CN113048977B - Indoor geomagnetic positioning method integrating radio waves and inertial sensor - Google Patents
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Abstract
The invention discloses an indoor geomagnetic positioning method fusing radio waves and an inertial sensor, which comprises an off-line stage and an on-line stage, wherein the off-line stage acquisition stage comprises the following steps: generating a corresponding signal space map after data acquisition is carried out on an area to be positioned through the mobile terminal; in the on-line positioning stage, the method comprises the following steps: a. calculating a relative position point A according to an inertial sensor in the mobile terminal; b. calculating the position by adopting a fingerprint method according to Wi-Fi, i Beacon and geomagnetic signals in the mobile terminal; c. and B), fusing the relative position A calculated by the PDR with the absolute position B calculated by the Wi-Fi signal, the i Beacon signal and the geomagnetic signal on the basis of the step a) and the step B) to obtain a fused position C. The indoor positioning system is reasonable in design, aims to further simplify indoor positioning by fusing radio waves and an inertial sensor on the premise of not changing indoor positioning precision, avoids the problem that large-scale hardware deployment is needed for indoor positioning based on geomagnetic signals, and is also beneficial to indoor positioning.
Description
Technical Field
The invention relates to the technical field of indoor positioning processing, in particular to an indoor geomagnetic positioning method fusing radio waves and an inertial sensor.
Background
The global satellite navigation system represented by GPS has reshaped human life in the past three decades, and has become an important technical milestone in modern society. Services based on the location of the GPS have become a part of life, playing a major role in business and social interaction. Especially the widespread use of mobile terminals and other wireless devices, has directly fueled the market demand for indoor location data. However, GPS does not provide reliable position data under indoor conditions due to non-line-of-sight communication problems. Therefore, indoor positioning technology is becoming a hot spot area for academic research and industrial applications.
The existing commercial indoor positioning systems can be generally divided into four types according to the positioning technology supported by the existing commercial indoor positioning systems, namely a first type of indoor positioning system based on a computer vision technology, a second type of indoor positioning system based on a wireless communication technology, a third type of indoor positioning system based on an LED visible light technology and a fourth type of indoor positioning system based on geomagnetic matching. The existing indoor positioning system based on geomagnetic matching is commonly used in the fourth category, large-scale hardware deployment is not needed for the construction of the indoor positioning system based on the geomagnetic, the geomagnetic signals do not have the problem of non-line-of-sight communication, and interference of shadow effect, multipath effect and the like of radio waves does not exist, and the geomagnetic signals are relatively stable as long as the indoor space structure is kept substantially unchanged. However, the current indoor positioning algorithm based on geomagnetic signals has the following problems:
first, not all indoor locations have strong geomagnetic fingerprint characteristics, but a geomagnetic positioning algorithm usually employs a deep learning technique to extract high-order correlation characteristics of geomagnetic signals and construct a complex fingerprint algorithm. For indoor positioning systems based on geomagnetic signals, cloud servers with strong computing power are generally required to be deployed, so that the landing cost of the indoor positioning systems is increased, and the complexity of system implementation is greatly increased;
secondly, in order to further solve the problem of insufficient geomagnetic fingerprint characteristics, indoor geomagnetic-based positioning algorithms usually merge other types of indoor signals, such as Wi-Fi or iBeacon signals. In order to obtain a good positioning effect, a Kalman algorithm or a particle filter algorithm is usually selected as a fusion algorithm, the modeling of the algorithm is complex, the calculated amount is large, and in the landing process of an indoor positioning system, the design algorithm is often customized according to the actual conditions of customers and venues, so that the landing period of a project is greatly increased, and the labor cost is increased.
In view of the foregoing, it is an urgent need to improve the existing indoor geomagnetic positioning technology.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an indoor geomagnetic positioning method fusing a radio wave and an inertial sensor, which is reasonable in design, aims to further simplify indoor positioning by fusing the radio wave and the inertial sensor on the premise of not changing the accuracy of the indoor positioning, and avoids the need of large-scale hardware deployment based on indoor positioning of geomagnetic signals.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
an indoor geomagnetic positioning method fusing radio wave and an inertial sensor comprises an off-line stage and an on-line stage,
the off-line phase acquisition phase comprises the following steps:
1) Data acquisition is carried out on an area to be positioned through the mobile terminal: a user holds a mobile terminal to collect radio wave signals and geomagnetic signal data in an area to be positioned for a period of time, wherein the radio wave signals are Wi-Fi-free signals and iBeacon signals;
2) Acquiring radio wave signals and geomagnetic signal data for a period of time through a mobile terminal and generating a corresponding signal space map;
in the on-line positioning stage, the method is carried out on the basis of the off-line stage acquisition stage, and comprises the following steps:
a. calculating a relative position point A according to an inertial sensor in the mobile terminal;
1) Initializing, namely initializing and setting the mobile terminal before a user holds the mobile terminal and starts to walk;
2) A user holds a mobile terminal to start walking, and a walking time sequence is calculated by acquiring an accelerometer signal on the mobile terminal and applying a step detection algorithm on the mobile terminal;
3) Calculating a step length through the mobile terminal;
4) The mobile terminal calculates a relative position point A estimated by the inertial sensor on the basis of a pedestrian course calculation formula PDR and the steps 1), 2) and 3);
b. calculating the position by adopting a fingerprint method according to Wi-Fi, iBeacon and geomagnetic signals in the mobile terminal;
1) Limiting the alternative position points of the fingerprint method to be close to the PDR output position points;
2) Respectively calculating matching scores of a signal value of an alternative position point in a signal space map and an observation signal value during positioning according to an Euclidean distance formula for a Wi-Fi signal, an iBeacon signal and a geomagnetic signal, wherein a neighbor threshold value is set to be K, and thus different matching score sets exist for each signal source;
3) And for Wi-Fi signals, iBeacon signals and geomagnetic signals, scoring the signal discrimination between alternative points, wherein the calculation formula is as follows:
wherein,
represents the signal source k, the discrimination score between the alternative points i and j, and->
A matching score, representing an alternative point i>
A matching score representing an alternative point j;
4) For Wi-Fi signals, iBeacon signals and geomagnetic signals, calculating the signal discrimination of a single alternative point, wherein the calculation formula is as follows:
wherein N represents the number of the alternative points,
a high signal source k indicates a higher discrimination at the alternative point i and will ≥>
Performing normalization processing to obtain a weight score>
5) And calculating the sum of the weight scores of all signal sources of each alternative point, and selecting the highest alternative point as the absolute position B of Wi-Fi/iBeacon/geomagnetic signal output, wherein the calculation formula is as follows:
wherein,
representing the discrimination scores of the signal source k at the alternative points i after the normalization processing on the signal source dimension, and selecting FS i The highest alternative point is used as an absolute position B of Wi-Fi/iBeacon/geomagnetic signal output; c. fusing the relative position A calculated by the PDR with the absolute position B calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal on the basis of the step a) and the step B), and calculating to obtain a fused position C, wherein the calculation formula is as follows:
x＝(1-α)*x PDR +α*x RF-MAG
y＝(1-α)*y PDR +α*y RF-MAG
wherein α is:
wherein D (P) PDR ，P Prev ) Indicates the distance, D (P), of the PDR position from the last position RF-MAG ，P Prew ) And the distance between the position calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal and the last position is represented. When FS is used RF-MAG The value is larger, namely when the discrimination scores given by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal are higher, the fusion position C calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal is more trusted. Similarly, when the PDR is farther away from the last position, the Wi-Fi signal, iBeaco, is also trustfullyn signal and geomagnetic signal.
Preferably, the inertial sensor in the mobile terminal is an acceleration sensor, an angular velocity sensor or a magnetic sensor; or an inertial measurement unit IMU consisting of a single, double or three-axis combination of an acceleration sensor and an angular velocity sensor and a resolving circuit, or an attitude reference system AHRS consisting of a single, double or three-axis combination of an acceleration sensor, an angular velocity sensor and a magnetic sensor and a resolving circuit;
preferably, the inertial sensor in the mobile terminal is a micro-electromechanical system (MEMS) sensor.
Preferably, the step length calculated by the mobile terminal in the step 3) is specifically as follows: firstly, a triaxial acceleration sensor in a mobile terminal acquires triaxial acceleration values generated when a person walks, detects the walking pace of the person by using a characteristic-matched step counting algorithm, and records the timestamp of the walking pace; secondly, acquiring the direction of the detected walking pace of the person according to a direction sensor in the mobile terminal; and thirdly, calculating the step frequency of the person when the person walks according to the difference of the time stamps of the adjacent steps of the person walking, and estimating the step length of the person when the person walks according to a relation table of the step frequency and the height when the person walks.
Preferably, the pedestrian heading calculation formula PDR is:
E r and N r Representing the east and north coordinates of the user in the ENU coordinate system, S r Representing the step size, alpha, of the step r Indicating the heading of the step. Note that this formula has a premise that it is reasonable to assume that the user's heading is constant in the first step (step or step), i.e., straight in the step, as is known from experience.
Preferably, the mobile terminal is a smart terminal, and the smart terminal is one or more of a smart phone, a tablet computer, a personal digital assistant, a handheld game console, a personal navigation device, a wearable device, smart glasses, a smart watch, a virtual display device, or a display enhancement device.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the indoor positioning system, the mobile terminal is adopted, and a cloud server with high computing power does not need to be deployed, so that the landing cost of the indoor positioning system is increased, and the complexity of system implementation is greatly reduced;
(2) In the invention, a Kalman algorithm or/and a particle filter algorithm do not need to be fused, so that the requirement and the installation of a large processor are reduced, and the cost investment is reduced;
(3) According to the invention, only indoor Wi-Fi signals and iBeacon signals need to be combined through the mobile equipment, and then combined with geomagnetic signals to finally obtain a fusion position, the design is reasonable, on the premise that the indoor positioning precision is not changed, the radio wave and the inertial sensor are fused to further simplify indoor positioning, hardware needs to be deployed on a large scale based on the indoor positioning of the geomagnetic signals are avoided, so that the indoor positioning precision is higher, and the indoor positioning requirement can be met.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail by embodiments with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, an indoor geomagnetic positioning method fusing a radio wave and an inertial sensor includes an off-line stage and an on-line stage,
the off-line phase acquisition phase comprises the following steps:
1) Data acquisition is carried out on an area to be positioned through the mobile terminal: a user holds a mobile terminal to collect radio wave signals and geomagnetic signal data in an area to be positioned for a period of time, wherein the radio wave signals are Wi-Fi-free signals and iBeacon (Bluetooth) signals;
the Wi-Fi router is mainly used for meeting internet access requirements of markets, merchants and customers, the Wi-Fi router is installed in a common market as a part of communication infrastructure in the market (signal coverage is considered when the Wi-Fi router is installed, so that the distribution of the Wi-Fi router is generally distributed and arranged as uniformly as possible on the basis of considering construction conditions), and a lot of merchants also install Bluetooth iBeacon equipment for marketing promotion activities such as store coupons, applets and custom pages, so that the Wi-Fi router or the Bluetooth iBeacon equipment can be considered to be equipment commonly existing in the market, the geomagnetic signal is from the earth magnetic field, and artificial hardware is not needed.
2) Acquiring radio wave signals and geomagnetic signal data for a period of time through a mobile terminal and generating a corresponding signal space map;
the signal space map is a commonly used technology in the fingerprint method, namely a database obtained through an acquisition process, data is the corresponding relation of 'position points-signals', and the signals refer to a mixed data structure of radio waves (Wi-Fi and iBeacon) and geomagnetic signal data in the text. In the process of generating the signal space map, two steps are carried out: the method comprises the steps that interpolation is carried out, radio wave data are not interpolated, geomagnetic signals are subjected to linear interpolation algorithm, and position points which are not collected in the collection process are mainly obtained through the interpolation algorithm; and secondly, gridding is carried out, the central point of the grid is used as a collecting position point, and averaging processing is carried out on the central point and the data in the grid.
In the off-line stage acquisition stage, an area to be positioned (target area) is sampled (interval traversal sampling), and then RSSI values of a plurality of nearby APs received by each positioning point are used as position fingerprint information of the positioning point and are stored.
In the on-line positioning stage, the method is carried out on the basis of the off-line stage acquisition stage, and comprises the following steps:
a. calculating a relative position point A according to an inertial sensor in the mobile terminal;
1) Initializing, namely initializing and setting the mobile terminal before a user holds the mobile terminal and starts to walk;
2) A user holds a mobile terminal to start walking, and a walking time sequence is calculated by acquiring an accelerometer signal on the mobile terminal and applying a step detection algorithm on the mobile terminal;
3) Calculating the step length by the mobile terminal according to a Weinberg walking estimation algorithm;
acquiring a triaxial acceleration value generated when a person walks according to a triaxial acceleration sensor in the mobile terminal, detecting the walking pace of the person by using a characteristic-matched step counting algorithm, and recording a timestamp of the walking pace; acquiring the direction of a detected walking pace of a person according to a direction sensor in the mobile terminal; calculating the step frequency of the walking of the person according to the difference of the time stamps of the adjacent steps of the walking of the person, and estimating the step length of the walking of the person according to a relation table of the step frequency and the height of the walking of the person.
4) The mobile terminal calculates a relative position point A estimated by the inertial sensor on the basis of a pedestrian course calculation formula PDR and the steps 1), 2) and 3);
the pedestrian course calculation formula PDR is as follows:
wherein E is r And N r Representing the east and north coordinates of the user in the ENU coordinate system, S r Indicates the step size, α, of the step r Indicating the heading of the step. Note that this formula has a premise that it is reasonable to assume that the user's heading is constant in the first step (step or step), i.e., straight in the step, as is known from experience. b. Calculating the position by adopting a fingerprint method according to Wi-Fi, iBeacon and geomagnetic signals in the mobile terminal, wherein the Wi-Fi signals are from a Wi-Fi chip of the mobile terminal, the iBeacon signals are from a Bluetooth chip of the mobile terminal, and the geomagnetic signals are from a magnetometer of the mobile terminal;
1) Limiting the alternative position points of the fingerprint method to be close to the PDR output position points;
the alternative points are all acquisition position points within a certain distance R by taking a position point output by the PDR as a center, namely acquisition points in the fingerprint acquisition process, wherein R is an algorithm parameter, and the position point output by the PDR is a position point output by a pedestrian course calculation formula PDR.
2) Respectively calculating matching scores of a signal value of an alternative position point in a signal space map and an observation signal value during positioning according to an Euclidean distance formula for a Wi-Fi signal, an iBeacon signal and a geomagnetic signal, wherein a neighbor threshold value is set to be K, and thus different matching score sets exist for each signal source;
wherein the Euclidean distance formula can obtain more detailed reference data and other resources, for example, inhttps:// baike.baidu.com/item/％E6％AC％A7％E5％87％A0％E9％87％8C％E5％BE％97％E5％ BA％A6％E9％87％8F/1274107fromtitle＝％E6％AC％A7％E6％B0％8F％E8％B7％9D％ E7％A6％BB&fromid＝1798948The above.
3) And for Wi-Fi signals, iBeacon signals and geomagnetic signals, scoring the signal discrimination between alternative points, wherein the calculation formula is as follows:
wherein,
a discrimination Score (discrimination Score) between candidate points i and j when representing a signal source k (Wi-Fi signal, iBeacon signal, and geomagnetic signal), and/or a judgment value>
Matching Score (Matching Score) representing alternate point i, -,>
match Score representing alternate point j (Matching Score); 4) Calculating the signal of a single alternative point for Wi-Fi signal, iBeacon signal and geomagnetic signalNumber discrimination, the calculation formula is as follows:
wherein N represents the number of the alternative points,
a high signal source k has a higher degree of discrimination at alternative point i and will +>
Performing normalization processing to obtain a weight score>
5) Calculating the sum of the weight scores of all signal sources of each alternative point, and selecting the highest alternative point as the absolute position B of Wi-Fi/iBeacon/geomagnetic signal output, wherein the calculation formula is as follows:
wherein,
representing the discrimination scores of the signal source k at the alternative points i after the normalization processing on the signal source dimension, and selecting FS i The highest alternative point is used as an absolute position B of Wi-Fi/iBeacon/geomagnetic signal output;
c. on the basis of the step a) and the step B), fusing the relative position A calculated by the PDR and the absolute position B calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal to calculate a fused position C, wherein the calculation formula is as follows:
x＝(1-α)*x PDR +α*x RF-MAG
y＝(1-α)*y PDR +α*y RF-MAG
wherein α is:
wherein D (P) PDR ，P Prev ) Indicates the distance, D (P), of the PDR position from the last position RF-MAG ，P Prew ) And the distance between the position calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal and the last position is represented. When FS is used RF-MAG The value is larger, namely when the discrimination scores given by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal are higher, the fusion position C calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal is more trusted. Similarly, when the PDR is farther from the last position, the fusion position C calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal is also trusted more at this time.
In the online positioning stage, a position fingerprint formed by RSSI of a lattice AP received by a mobile terminal at an unknown position is matched with fingerprint information of a known position in a database by using a matching algorithm, so that the position with the highest matching degree is the current position of the terminal, and the positioning function is mainly realized.
A mobile terminal refers to a device or apparatus that can sense the motion, position and/or heading of a pedestrian in space. For example, the mobile terminal may include, but is not limited to, an inertial measurement unit, a camera, a barometer, and the like. The mobile terminal is also a smart terminal, and the smart terminal may be one or more of a smart phone (smart phone), a tablet, a personal digital assistant (pda), a handheld game console, a personal navigation device (pnd), a wearable device, smart glasses, a smart watch, a virtual display device or a display enhancement device (such as googleglass, oculusrift, hololens, gearvr), and the like. The mobile terminal may be associated with a platform of a conveyance device. Platforms may include, but are not limited to, pedestrians, vehicles transporting pedestrians, vessels, and the like. The intelligent terminal can be fixed on the platform or not, the intelligent terminal can be connected with the platform through a network, and a series of calculation algorithms exist in the platform, so that the storage burden of the intelligent terminal is favorably reduced, and the occupied memory is reduced. The most preferable mobile terminal in the invention is a smart phone, which has wide application and is convenient to carry.
The inertial sensor in the mobile terminal is an acceleration sensor, an angular velocity sensor or a magnetic sensor; or an inertial measurement unit IMU consisting of a single, double or three-axis combination of an acceleration sensor and an angular velocity sensor and a resolving circuit, or an attitude reference system AHRS consisting of a single, double or three-axis combination of an acceleration sensor, an angular velocity sensor and a magnetic sensor and a resolving circuit; the inertial sensor in the mobile terminal is a micro-electromechanical system (MEMS) sensor.
In order to solve the problems of complex algorithm design, complex system architecture and high power consumption in the current geomagnetic indoor positioning field and effectively utilize the advantage that a geomagnetic signal-based indoor positioning algorithm does not need to deploy hardware on a large scale, the invention provides a lightweight geomagnetic positioning algorithm integrating radio waves and an inertial sensor in an indoor environment.
For a mobile terminal, the electric quantity is a valuable resource, namely an iOS operating system closed by apple company, an iOS operating system opened by Google company, and an android operating system secondarily developed by a plurality of mobile phone manufacturers. In order to improve the overall energy efficiency ratio of the mobile terminal and seize the market share, more limitation and monitoring are added to the electric quantity consumption of the mobile terminal. The characteristic of large calculation amount of the existing geomagnetic algorithm makes the geomagnetic algorithm difficult to further popularize in the consumer electronics market, and the limitation and monitoring can be correspondingly reduced through the geomagnetic estimation method, so that the power consumption of the mobile terminal can be reduced, and the power consumption using time of the mobile terminal can be greatly prolonged.
In a word, the method solves the problems of complex algorithm design, complex system architecture and high power consumption in the current geomagnetic indoor positioning field, and can effectively utilize the advantage that the indoor positioning algorithm based on the geomagnetic signals does not need to deploy hardware in a large scale.
It should be noted that, although the above embodiments have been described herein, the invention is not limited thereto. Therefore, based on the innovative concepts of the present invention, the technical solutions of the present invention can be directly or indirectly applied to other related technical fields by making changes and modifications to the embodiments herein or by using equivalent structures or equivalent processes performed in the content of the present specification and the attached drawings, which are included in the scope of the present patent.
Claims (4)
1. An indoor geomagnetic positioning method integrating radio wave and inertial sensors is characterized by comprising an off-line stage and an on-line stage,
the off-line phase acquisition phase comprises the following steps:
1) Data acquisition is carried out on an area to be positioned through the mobile terminal: a user holds a mobile terminal to collect radio wave signals and geomagnetic signal data in an area to be positioned for a period of time, wherein the radio wave signals are Wi-Fi signals and iBeacon signals;
2) Acquiring radio wave signals and geomagnetic signal data for a period of time through a mobile terminal and then generating a corresponding signal space map;
in the process of generating the signal space map, two steps are carried out: the method comprises the steps that interpolation is carried out, radio wave data are not interpolated, geomagnetic signals are subjected to linear interpolation algorithm, and position points which are not collected in the collection process are obtained through the interpolation algorithm; secondly, gridding is carried out, the central point of a grid is used as a collecting position point, and data in the same grid are averaged;
in the on-line positioning stage, the off-line stage acquisition stage is based on the above, and the method comprises the following steps:
a. calculating a relative position point A according to an inertial sensor in the mobile terminal;
1) Initializing, namely initializing and setting the mobile terminal before a user holds the mobile terminal and starts to walk;
2) A user holds a mobile terminal to start walking, and a walking time sequence is calculated by acquiring an accelerometer signal on the mobile terminal and applying a step detection algorithm on the mobile terminal;
3) Calculating step length through the mobile terminal;
4) The mobile terminal calculates a relative position point A estimated by the inertial sensor according to a pedestrian course calculation formula PDR and the steps 1), 2) and 3);
b. calculating the position by adopting a fingerprint method according to Wi-Fi, iBeacon and geomagnetic signals in the mobile terminal;
1) Limiting the alternative position points of the fingerprint method to be close to the PDR output position points;
2) Respectively calculating matching scores of a signal value of an alternative position point in a signal space map and an observation signal value during positioning according to an Euclidean distance formula for a Wi-Fi signal, an iBeacon signal and a geomagnetic signal, wherein a neighbor threshold value is set to be K, and thus different matching score sets exist for each signal source;
3) And for Wi-Fi signals, iBeacon signals and geomagnetic signals, scoring the signal discrimination between alternative points, wherein the calculation formula is as follows:
wherein,
a discrimination score between alternate points i and j, representing signal source k, in->
Matching score representing alternate point i>
A matching score representing an alternative point j;
4) For Wi-Fi signals, iBeacon signals and geomagnetic signals, calculating the signal discrimination of a single alternative point, wherein the calculation formula is as follows:
wherein N representsThe number of the selected points is equal to the total number of the selected points,
a high signal source k indicates a higher discrimination at the alternative point i and will ≥>
Performing normalization to obtain a weight score>
5) Calculating the sum of the weight scores of all signal sources of each alternative point, and selecting the highest alternative point as the absolute position B of Wi-Fi/iBeacon/geomagnetic signal output, wherein the calculation formula is as follows:
wherein,
representing the discrimination scores of the signal source k at the alternative points i after the normalization processing on the signal source dimension, and selecting FS i The highest alternative point is used as an absolute position B of Wi-Fi/iBeacon/geomagnetic signal output; c. on the basis of the step a) and the step B), fusing the relative position A calculated by the PDR and the absolute position B calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal to calculate a fused position C, wherein the calculation formula is as follows:
x＝(1-α)*x PDR +α*x RF-MAG
y＝(1-α)*y PDR +α*y RF-MAG
wherein α is:
wherein D (P) PDR ，P Prev ) Indicates the distance of the PDR position from the last position, D (P) RF-MAG ，P Prew ) Representing the distance between the position calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal and the last position; when FS is available RF-MAG The value is larger, namely when the discrimination scores given by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal are higher, the fusion position C calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal is more trusted; similarly, when the distance between the PDR and the last position is farther, the fusion position C calculated by the Wi-Fi signal, the iBeacon signal and the geomagnetic signal is more trusted; the pedestrian course calculation formula PDR is as follows:
E r and N r Representing the east and north coordinates of the user in the ENU coordinate system, S r Indicates the step size, α, of the step r Indicating the course of the step; assume that the user's heading within a first step, step or step return, is constant, i.e., a straight line is taken within the step.
2. The indoor geomagnetic positioning method combining radio wave and inertial sensors, according to claim 1, wherein the inertial sensor in the mobile terminal is a MEMS sensor.
3. The method as claimed in claim 1 or 2, wherein the step length calculated by the mobile terminal in step 3) is as follows: firstly, a triaxial acceleration sensor in a mobile terminal acquires triaxial acceleration values generated when a person walks, detects the walking pace of the person by using a characteristic-matched step counting algorithm, and records the timestamp of the walking pace; secondly, acquiring the direction of the detected walking pace of the person according to a direction sensor in the mobile terminal; and thirdly, calculating the step frequency when the person walks according to the difference of the timestamps of the adjacent steps of the person walking, and estimating the step length when the person walks according to a relation table of the step frequency and the height when the person walks.
4. The indoor geomagnetic positioning method combining radio wave and inertial sensor according to claim 1 or 2, wherein the mobile terminal is a smart terminal, and the smart terminal is one or more of a smart phone, a tablet computer, a personal digital assistant, a handheld game console, smart glasses, a smart watch, a virtual display device, or a display enhancement device.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106093843A (en) * | 2016-05-31 | 2016-11-09 | 天津大学 | A kind of smart phone user indoor orientation method based on earth magnetism auxiliary WiFi |
| CN110320495A (en) * | 2019-08-01 | 2019-10-11 | 桂林电子科技大学 | A kind of indoor orientation method based on Wi-Fi, bluetooth and PDR fusion positioning |
| CN110954109A (en) * | 2019-12-12 | 2020-04-03 | 杭州十域科技有限公司 | Indoor space positioning equipment and positioning method based on fingerprint positioning and inertial navigation system |
| CN110986997A (en) * | 2019-10-25 | 2020-04-10 | 杭州十域科技有限公司 | Method and system for improving indoor inertial navigation precision |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10341982B2 (en) * | 2014-10-28 | 2019-07-02 | Invensense, Inc. | Technique and system of positioning a mobile terminal indoors |
| US9906914B2 (en) * | 2016-05-11 | 2018-02-27 | Mapsted Corp. | Scalable indoor navigation and positioning systems and methods |
| CN110081888B (en) * | 2019-05-15 | 2021-06-22 | 华南师范大学 | Indoor positioning algorithm based on credibility-based inertial navigation and geomagnetic fusion |
| 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 |
| CN110779513B (en) * | 2019-10-28 | 2021-06-18 | 杭州十域科技有限公司 | Indoor public service place positioning system |
| CN111901749A (en) * | 2020-08-29 | 2020-11-06 | 桂林电子科技大学 | High-precision three-dimensional indoor positioning method based on multi-source fusion |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106093843A (en) * | 2016-05-31 | 2016-11-09 | 天津大学 | A kind of smart phone user indoor orientation method based on earth magnetism auxiliary WiFi |
| CN110320495A (en) * | 2019-08-01 | 2019-10-11 | 桂林电子科技大学 | A kind of indoor orientation method based on Wi-Fi, bluetooth and PDR fusion positioning |
| CN110986997A (en) * | 2019-10-25 | 2020-04-10 | 杭州十域科技有限公司 | Method and system for improving indoor inertial navigation precision |
| CN110954109A (en) * | 2019-12-12 | 2020-04-03 | 杭州十域科技有限公司 | Indoor space positioning equipment and positioning method based on fingerprint positioning and inertial navigation system |
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