position result, anchor point jumps after solving the problems, such as the calibration that landmark point acquisition occurs less；Do not utilize PDR and WiFi
The conventional method for positioning fusion has evaded bring fusion position error due to WiFi signal fluctuation is big.
Detailed description of the invention
Fig. 1 is PDR system architecture diagram；
Fig. 2 is fuzzy logic deterministic process flow diagram；
Fig. 3 is the particle filter method flow chart based on terrestrial reference.
Specific embodiment
A specific embodiment of the invention is described further in the following with reference to the drawings and specific embodiments:
In conjunction with attached drawing, merge the indoor orientation method of fuzzy logic judgement and cartographic information the following steps are included:
Step 1: indoor pedestrian track calculates, using two-dimensional vector [x, y]TIndicate the position of pedestrian, and according to formula (1)
The position of pedestrian is updated:
Wherein, [xi-1,yi-1]TRepresent the position of the (i-1)-th walking people, θiWith siRespectively represent walking direction and the step of the i-th step
It is long；Three parts that pedestrian's reckoning subsystem is mainly made of walking detection, walking direction and step-size estimation, pedestrian's boat position
Calculate that the architecture diagram of (Pedestrian Dead Reckoning, PDR) algorithm is detailed in lower Fig. 1.
Step 2: the adaptive weighting deflection blending algorithm based on fuzzy logic judgement melts it is first determined whether enabling
Hop algorithm or the simple direction angular data using a certain optimum state；After if judgement enables blending algorithm, according to gyro
The data cases of the actual acquisition of instrument and magnetometer, adaptive adjustment weight to select the blending algorithm of suitable current state,
By giving the relevance threshold of gyroscope and magnetometer, the deflection of magnetometer is recycled to judge row after determining the two correlation
Walk state, it is determined whether last moment deflection is added, is finally merged according to adaptive weighting；
In step 2,
The shift state of gyroscope and magnetometer is divided into four kinds, is { verylow, low, med, high } respectively, it is corresponding
Four sections are [0 0.05], [0.05 0.1], [0.1 0.3], [0.3 1], later to needing input data to pass through formula (2)
It is standardized:
Wherein, st indicates the standardized value of output, valuetFor current input value, min (value), max (value)
Respectively represent the minimum value and maximum value in current data；
Define five kinds of output valves, respectively better, mean, ooffset, goffset, Pre.Five kinds of output valves are right respectively
Answer following several Fusion Strains:
1) better: magnetometer data is utilized merely.
2) mean: the offset of magnetometer is relatively low at this time.Magnetometer t-4 being averaged to the deflection of t moment is taken at this time
Value is as bearing data at this time
3) ooffset: magnetometer offset at this time is serious and gyroscope is in tolerance interval, switches to another fusion skill
Art is merged according to weight, the weight of the weight ratio gyroscope of magnetometer wants low at this time.
4) goffset: gyroscope drift is serious at this time and magnetometer offset is in tolerance interval, switches to another fusion
Technology is merged according to weight, the weight of the weight ratio gyroscope of magnetometer wants high at this time.
5) pre: if the deviation of gyroscope and magnetometer all than more serious, utilizes the deflection of last moment at this time.
The If-Then dicision rules of fuzzy logic are given later:
Wherein, m-ori and m-tuo respectively represents the magnetic at the current time and last moment that are collected into using mobile phone sensor
The changing value of the data of power meter and gyroscope, range represent corresponding output area.
The If-Then dicision rules of given fuzzy logic；
Wherein, m-ori and m-tuo respectively represents the magnetic at the current time and last moment that are collected into using mobile phone sensor
The changing value of the data of power meter and gyroscope, range represent corresponding output area.
If m-tuo belongs to low state or med state and m-ori belongs to high state so output area and is
ooffset；
If m-ori belongs to low state or med state and m-tuo belongs to high state so output area and is
goffset；
If it is better that m-ori, which belongs to high state so output area,；
If it is mean that m-ori, which belongs to med state so output area,；
If m-ori belongs to low state and m-tuo belongs to low state so output area for pre；
Corresponding blending algorithm is selected according to given range output valve later.Fuzzy logic deterministic process is detailed in lower Fig. 2.
Corresponding blending algorithm is selected according to given range output valve；
Whether pedestrian is detected in the straight line stage by formula (3):
||θmi|-|θmi-1| | > Th1 ∪ | | θmi|-|θmi-1| | < Th2 (3)
Wherein, θmIt indicates the measured value of deflection, uses the deflection θ at current time respectivelymiWith the deflection of last moment
θmi-1Seek difference and to be less than given threshold value Th1, while the θ at i momentmiNeed the angle, θ with the i+1 momentmi+1Difference it is small
It in threshold value Th2, is limited by above-mentioned two condition, so that it is determined that pedestrian's walking states.
Determine in pedestrian movement's state procedure using formula (3), in turning, fusion deflection is counted using formula (4)
It calculates:
anglet=ωog(ωoriangleori t-1+ωorianglegyor t-1) (4)
Direction fusion angle under straight-going state is calculated using formula (5):
anglet=ωpog(ωpreanglet-1+ωorianglet-1 ori+ωgyoanglet-1 gyor) (5)
Wherein,
anglet、anglet-1The deflection of respectively fused deflection and last moment, ωpre、ωori、ωgyoPoint
Not Wei a moment deflection weight, magnetometer weight and gyroscope weight, magnetometer offset at this time is serious and gyroscope exists
Tolerance interval, then the ratio between last moment weight is ωpre: ωori: ωgyo=2:1:2, on the contrary it is 2:2:1.
Step 3: the terrestrial reference identified by WiFi peak point is added, to particle position in the particle filter based on map match
It is modified.Specific amendment flow chart is as shown in Figure 3.
In step 3, the particle filter based on map match is carried out using formula (8),
Wherein, p (x0) be dynamical system always prior probability distribution, k moment system mode xkPosterior probability distribution be
p(x0:k|z1:k), N represents number of particles, x0:kSystem mode of the expression system from 0 to k moment, z1:kThe observation of system is represented,
It indicates at a distance from PD result in the application, the weight of particle meets normalizing condition shown in formula (9)
Weight is updated as shown in formula (10),
Constraint of the right value update of particle by cartographic information, when particle updates weight by particle right value update through walls
It is 0, whereinFor pedestrian position posterior probability, it is expressed as formula (11)
When pedestrian is in straight-going state, according to formula (11) more new particle output position, when system detection is to anchor point or
When person detects pedestrian's turn condition, the data stored in database are updated to particle output position at this time, this up-to-date style
(12) indicate that i-th of particle at a distance from terrestrial reference, is used for the location updating of particle next time at time k later:
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.
Certainly, the above description is not a limitation of the present invention, and the present invention is also not limited to the example above, this technology neck
The variations, modifications, additions or substitutions that the technical staff in domain is made within the essential scope of the present invention also should belong to of the invention
Protection scope.
Claims (4)
1. the indoor orientation method of a kind of fusion fuzzy logic judgement and cartographic information, which comprises the following steps:
Step 1: indoor pedestrian track calculates, using two-dimensional vector [x, y]TIndicate the position of pedestrian, and according to formula (1) to pedestrian
Position be updated:
Wherein, [xi-1,yi-1]TRepresent the position of the (i-1)-th walking people, θiWith siRespectively represent walking direction and the step-length of the i-th step；
Step 2: the adaptive weighting deflection blending algorithm based on fuzzy logic judgement is calculated it is first determined whether enabling fusion
Method or the simple direction angular data using a certain optimum state；If after judgement enables blending algorithm, according to gyroscope and
The data cases of the actual acquisition of magnetometer, adaptive adjustment weight are passed through with selecting the blending algorithm for being suitble to current state
The relevance threshold of given gyroscope and magnetometer recycles the deflection of magnetometer to judge walking shape after determining the two correlation
State, it is determined whether last moment deflection is added, is finally merged according to adaptive weighting；
Step 3: the terrestrial reference identified by WiFi peak point is added in the particle filter based on map match, carries out to particle position
Amendment.
2. the indoor orientation method of a kind of fusion fuzzy logic judgement as described in claim 1 and cartographic information, feature exists
In, in the step 2,
The shift state of gyroscope and magnetometer is divided into three kinds, corresponding three sections, later to needing input data to pass through formula
(2) it is standardized:
Wherein, st indicates the standardized value of output, valuetFor current input value, min (value), max (value) generation respectively
Minimum value and maximum value in table current data；
Five kinds of output valves are defined, five kinds of output valves respectively correspond respective Fusion Strain；
The If-Then dicision rules of given fuzzy logic；
Corresponding blending algorithm is selected according to given range output valve；
Whether pedestrian is detected in the straight line stage by formula (3):
||θmi|-|θmi-1| | > Th1 ∪ | | θmi|-|θmi-1| | < Th2 (3)
Wherein, θmIt indicates the measured value of deflection, uses the deflection θ at current time respectivelymiWith the deflection θ of last momentmi-1It asks
Difference makes the difference be less than given threshold value Th1, while the θ at i momentmiNeed the angle, θ with the i+1 momentmi+1Difference be less than
Threshold value Th2 is limited by above-mentioned two condition, so that it is determined that pedestrian's walking states.
3. the indoor orientation method of a kind of fusion fuzzy logic judgement as claimed in claim 2 and cartographic information, feature exists
In in using formula (3) judgement pedestrian movement's state procedure, in turning, fusion deflection is calculated using formula (4):
anglet=ωog(ωoriangleori t-1+ωorianglegyor t-1) (4)
Direction fusion angle under straight-going state is calculated using formula (5):
anglet=ωpog(ωpreanglet-1+ωorianglet-1 ori+ωgyoanglet-1 gyor) (5)
Wherein,
anglet、anglet-1The deflection of respectively fused deflection and last moment, ωpre、ωori、ωgyoRespectively
The weight of one moment deflection weight, magnetometer weight and gyroscope, magnetometer offset at this time is serious and gyroscope can connect
By range, then the ratio between last moment weight is ωpre: ωori: ωgyo=2:1:2, on the contrary it is 2:2:1.
4. the indoor orientation method of a kind of fusion fuzzy logic judgement as described in claim 1 and cartographic information, feature exists
In, in the step 3, the particle filter based on map match is carried out using formula (8),
Wherein, p (x0) be dynamical system always prior probability distribution, k moment system mode xkPosterior probability distribution be p
(x0:k|z1:k), N represents number of particles, x0:kSystem mode of the expression system from 0 to k moment, z1:kThe observation of system is represented,
It indicates at a distance from PD result in the application, the weight of particle meets normalizing condition shown in formula (9)
Weight is updated as shown in formula (10),
Constraint of the right value update of particle by cartographic information, when particle update weight when by particle right value update through walls be 0,
WhereinFor pedestrian position posterior probability, it is expressed as formula (11)
When pedestrian is in straight-going state, according to formula (11) more new particle output position, when system detection is to anchor point or inspection
When measuring pedestrian's turn condition, the data stored in database are updated to particle output position at this time, this up-to-date style (12) table
Show that i-th of particle at a distance from terrestrial reference, is used for the location updating of particle next time at time k later:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910587333.1A CN110345939B (en) | 2019-07-02 | 2019-07-02 | Indoor positioning method integrating fuzzy logic judgment and map information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910587333.1A CN110345939B (en) | 2019-07-02 | 2019-07-02 | Indoor positioning method integrating fuzzy logic judgment and map information |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110345939A true CN110345939A (en) | 2019-10-18 |
| CN110345939B CN110345939B (en) | 2021-03-19 |
Family
ID=68177536
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910587333.1A Active CN110345939B (en) | 2019-07-02 | 2019-07-02 | Indoor positioning method integrating fuzzy logic judgment and map information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110345939B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860354A (en) * | 2020-07-23 | 2020-10-30 | 成都民航空管科技发展有限公司 | System track output method and device based on multi-monitoring-source target fusion weight |
| CN112631288A (en) * | 2020-12-09 | 2021-04-09 | 上海欧菲智能车联科技有限公司 | Parking positioning method and device, vehicle and storage medium |
| CN112729282A (en) * | 2020-12-21 | 2021-04-30 | 杭州电子科技大学 | Indoor positioning method integrating single anchor point ranging and pedestrian track calculation |
| CN113810846A (en) * | 2021-10-15 | 2021-12-17 | 湖南大学 | Indoor positioning method based on WiFi and IMU fusion |
| CN115406435A (en) * | 2022-08-24 | 2022-11-29 | 同济大学 | Indoor electronic map construction method and device based on WLAN and MEMS and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106845716A (en) * | 2017-01-25 | 2017-06-13 | 东南大学 | A kind of unmanned surface vehicle local delamination paths planning method based on navigation error constraint |
| CN107255474A (en) * | 2017-05-11 | 2017-10-17 | 杭州电子科技大学 | A kind of PDR course angles of fusion electronic compass and gyroscope determine method |
| CN108362289A (en) * | 2018-02-08 | 2018-08-03 | 浙江大学城市学院 | A kind of mobile intelligent terminal PDR localization methods based on Multi-sensor Fusion |
-
2019
- 2019-07-02 CN CN201910587333.1A patent/CN110345939B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106845716A (en) * | 2017-01-25 | 2017-06-13 | 东南大学 | A kind of unmanned surface vehicle local delamination paths planning method based on navigation error constraint |
| CN107255474A (en) * | 2017-05-11 | 2017-10-17 | 杭州电子科技大学 | A kind of PDR course angles of fusion electronic compass and gyroscope determine method |
| CN108362289A (en) * | 2018-02-08 | 2018-08-03 | 浙江大学城市学院 | A kind of mobile intelligent terminal PDR localization methods based on Multi-sensor Fusion |
Non-Patent Citations (7)
| Title |
|---|
| EDITH PULIDO HERRERA 等: "Pedestrain Localization System based on Kalman Filter and Fuzzy logic", 《SENSORS 2006 IEEE》 * |
| HAITAO BAO 等: "A Novel Map-Based Dead-Reckoning Algorithm for", 《JOURNAL OF SENSOR AND ACTUATOR NETWORKS》 * |
| JAU-HSIUNG WANG 等: "MULTI-SENSOR DATA FUSION FOR LAND VEHICLE ATTITUDE", 《DATA SCIENCE JOURNAL》 * |
| JINGJING SHI 等: "Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point Map", 《MICROMACHINES》 * |
| 姚浩: "融合Landmarks和BMO-PF的室内PDR定位研究与实现", 《中国优秀硕士学位论文全文数据库》 * |
| 章雪挺: "基于模糊卡尔曼的MEMS陀螺误差校正算法研究", 《杭州电子科技大学学报（自然科学版）》 * |
| 赵世华 等: "基于惯性传感器与WiFi的室内行人轨迹推算", 《山东理工大学学报（自然科学版）》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860354A (en) * | 2020-07-23 | 2020-10-30 | 成都民航空管科技发展有限公司 | System track output method and device based on multi-monitoring-source target fusion weight |
| CN111860354B (en) * | 2020-07-23 | 2022-09-02 | 成都民航空管科技发展有限公司 | System track output method and device based on multi-monitoring-source target fusion weight |
| CN112631288A (en) * | 2020-12-09 | 2021-04-09 | 上海欧菲智能车联科技有限公司 | Parking positioning method and device, vehicle and storage medium |
| CN112631288B (en) * | 2020-12-09 | 2023-01-06 | 上海欧菲智能车联科技有限公司 | Parking positioning method and device, vehicle and storage medium |
| CN112729282A (en) * | 2020-12-21 | 2021-04-30 | 杭州电子科技大学 | Indoor positioning method integrating single anchor point ranging and pedestrian track calculation |
| CN112729282B (en) * | 2020-12-21 | 2022-03-25 | 杭州电子科技大学 | Indoor positioning method integrating single anchor point ranging and pedestrian track calculation |
| CN113810846A (en) * | 2021-10-15 | 2021-12-17 | 湖南大学 | Indoor positioning method based on WiFi and IMU fusion |
| CN113810846B (en) * | 2021-10-15 | 2022-05-03 | 湖南大学 | Indoor positioning method based on WiFi and IMU fusion |
| CN115406435A (en) * | 2022-08-24 | 2022-11-29 | 同济大学 | Indoor electronic map construction method and device based on WLAN and MEMS and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110345939B (en) | 2021-03-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110345939A (en) | A kind of indoor orientation method merging fuzzy logic judgement and cartographic information | |
| US11187540B2 (en) | Navigate, track, and position mobile devices in GPS-denied or GPS-inaccurate areas with automatic map generation | |
| CA2991505C (en) | Scalable indoor navigation and positioning systems and methods | |
| CN106412826B (en) | Indoor orientation method and positioning device based on Multi-source Information Fusion | |
| Sun et al. | Indoor positioning tightly coupled Wi-Fi FTM ranging and PDR based on the extended Kalman filter for smartphones | |
| CN109298389A (en) | Indoor pedestrian based on multiparticle group optimization combines position and orientation estimation method | |
| US8626443B2 (en) | Method for creating a map relating to location-related data on the probability of future movement of a person | |
| ES2366875T3 (en) | DEVICE AND PROCEDURE FOR UPDATING CARTOGRAPHIC DATA. | |
| CN105606102B (en) | Grid model based PDR indoor positioning method and system | |
| CN108519615A (en) | Mobile robot autonomous navigation method based on integrated navigation and Feature Points Matching | |
| Gilliéron et al. | Indoor navigation performance analysis | |
| Ren et al. | A fuzzy logic map matching for wheelchair navigation | |
| CN108632761A (en) | A kind of indoor orientation method based on particle filter algorithm | |
| CN109141427A (en) | EKF localization method under nlos environment based on distance and angle probabilistic model | |
| CN110260855A (en) | A kind of indoor pedestrian navigation localization method merging pedestrian's dead reckoning, Geomagnetism Information and indoor map information | |
| Li et al. | WiFi/PDR integrated navigation with robustly constrained Kalman filter | |
| CN108759834A (en) | A kind of localization method based on overall Vision | |
| Blazquez et al. | Simple map-matching algorithm applied to intelligent winter maintenance vehicle data | |
| Lee et al. | Indoor localization by Kalman filter based combining of UWB-positioning and PDR | |
| CN108020813A (en) | Localization method, positioner and electronic equipment | |
| CN108759815A (en) | A kind of information in overall Vision localization method merges Combinated navigation method | |
| Wen et al. | An indoor localization and tracking system using successive weighted RSS projection | |
| Li et al. | Automatic construction of radio maps by crowdsourcing PDR traces for indoor positioning | |
| CN114727384A (en) | Bluetooth RSSI positioning method based on weighted min-max | |
| Alaoui et al. | A multi-hypothesis particle filtering approach for pedestrian dead reckoning |
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 |