CN116184312A - Indoor crowdsourcing fingerprint library construction method based on semantic Wi-Fi - Google Patents
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Abstract
The invention belongs to the technical field of indoor positioning, and discloses a semantic Wi-Fi-based indoor crowdsourcing fingerprint library construction method, which adopts crowdsourcing mode positioning to construct a semantic Wi-Fi indoor crowdsourcing fingerprint library, so that a user of positioning service can be a data provider at the same time, the Wi-Fi fingerprint acquisition workload is reduced, and the semantic Wi-Fi-based indoor crowdsourcing fingerprint library construction method has the advantages of high activity, abundant data, low cost, large data volume and the like; and after the system deployment is completed, the support can be provided for updating the data, and the stability and the applicability of the system are further enhanced through repeated iteration and virtuous circle.
Description
Technical Field
The invention belongs to the field of indoor positioning, and particularly relates to an indoor crowdsourcing fingerprint library construction method based on semantic Wi-Fi.
Background
Along with the high-speed development of social economy and mobile communication networks, intelligent equipment is popularized in people, various mobile phone applications based on location services, such as Goodyear maps and beauty groups, provide convenience for people's clothing and eating houses, and the importance of the location services can see a spot. These applications are currently based mainly on satellite navigation based GNSS signal positioning techniques to obtain the position of the user. The GNSS technology has the advantages of high positioning accuracy, wide coverage range and strong real-time performance, and GNSS chips are installed on most smartphones. However, in the GNSS positioning technology, positioning performance is drastically reduced in Non-Line-of-Sight (NLOS) environments such as indoor environments and urban canyons. Therefore, developing an effective new technology for localization is a research hotspot in industry and academia.
In recent years, there have been many researches and researches in the field of positioning, and a lot of mature positioning technologies such as Ultra Wideband (UWB), infrared, radio Frequency Identification (RFID), zigBee, bluetooth, etc. have appeared, and these positioning technologies have advantages of high accuracy, but support of special devices such as bluetooth beacons used in bluetooth positioning, transmitting and receiving devices of UWB positioning, etc. are mostly required. Because of the high cost of specialized equipment, it is generally difficult to popularize and apply. At present, a positioning technical scheme which is high in precision, low in cost and suitable for large-scale popularization is still lacking. Among the existing positioning technologies, the Wi-Fi signal strength-based positioning technology is outstanding in positioning technology because special hardware equipment is not needed and the Wi-Fi signal strength-based positioning technology is widely deployed in indoor environments such as large markets, office buildings and the like in various cities in China. At present, most of positioning technologies based on Wi-Fi signal intensity need earlier-stage survey of target positioning buildings and areas, so that deployment cost is high, and popularization and promotion are difficult. Therefore, how to reduce the deployment cost in Wi-Fi positioning systems is one of the problems that the current indoor positioning field is urgently needed to solve.
Through the above analysis, the problems and defects existing in the prior art are as follows:
(1) GNSS signals often are difficult to reach in complex spaces such as urban canyons, indoors, underground and the like due to signal shielding and attenuation, and positioning requirements in the shielded spaces such as indoors and the like are difficult to meet.
(2) The indoor positioning method based on Wi-Fi fingerprints has the advantages of high accuracy and high availability, but the Wi-Fi fingerprint acquisition work is time-consuming and labor-consuming, and is not beneficial to large-scale popularization of the positioning method.
Disclosure of Invention
Aiming at the technical problems of the prior positioning technology method, the invention provides an indoor crowdsourcing fingerprint library construction method based on semantic Wi-Fi. According to the invention, crowdsourcing mode positioning is adopted to construct a semantic Wi-Fi indoor crowdsourcing fingerprint library, a user of positioning service simultaneously becomes a data provider, wi-Fi fingerprint acquisition workload is reduced, and the method has the advantages of low cost and strong updating capability.
The invention is realized in such a way that the indoor crowdsourcing fingerprint library construction method based on semantic Wi-Fi comprises the following steps:
step one: combining semantic Wi-Fi information extraction of a map, extracting semantic Wi-Fi information of all floors in a building monomer according to a three-dimensional plan of the building monomer, and constructing a floor semantic Wi-Fi library in the building monomer;
step two: floor identification by utilizing semantic Wi-Fi information is carried out, floor-by-floor name matching is carried out by extracting Wi-Fi name information of crowdsourcing users and floor semantic Wi-Fi library information in a building single body, and the floor with the highest matching rate is the floor where the crowdsourcing users are located;
step three: dead reckoning the crowd source data after the floors are determined based on pedestrian position estimation of the motion sensor, calculating the step length and the course of the pedestrians through sensor characteristics of an accelerometer, a gyroscope and a magnetometer, recovering the motion trail of the pedestrians indoors, and obtaining the position coordinates of the pedestrians indoors;
step four: and constructing a fingerprint library based on the crowd source data, dividing the plan of different floors of the building monomer according to grids of 2m multiplied by 2m, matching the motion trail coordinates of all users of each floor to the grid nearest to the motion trail coordinates, and correlating Wi-Fi fingerprint information with geographic coordinates to form a Wi-Fi fingerprint map.
The invention further aims to provide an indoor crowd-sourced fingerprint library construction system based on semantic Wi-Fi, which comprises the following steps:
the semantic Wi-Fi information module is used for extracting semantic Wi-Fi information of all floors in the building monomer according to the three-dimensional plan of the building monomer and constructing a floor semantic Wi-Fi library in the building monomer;
the floor identification module is used for carrying out floor-by-floor name matching by extracting Wi-Fi name information of the crowdsourcing user and floor semantic Wi-Fi library information in the building unit, and the floor with the highest matching rate is the floor where the crowdsourcing user is located;
the pedestrian position module is used for dead reckoning the crowd source data after the floors are determined, calculating the step length and the course of the pedestrians through the sensor characteristics of the accelerometer, the gyroscope and the magnetometer, and recovering the indoor movement track of the pedestrians to obtain the indoor position coordinates of the pedestrians;
and constructing a fingerprint library, namely dividing the plane diagrams of different floors of the building monomer according to grids of 2m multiplied by 2m, matching the motion trail coordinates of all users of each floor to the grid nearest to the motion trail coordinates, and simultaneously associating Wi-Fi fingerprint information with geographic coordinates to form a Wi-Fi fingerprint diagram.
It is a further object of the present invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of any one of the above-mentioned improved semantic Wi-Fi based indoor crowdsourcing fingerprint library construction method.
It is a further object of the present invention to provide a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of any of the above-mentioned improved semantic Wi-Fi based indoor crowd-sourced fingerprint library construction methods.
The invention further aims to provide an information data processing terminal which is used for realizing the improved indoor crowdsourcing fingerprint library construction system based on semantic Wi-Fi.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
first, aiming at the technical problems in the prior art and the difficulty of solving the problems, the technical problems solved by the technical proposal of the invention are analyzed in detail and deeply by tightly combining the technical proposal to be protected, the results and data in the research and development process, and the like, and some technical effects brought after the problems are solved have creative technical effects. The specific description is as follows:
the invention adopts the crowdsourcing mode to locate, solves the problem that a great deal of time and labor cost are spent on constructing and updating the position fingerprint library, and the crowdsourcing mode is used for collecting the locating data, namely, a great deal of end users actively or passively provide open data containing geographic information and attribute information and created by user traveling to the public through the Internet, and has the advantages of high activity, abundant data, low cost, large data quantity and the like; by adopting the crowdsourcing mode, a user of the positioning service becomes a data provider at the same time, and even after the system deployment is completed, the support for updating the data can be provided, and the stability and the applicability of the system are further enhanced through repeated iteration and virtuous circle.
Second, as inventive supplementary evidence of the claims of the present invention, the following important aspects are also presented:
whether the technical scheme of the invention solves the technical problems that people want to solve all the time but fail to obtain success all the time is solved:
at present, most intelligent terminals do not have built-in barometer sensors, but the built-in barometers are difficult to accurately judge the floor where the user is located according to the barometer due to the characteristics of the barometer. Therefore, how to accurately determine the floor where the user is located in the fingerprint positioning method based on crowd-sourced data is one of difficulties. In addition, in the process of estimating the two-dimensional plane position information based on the built-in sensor of the general smart phone, such as an accelerometer and a gyroscope, only the relative position of the user can be obtained, and global positioning cannot be performed. This is one of the factors limiting the application of crowdsourcing data based fingerprint locating methods.
Different from wireless signals such as ultra-wideband, infrared rays, wireless radio frequency identification, zigBee, bluetooth and the like, wi-Fi wireless signals can collect AP point field intensity information and can collect AP point semantic information. And AP point semantic information has a direct correlation with scene location. Through Wi-Fi semantic feature matching, the location information of the environment where the user is located can be provided in the location service field rapidly and efficiently.
The invention provides an indoor crowdsourcing fingerprint library construction method based on semantic WiFi, which utilizes an indoor low-cost three-dimensional map to construct a semantic WiFi library of an indoor scene, can easily solve floor positioning and absolute position estimation in crowdsourcing positioning, and has great application potential.
Drawings
Fig. 1 (a) is a B1-layer semantic Wi-Fi information example diagram of a yingtai creative city provided by the embodiment of the present invention, (B) is a F1-layer semantic Wi-Fi information example diagram of a yingtai creative city provided by the embodiment of the present invention, and (c) is a F2-layer semantic WiFi information example diagram of a yingtai creative city provided by the embodiment of the present invention;
FIG. 2 is a graph of floor recognition results for a typical crowd-sourced user provided by an embodiment of the present invention;
FIG. 3 is a diagram of a motion profile of a typical crowd-sourced user provided by an embodiment of the present invention;
fig. 4 (a) is a B1 Wi-Fi fingerprint of the yingtai creative city provided by the embodiment of the present invention, (B) is a F1 Wi-Fi fingerprint of the yingtai creative city provided by the embodiment of the present invention, and (c) is a F2 Wi-Fi fingerprint of the yingtai creative city provided by the embodiment of the present invention;
fig. 5 is a graph of cumulative error distribution in the yintai creative city provided by an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In order to fully understand how the invention may be embodied by those skilled in the art, this section is an illustrative embodiment in which the claims are presented for purposes of illustration.
The embodiment of the invention provides an indoor crowdsourcing fingerprint library construction method based on semantic Wi-Fi, which comprises the following steps:
step one: combining semantic Wi-Fi information extraction of a map, extracting semantic Wi-Fi information of all floors in a building monomer according to a three-dimensional plan of the building monomer, and constructing a floor semantic Wi-Fi library in the building monomer;
step two: floor identification by utilizing semantic Wi-Fi information is carried out, floor-by-floor name matching is carried out by extracting Wi-Fi name information of crowdsourcing users and floor semantic Wi-Fi library information in a building single body, and the floor with the highest matching rate is the floor where the crowdsourcing users are located;
step three: dead reckoning the crowd source data after the floors are determined based on pedestrian position estimation of the motion sensor, calculating the step length and the heading of the pedestrians through thank you sensor features of the accelerometer, the gyroscope and the magnetometer, recovering the motion trail of the pedestrians indoors, and obtaining the position coordinates of the pedestrians indoors;
step four: and constructing a fingerprint library based on the crowd source data, dividing the plan of different floors of the building monomer according to grids of 2m multiplied by 2m, matching the motion trail coordinates of all users of each floor to the grid nearest to the motion trail coordinates, and correlating Wi-Fi fingerprint information with geographic coordinates to form a Wi-Fi fingerprint map.
The invention further aims to provide an indoor crowd-sourced fingerprint library construction system based on semantic Wi-Fi, which comprises the following steps:
the semantic Wi-Fi information module is used for extracting semantic Wi-Fi information of all floors in the building monomer according to the three-dimensional plan of the building monomer and constructing a floor semantic Wi-Fi library in the building monomer;
the floor identification module is used for carrying out floor-by-floor name matching by extracting Wi-Fi name information of the crowdsourcing user and floor semantic Wi-Fi library information in the building unit, and the floor with the highest matching rate is the floor where the crowdsourcing user is located;
the pedestrian position module is used for dead reckoning the crowd source data after the floors are determined, calculating the step length and the course of the pedestrians through the sensor characteristics of the accelerometer, the gyroscope and the magnetometer, and recovering the indoor movement track of the pedestrians to obtain the indoor position coordinates of the pedestrians;
and constructing a fingerprint library, namely dividing the plane diagrams of different floors of the building monomer according to grids of 2m multiplied by 2m, matching the motion trail coordinates of all users of each floor to the grid nearest to the motion trail coordinates, and simultaneously associating Wi-Fi fingerprint information with geographic coordinates to form a Wi-Fi fingerprint diagram.
Example 1
The embodiment of the invention takes the Lopa nationality 35 silver-Tai creative city of the mountain area of the Wuhan city in Hubei province as a typical user environment, and the scene is a large mall and the internal environment is complex.
The embodiment of the invention is illustrated by constructing a fingerprint library by using mass source data of B1-F2 layers of Yintai creative city, and comprises the following specific steps:
step one: and carrying out semantic Wi-Fi information extraction by combining with a map of the Yintai creative city of the Wuhan city, wherein the semantic Wi-Fi information extraction result of each floor is shown in figure 1.
Step two: floor recognition is performed by using semantic Wi-Fi information, and the floor recognition result of a typical crowd-sourced user is shown in fig. 2.
Step three: and integrating the three-dimensional floor recognition result of the user, and assisting in calculating the two-dimensional motion trail of the user. For example, motion trail calculation is performed on accelerometer, gyroscope and magnetometer data of a typical crowd-sourced user, and the trail of the typical user is obtained as shown in fig. 3, and can be seen to be consistent with the corridor main road of the layer F1 of the yintai creative city.
Step four: dividing the plane map of the Yintai creative city according to grids of 2m multiplied by 2m, matching the motion track coordinates of all users of each floor to the grids nearest to the motion track coordinates, and simultaneously associating Wi-Fi fingerprint information with coordinate positions to form a fingerprint map, wherein the fingerprint map of each floor of the Yintai creative city can be seen in fig. 4.
Step five: the data used for the fingerprint library positioning performance test are collected manually, and the test point information comprises position coordinates, floor coordinates and WiFi information. Performing fingerprint-based positioning test on the test point information and a crowd-sourced fingerprint library by using an STG-KNN algorithm to obtain estimated test point position coordinates, respectively performing floor accuracy calculation on the estimated position coordinates and real position coordinates, and performing plane error statistics to obtain that the floor identification accuracy of the Yintai creative city is 98%; the positioning accuracy of 68% was 7.7m, and the positioning accuracy of 95% was 16.0m, as shown in fig. 5.
In order to prove the inventive and technical value of the technical solution of the present invention, this section is an application example on specific products or related technologies of the claim technical solution.
The invention mainly protects an indoor crowdsourcing fingerprint library construction method based on semantic WiFi, which takes building B1-F1-F2 of Yintai creative city of large-scale market in Wuhan city as an example, and carries out the explanation of the indoor crowdsourcing fingerprint library construction method based on semantic WiFi, and comprises the following specific processes:
(1) Firstly, constructing a semantic WiFi library according to floor plan diagrams of three layers of creative cities B1-F1-F2 as shown in FIG. 1;
(2) Extracting semantic WiFi in the whole process of user movement, and matching with semantic WiFi libraries of different floors in step (1), wherein the matching result is shown in figure 2, and the floor where the user is currently located is seen to be F1;
(3) Estimating motion trajectories of users at different floors according to the accelerometer, the gyroscope and the magnetometer, wherein the trajectory estimation result at the F1 layer is shown in fig. 3;
(3) Based on the track data of the step (3), matching WiFi fingerprints (including mac addresses, signal strength and semantic names) of each track position by taking the time stamp as a reference, and constructing a WiFi fingerprint library of each layer, as shown in fig. 4;
(4) In order to test the construction accuracy of the fingerprint library, wiFi fingerprint points of the creative city test points are collected, the current position is estimated by matching with the constructed WiFi fingerprint library, error calculation is carried out on the WiFi fingerprint points and the actual positions of the test points, and an error accumulation distribution curve is obtained, as shown in fig. 5, 68% of positioning errors are 7.7m, and 95% of positioning errors are 16.0m.
The embodiment of the invention has a great advantage in the research and development or use process, compared with the prior art, the embodiment of the invention has great advantages, and the following is described with reference to data, charts and the like in the experimental process.
Firstly, constructing a semantic WiFi library according to floor plan diagrams of three layers of creative cities B1-F1-F2, as shown in figure 1; then extracting semantic WiFi of the whole user movement process, and matching with semantic WiFi libraries of different floors, wherein the matching result is shown in figure 2, and the current floor of the user is shown as F1; estimating motion trajectories of users at different floors according to the accelerometer, the gyroscope and the magnetometer, wherein the trajectory estimation result at the F1 layer is shown in fig. 3; based on the trace data, the WiFi fingerprints (including mac address, signal strength and semantic name) of each trace position are matched by taking the timestamp as a reference, and the construction of a WiFi fingerprint library of each layer is carried out, as shown in fig. 4; and acquiring WiFi fingerprints of the creative city test points, matching with the constructed WiFi fingerprint library to estimate the current position of the user, and calculating errors with the actual positions of the test points to obtain an error cumulative distribution curve, wherein as shown in figure 5, 68% of positioning errors are 7.7m, and 95% of positioning errors are 16.0m.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (8)
1. The indoor crowdsourcing fingerprint library construction method based on the semantic Wi-Fi is characterized in that crowdsourcing mode positioning is adopted to construct an indoor crowdsourcing fingerprint library of the semantic Wi-Fi, a user of positioning service simultaneously serves as a data provider, wi-Fi fingerprint collection workload is reduced, wi-Fi fingerprint information is associated with geographic coordinates, and a Wi-Fi fingerprint map is formed.
2. The method for building the semantic Wi-Fi-based indoor crowd-sourced fingerprint library of claim 1, wherein the crowd-sourced mode is used for collecting positioning data and providing open data containing geographic information and attribute information created by user travel to the public through internet by a large number of end users actively or passively.
3. The semantic Wi-Fi based indoor crowd-sourced fingerprint library construction method of claim 1, comprising the steps of:
(1) Combining semantic Wi-Fi information extraction of a map, extracting semantic Wi-Fi information of all floors in a building monomer according to a three-dimensional plan of the building monomer, and constructing a floor semantic Wi-Fi library in the building monomer;
(2) Floor identification by utilizing semantic Wi-Fi information is carried out, floor-by-floor name matching is carried out by extracting Wi-Fi name information of crowdsourcing users and floor semantic Wi-Fi library information in a building single body, and the floor with the highest matching rate is the floor where the crowdsourcing users are located;
(3) Dead reckoning the crowd source data after the floors are determined based on pedestrian position estimation of the motion sensor, calculating the step length and the course of the pedestrians through sensor characteristics of an accelerometer, a gyroscope and a magnetometer, recovering the motion trail of the pedestrians indoors, and obtaining the position coordinates of the pedestrians indoors;
(4) And constructing a fingerprint library based on the crowd source data, dividing the plan of different floors of the building monomer according to grids of 2m multiplied by 2m, matching the motion trail coordinates of all users of each floor to the grid nearest to the motion trail coordinates, and correlating Wi-Fi fingerprint information with geographic coordinates to form a Wi-Fi fingerprint map.
4. The semantic Wi-Fi based indoor crowd-sourced fingerprint library construction method of claim 1, comprising a semantic Wi-Fi based indoor crowd-sourced fingerprint library construction system.
5. The semantic Wi-Fi based indoor crowd-sourced fingerprint library construction method of claim 3, wherein the semantic Wi-Fi based indoor crowd-sourced fingerprint library construction system comprises:
the semantic Wi-Fi information module is used for extracting semantic Wi-Fi information of all floors in the building monomer according to the three-dimensional plan of the building monomer and constructing a floor semantic Wi-Fi library in the building monomer;
the floor identification module is used for carrying out floor-by-floor name matching by extracting Wi-Fi name information of the crowdsourcing user and floor semantic Wi-Fi library information in the building unit, and the floor with the highest matching rate is the floor where the crowdsourcing user is located;
the pedestrian position module is used for dead reckoning the crowd source data after the floors are determined, calculating the step length and the course of the pedestrians through the sensor characteristics of the accelerometer, the gyroscope and the magnetometer, and recovering the indoor movement track of the pedestrians to obtain the indoor position coordinates of the pedestrians;
and constructing a fingerprint library, namely dividing the plane diagrams of different floors of the building monomer according to grids of 2m multiplied by 2m, matching the motion trail coordinates of all users of each floor to the grid nearest to the motion trail coordinates, and simultaneously associating Wi-Fi fingerprint information with geographic coordinates to form a Wi-Fi fingerprint diagram.
6. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the improved semantic Wi-Fi based indoor crowd-sourced fingerprint library construction method of any one of claims 1 to 5.
7. A computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to perform the steps of the improved semantic Wi-Fi based indoor crowd-sourced fingerprint library construction method of any of claims 1 to 5.
8. An information data processing terminal, wherein the information data processing terminal is used for realizing the improved semantic Wi-Fi-based indoor crowd-sourced fingerprint library construction system according to claim 4.
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