CN109164411B - Personnel positioning method based on multi-data fusion - Google Patents
Personnel positioning method based on multi-data fusion Download PDFInfo
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- CN109164411B CN109164411B CN201811041952.2A CN201811041952A CN109164411B CN 109164411 B CN109164411 B CN 109164411B CN 201811041952 A CN201811041952 A CN 201811041952A CN 109164411 B CN109164411 B CN 109164411B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0257—Hybrid positioning
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0284—Relative positioning
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/10—Position of receiver fixed by co-ordinating a plurality of position lines defined by path-difference measurements, e.g. omega or decca systems
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract
The invention discloses a personnel positioning method based on multi-data fusion, which is a positioning method integrating WIFI, an inertial sensor and geomagnetic information. The invention utilizes machine learning and acceleration data to identify the motion state of pedestrians, and performs Pedestrian Dead Reckoning (PDR) after obtaining the data of step length, step number and direction angle. Based on the PDR positioning, the accumulated error of the PDR is improved by fusing geomagnetic matching positioning results, and the WIFI anchor point is adopted to realize position initialization and correct positioning errors. The multi-data dynamic fusion algorithm based on particle filtering improves the system precision and robustness, is free from the interference of external environment, has low cost, is convenient to carry, and can be applied to complex environments such as indoor environments, tunnels, underground mine and the like.
Description
Technical Field
The invention relates to a personnel positioning method based on multi-data fusion.
Background
In recent years, the development of internet technology is mature, and mobile intelligent terminals are rapidly popularized. The pedestrian is positioned and navigated in real time by the Location Based Service (LBS), so that real-time effective related information is provided for the pedestrian, and convenience is brought to the life of people. It has wide application in various fields of vehicle navigation, commodity transportation logistics, traffic management, medical aid and personal positioning.
WIFI positioning is the determination of the position coordinates of a target point by collecting the signal strength from a wireless access point at the target point. Because of the complexity of indoor environment, the WIFI fingerprint positioning accuracy is not high and unstable.
Small inertial sensors have become a standard for various smart handsets, providing the potential for handheld mobile based Pedestrian Dead Reckoning (PDR). The PDR can be continuously and autonomously positioned under the condition of not depending on external information, and has small noise and good stability in a short period.
The geomagnetic field has rich characteristics such as total intensity, vector intensity, magnetic inclination angle, declination angle and intensity gradient, and the like, and provides sufficient matching information for geomagnetic matching. However, the magnetic signals are easily interfered by electric and magnetic signal sources which change continuously in the environment, the positioning result is unstable, and the precision can be affected.
In summary, any indoor positioning technology has advantages and limitations, such as the advantages and complementarity of the indoor positioning technology can be brought into play in a cooperative manner if the indoor positioning technology can be used for making a best of the advantages and the disadvantages, and the positioning accuracy can be further improved.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a personnel positioning method based on multi-data fusion, which overcomes the defects of a single positioning technology, exerts the self-advantages and complementarity of each positioning technology in a cooperative manner, and improves the positioning precision. The dynamic weighting fusion algorithm provided by the invention realizes the multi-data fusion of WIFI positioning, PDR positioning and geomagnetic matching positioning, and improves the positioning effect. The PDR positioning has small noise and high precision in a short time, and is used for calculating the relative distance and obtaining the final position; geomagnetic matching positioning has the characteristic of no accumulation of errors in the whole day, and is used for correcting the positioning result of the PDR and reducing noise; and the WIFI short-distance positioning accuracy is high, and the WIFI short-distance positioning accuracy is used for initializing particles and serving as an anchor node for updating positions.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a personnel positioning method based on multi-data fusion, which adopts a particle filtering method to fuse position information obtained by WIFI fingerprint positioning, a PDR positioning algorithm and a geomagnetic matching positioning algorithm, thereby realizing personnel positioning;
the method specifically comprises the following steps:
step 2, estimating the position of each particle in the step 1 by using a PDR positioning algorithm and a geomagnetic matching positioning algorithm respectively, and carrying out weighted fusion on the results of the PDR positioning algorithm and the geomagnetic matching positioning algorithm to obtain the position prediction of each particle;
step 3, updating the particle weight, and carrying out normalization processing on the updated particle weight;
step 4, calculating the current position of the personnel according to the updated particle weight;
step 5, if the current WIFI signal strength RSSI exceeds a set threshold, outputting the position information determined by the WIFI anchor point as a current personnel positioning result, and returning to the step 2 after resampling and particle updating; otherwise, outputting the current position result of the personnel calculated in the step 4 as a current personnel positioning result.
As a further technical solution of the present invention, the radius r in step 1 is determined according to the WIFI signal value.
As a further technical scheme of the present invention, in step 2, the weighted fusion is performed on the results of the PDR positioning algorithm and the geomagnetic matching positioning algorithm, specifically:
P＝P PDR ·θ(Δt)+P DC ·(1-θ(Δt))
wherein P is the position after weighting and fusing the results of the PDR positioning algorithm and the geomagnetic matching positioning algorithm, and P PDR Is the result of the PDR positioning algorithm, P DC Is the result of the geomagnetic matching and positioning algorithm, θ (Δt) is the weight occupied by the PDR positioning algorithm,
Δt is the positioning duration, γ is the positioning duration when the result of the PDR positioning algorithm drops to 3dB of the actual position, and the value of n is confirmed by the actual positioning environment.
As a further technical scheme of the invention, the mean value is 0 and the variance is sigma in the step 3 2 The gaussian distribution description particle weight updating method, the updated particle weight is:
in the method, in the process of the invention,
is the current i-th particle position.
As a further technical solution of the present invention, the current position of the person in step 4 is:
in the method, in the process of the invention,
is the updated weight, P i The position of the ith particle after the weighted fusion of the results of the PDR positioning algorithm and the geomagnetic matching positioning algorithm.
As a further technical scheme of the present invention, in step 5, resampling is performed by using a polynomial resampling method, specifically:
1) All particles are arranged into x according to the updated weight size sequence 1 ,x 2 ,…,x N And construct a discrete cumulative distribution function
2) Generating random numbers tau obeying uniform distribution in the intervals of [0,1], sequentially comparing with F (K) until the particles corresponding to the current F (K) > tau are copied as new particles when F (K) > tau, and the weight of the new particles is 1/N;
3) And repeatedly executing the step 2) for N times to generate N new particles, thereby completing the updating of the particles.
As a further technical solution of the present invention, the PDR positioning algorithm in step 2 specifically includes: firstly, identifying the motion state of a person according to accelerometer data, and then adopting a PDR algorithm to position the person according to the motion state of the person.
As a further technical scheme of the invention, the motion state of the personnel is specifically identified according to the accelerometer data: firstly, carrying out windowed average filtering on acceleration data, and then classifying the filtered data by adopting a K-means clustering algorithm, so as to identify the motion state of personnel.
As a further aspect of the present invention, the accelerometer data includes an amplitude average Mod, a variance Var, a peak Max, and a spectrum maximum frequency MF.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
1) The multi-data dynamic fusion algorithm based on particle filtering improves the system precision and robustness, is free from the interference of external environment, has low cost, is convenient to carry, and can be applied to complex environments such as indoor environments, tunnels, underground mine and the like;
2) The invention overcomes the defects of a single positioning technology, exerts the self-advantages and complementarity of each positioning technology in a cooperative manner, and improves the positioning precision.
Drawings
FIG. 1 is a block flow diagram of the method of the present invention;
FIG. 2 is a flow chart of a particle filtering algorithm;
FIG. 3 is a flow chart of geomagnetic matching localization;
fig. 4 is a flow chart of state identification in combination with PDR positioning.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings:
the invention aims to provide a positioning method based on multi-data fusion, and a positioning system comprises a hardware terminal and a WEBGIS display platform. In the moving process, inertial motion information of a target, WIFI in the environment and geomagnetic signals are transmitted to a server through the WIFI, and finally, a particle filter fusion algorithm is adopted to estimate the position of the target and display the position on a GIS platform in real time.
The invention relates to a personnel positioning method based on multi-data fusion, which aims to realize personnel positioning with convenient deployment, low cost and real-time high precision. In the specific implementation process, the following operations are required:
(1) Calibration of inertial sensors. Under the influence of the process conditions, the inertial device has errors such as quantization noise, random walk and zero offset instability. Heretofore, error cancellation has been required for inertial sensors. Based on the long correlation and instability of the sensor output noise, the invention adopts a threshold denoising algorithm based on wavelet analysis.
(2) And (5) constructing a digital map. After the pedestrian position is obtained by utilizing the PDR algorithm and the landmark positioning algorithm, the specific spatial position of the target is determined by combining the position map information. The map information constraint is mainly to compensate and correct the positioning error according to the indoor building structure, so that the convergence speed of the positioning process can be increased, the positioning accuracy can be improved, and the system robustness can be enhanced. In the construction process, the thickness of building structures such as walls, doors and windows is ignored, the building is split into a plurality of layers of planes (a plurality of polygons), each plane is formed by connecting a wearable boundary and a non-wearable boundary, and the structures such as the walls, the doors and the like are represented in an abstract mode. In order to reduce the searching range when updating the particle filtering weight, the different rooms (Room) in each layer of the building are uniquely numbered as a whole; each region is internally divided into a plurality of convex polygons (Poly) with side lengths of 1-3 meters and numbered. The four vertexes of the polygon are represented by two-dimensional coordinates (corresponding positions on the map), then the type of each side is defined, the wall body is null (0), the non-wall body is ref (1), and a group of completed map data can be formed after all planes are defined, wherein the map data comprise the position of the region and the types of the boundaries.
The invention discloses a personnel positioning method based on multi-data fusion, which is a positioning method integrating WIFI, an inertial sensor and geomagnetic information. The invention utilizes machine learning and acceleration data to identify the motion state of pedestrians, and performs Pedestrian Dead Reckoning (PDR) after obtaining the data of step length, step number and direction angle. Based on the PDR positioning, the accumulated error of the PDR is improved by fusing geomagnetic matching positioning results, and the WIFI anchor point is adopted to realize position initialization and correct positioning errors. The multi-data dynamic fusion algorithm based on particle filtering provided by the invention improves the system precision and robustness, is free from the interference of external environment, has low cost, is convenient to carry, can be applied to complex environments such as indoor, tunnel, underground mine and the like, and is intended to realize the indoor positioning with convenient deployment, low cost and real-time high precision.
The invention provides a personnel positioning method based on multi-data fusion, which adopts a particle filtering method to fuse position information obtained by WIFI fingerprint positioning, a PDR positioning algorithm and a geomagnetic matching positioning algorithm, thereby realizing personnel positioning.
PDR positioning algorithm: combining machine learning and a traditional PDR algorithm to judge the gesture of a pedestrian, adopting different step models and step frequency detection thresholds according to different walking modes, and solving the current relative position P PDR . The extracted accelerometer frequency characteristics comprise an amplitude average value (Mod), a variance (Var), a peak value (Max) and a frequency spectrum Maximum Frequency (MF), and the extracted accelerometer frequency characteristics are classified by using a K-means clustering algorithm, so that different motion postures of pedestrians are identified, as shown in fig. 4.
Geomagnetic matching location algorithm (as shown in fig. 3): geomagnetic information is collected by utilizing a magnetometer, a geomagnetic fingerprint database is established, and the fingerprint of each position is taken as a starting point to geomagnetism of the positionTime series. Adopting a FAST-DTW algorithm, judging that the matching is successful once the accumulated distance reaches the minimum value, and representing the current time position of each particle obtained by geomagnetic matching as P DC . The forward geomagnetic fingerprint, the reverse geomagnetic fingerprint and the subsequent geomagnetic fingerprint collected on line are used for matching, and the probability that the to-be-positioned points appear on the same path is improved. Once the cumulative distance reaches a minimum, a successful match is determined and the current inferred position is calibrated to the position of the track inference when the pending site first appears at that position.
WIFI fingerprint positioning: by adopting the traditional WIFI fingerprint positioning method, a fingerprint library is built only within 10 meters of a base station square circle, and places which cannot pass are not measured.
The invention provides a personnel positioning method based on multi-data fusion, which is shown in fig. 1 and 2 and specifically comprises the following steps:
And 2, estimating the position (state) of each particle in the step 1 by using a PDR positioning algorithm and a geomagnetic matching positioning algorithm, and carrying out weighted fusion on the results of the PDR positioning algorithm and the geomagnetic matching positioning algorithm to obtain the position prediction of each particle.
The PDR positioning has high short-time precision, the accumulated error continuously reduces the positioning precision along with the time, and the geomagnetic positioning with better stability is adopted at the moment. On the basis, the invention constructs a dynamic weight function to fuse PDR positioning and geomagnetic matching positioning:
P＝P PDR ·θ(Δt)+P DC ·(1-θ(Δt))
wherein P is the position after weighting and fusing the results of the PDR positioning algorithm and the geomagnetic matching positioning algorithm, and P PDR Is the result of the PDR positioning algorithm, P DC Is geomagnetic matching and positioning algorithmResults; θ (Δt) is the weight that the PDR positioning algorithm takes, is a decreasing function of Δt, i.e. "trust" in PDR positioning gradually decreases over time,
Δt is the positioning duration, γ is the positioning duration when the result of the PDR positioning algorithm drops to 3dB of the actual position, and n is determined by the actual positioning environment, and n=3 is generally taken.
And step 3, updating the particle weight, and carrying out normalization processing on the updated particle weight. With mean 0 and variance sigma 2 The gaussian distribution description particle weight updating method, the updated particle weight is:
in the method, in the process of the invention,
is the current i-th particle position; sigma (sigma) 2 The size of (2) is determined according to the actual situation. Particle weight updating is combined with map information, so that the suitability and robustness of the system and the map information can be improved.
Step 4, calculating the current position of the personnel according to the updated particle weight:
in the method, in the process of the invention,
is the updated weight, P i The position of the ith particle after the weighted fusion of the results of the PDR positioning algorithm and the geomagnetic matching positioning algorithm.
Step 5, if the current WIFI signal strength RSSI exceeds a set threshold, outputting the position information determined by the WIFI anchor point as a current personnel positioning result, and returning to the step 2 after resampling and particle updating; otherwise, outputting the current position result of the personnel calculated in the step 4 as a current personnel positioning result.
The effective particle number ESS being below a certain threshold, which indicates that the particle degradation is severe, polynomial resampling is initiated.
Resampling is carried out by adopting a polynomial resampling method, and the method specifically comprises the following steps:
1) All particles are arranged into x according to the updated weight size sequence 1 ,x 2 ,…,x N And construct a discrete cumulative distribution function
2) Generating random numbers tau obeying uniform distribution in the intervals of [0,1], sequentially comparing with F (K) until the particles corresponding to the current F (K) > tau are copied as new particles when F (K) > tau, and the weight of the new particles is 1/N;
3) And repeatedly executing the step 2) for N times to generate N new particles, thereby completing the updating of the particles.
The foregoing is merely illustrative of the embodiments of the present invention, and the scope of the present invention is not limited thereto, and any person skilled in the art will appreciate that modifications and substitutions are within the scope of the present invention, and the scope of the present invention is defined by the appended claims.
Claims (6)
1. A personnel positioning method based on multi-data fusion is characterized in that a particle filtering method is adopted to fuse position information obtained by WIFI fingerprint positioning, PDR positioning algorithm and geomagnetic matching positioning algorithm, so that personnel positioning is achieved;
the method specifically comprises the following steps:
step 1, determining a current position range of a person by using a WIFI anchor point: at the location (x 0 ,y 0 ,z 0 ) At radius rUniformly scattering N particles, wherein the weight of each particle is 1/N, and the particles are mutually independent and meet Gaussian distribution;
step 2, estimating the position of each particle in the step 1 by using a PDR positioning algorithm and a geomagnetic matching positioning algorithm respectively, and carrying out weighted fusion on the results of the PDR positioning algorithm and the geomagnetic matching positioning algorithm to obtain the position prediction of each particle;
the weighted fusion is carried out on the results of the PDR positioning algorithm and the geomagnetic matching positioning algorithm, specifically:
P＝P PDR ·θ(Δt)+P DC ·(1-θ(Δt))
wherein P is the position after weighting and fusing the results of the PDR positioning algorithm and the geomagnetic matching positioning algorithm, and P PDR Is the result of the PDR positioning algorithm, P DC Is the result of the geomagnetic matching and positioning algorithm, θ (Δt) is the weight occupied by the PDR positioning algorithm,
Δt is the positioning duration time, γ is the positioning duration time when the result of the PDR positioning algorithm drops to 3dB of the actual position, and the value of n is confirmed by the actual positioning environment;
step 3, updating the particle weight, and carrying out normalization processing on the updated particle weight;
wherein the mean value is 0 and the variance is sigma 2 The gaussian distribution description particle weight updating method, the updated particle weight is:
in the method, in the process of the invention,
is the current i-th particle position;
step 4, calculating the current position of the personnel according to the updated particle weight;
step 5, if the current WIFI signal strength RSSI exceeds a set threshold, outputting the position information determined by the WIFI anchor point as a current personnel positioning result, and returning to the step 2 after resampling and particle updating; otherwise, outputting the current position result of the personnel calculated in the step 4 as a current personnel positioning result;
the resampling is carried out by adopting a polynomial resampling method, which comprises the following steps:
1) All particles are arranged into x according to the updated weight size sequence 1 ,x 2 ,…,x N And construct a discrete cumulative distribution function
2) Generating random numbers tau obeying uniform distribution in the intervals of [0,1], sequentially comparing with F (K) until the particles corresponding to the current F (K) > tau are copied as new particles when F (K) > tau, and the weight of the new particles is 1/N;
3) And repeatedly executing the step 2) for N times to generate N new particles, thereby completing the updating of the particles.
2. The personnel positioning method based on multi-data fusion according to claim 1, wherein the radius r in the step 1 is determined according to the WIFI signal value.
3. The personnel positioning method based on multi-data fusion according to claim 1, wherein the current position of the personnel in the step 4 is:
in the method, in the process of the invention,
is the updated weight, P i The result of the PDR positioning algorithm and the geomagnetic matching positioning algorithm is weighted and fusedThe position of the i-th particle.
4. The personnel positioning method based on multi-data fusion according to claim 1, wherein the PDR positioning algorithm in step 2 is specifically: firstly, identifying the motion state of a person according to accelerometer data, and then adopting a PDR algorithm to position the person according to the motion state of the person.
5. The personnel positioning method based on multi-data fusion according to claim 4, wherein the identifying of the movement state of the personnel according to the accelerometer is specifically: firstly, carrying out windowed average filtering on acceleration data, and then classifying the filtered data by adopting a K-means clustering algorithm, so as to identify the motion state of personnel.
6. The method of claim 4, wherein the accelerometer data includes an average magnitude Mod, a variance Var, a peak Max, and a maximum frequency MF.
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|---|---|---|---|---|
| CN110121150B (en) * | 2019-05-24 | 2021-03-19 | 上海物联网有限公司 | Indoor trans-regional seamless positioning system based on UWB technology |
| CN110300385A (en) * | 2019-07-09 | 2019-10-01 | 桂林理工大学 | A kind of indoor orientation method based on adaptive particle filter |
| CN110285809B (en) * | 2019-07-15 | 2020-12-08 | 李素敏 | Indoor and outdoor integrated combined positioning device |
| CN110307849B (en) * | 2019-07-15 | 2020-12-08 | 李素敏 | Personnel indoor positioning and indoor and outdoor integrated positioning method |
| CN111623765B (en) * | 2020-05-18 | 2022-07-01 | 清华大学 | Indoor positioning method and system based on multi-mode data |
| CN111595344B (en) * | 2020-06-01 | 2023-02-24 | 中国矿业大学 | Multi-posture downlink pedestrian dead reckoning method based on map information assistance |
| CN112261606B (en) * | 2020-09-28 | 2021-09-07 | 南京邮电大学 | Self-adaptive indoor fusion positioning method based on dynamic environment |
| CN112964243B (en) * | 2021-01-11 | 2024-05-28 | 重庆市蛛丝网络科技有限公司 | Indoor positioning method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104180805A (en) * | 2014-08-29 | 2014-12-03 | 中国海洋大学 | Smart phone-based indoor pedestrian positioning and tracking method |
| CN104977006A (en) * | 2015-08-11 | 2015-10-14 | 北京纳尔信通科技有限公司 | Indoor positioning method based on fuzzy theory and multi-sensor fusion |
| CN106197418A (en) * | 2016-06-22 | 2016-12-07 | 西安交通大学 | The indoor orientation method that a kind of fingerprinting based on sliding window merges with sensor |
| CN107396321A (en) * | 2017-08-02 | 2017-11-24 | 江南大学 | Unsupervised formula indoor orientation method based on mobile phone sensor and iBeacon |
| CN108225324A (en) * | 2017-12-27 | 2018-06-29 | 中国矿业大学 | A kind of indoor orientation method merged based on the geomagnetic matching of intelligent terminal with PDR |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102034527B1 (en) * | 2014-02-11 | 2019-10-21 | 한국전자통신연구원 | System for filtering location of Mobile terminal by fusing wi-fi location and sensing information |
-
2018
- 2018-09-07 CN CN201811041952.2A patent/CN109164411B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104180805A (en) * | 2014-08-29 | 2014-12-03 | 中国海洋大学 | Smart phone-based indoor pedestrian positioning and tracking method |
| CN104977006A (en) * | 2015-08-11 | 2015-10-14 | 北京纳尔信通科技有限公司 | Indoor positioning method based on fuzzy theory and multi-sensor fusion |
| CN106197418A (en) * | 2016-06-22 | 2016-12-07 | 西安交通大学 | The indoor orientation method that a kind of fingerprinting based on sliding window merges with sensor |
| CN107396321A (en) * | 2017-08-02 | 2017-11-24 | 江南大学 | Unsupervised formula indoor orientation method based on mobile phone sensor and iBeacon |
| CN108225324A (en) * | 2017-12-27 | 2018-06-29 | 中国矿业大学 | A kind of indoor orientation method merged based on the geomagnetic matching of intelligent terminal with PDR |
Non-Patent Citations (2)
| Title |
|---|
| 基于PDR、WiFi指纹识别、磁场匹配组合的室内行人导航定位;张鹏 等;《测绘地理信息》;20160630;第41卷(第3期);第29-37页 * |
| 基于WiFi、PDR、地磁相融合的室内定位研究;宋宇等;《微电子学与计算机》;20180605(第06期);第60-68页 * |
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