CN113048977A - Indoor geomagnetic positioning method integrating radio waves and inertial sensor - Google Patents

Indoor geomagnetic positioning method integrating radio waves and inertial sensor Download PDF

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CN113048977A
CN113048977A CN202110255732.5A CN202110255732A CN113048977A CN 113048977 A CN113048977 A CN 113048977A CN 202110255732 A CN202110255732 A CN 202110255732A CN 113048977 A CN113048977 A CN 113048977A
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geomagnetic
mobile terminal
signals
ibeacon
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CN113048977B (en
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徐强
韩业强
吉喆
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Hangzhou Shiyu Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/04—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means
    • G01C21/08—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means involving use of the magnetic field of the earth
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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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-F i 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 invention has reasonable design, aims to further simplify indoor positioning by fusing radio waves and an inertial sensor on the premise of not changing the precision of indoor positioning, avoids the need of large-scale hardware deployment based on geomagnetic signal indoor positioning, and is also beneficial to indoor positioning.

Description

Indoor geomagnetic positioning method integrating radio waves and inertial sensor
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 features, but the geomagnetic positioning algorithm usually employs a deep learning technique to extract high-order correlation features 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 incorporate 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 integrating radio wave and inertial sensors 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 then 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 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:
Figure BDA0002968293900000031
wherein the content of the first and second substances,
Figure BDA0002968293900000032
representing the signal source k, the discrimination score between candidate points i and j,
Figure BDA0002968293900000033
a matching score representing the alternative point i,
Figure BDA0002968293900000034
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:
Figure BDA0002968293900000035
wherein N represents the number of the alternative points,
Figure BDA0002968293900000041
a high signal source k has a higher discrimination at the alternative point i and will
Figure BDA0002968293900000042
Normalization processing is carried out to obtain weight fraction
Figure BDA0002968293900000043
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:
Figure BDA0002968293900000044
wherein the content of the first and second substances,
Figure BDA0002968293900000045
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 FSiThe 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)*xPDR+α*xRF-MAG
y＝(1-α)*yPDR+α*yRF-MAG
wherein α is:
Figure BDA0002968293900000046
wherein D (P)PDR，PPrev) Indicates the distance, D (P), of the PDR position from the last positionRF-MAG，PPrew) 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 usedRF-MAGThe 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.
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:
Figure BDA0002968293900000051
Erand NrRepresenting the east and north coordinates of the user in the ENU coordinate system, SrRepresenting the step size, alpha, of the steprIndicating 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 beneficial effects that:
(1) according to the indoor positioning system, the mobile terminal is adopted, and a cloud server with high computing capacity is not required to be deployed, so that the landing cost of the indoor positioning system is improved, 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, so that a fusion position is finally obtained.
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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 then 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 provided: 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 according to a pedestrian course calculation formula PDR and the steps 1), 2) and 3);
the pedestrian course calculation formula PDR is as follows:
Figure BDA0002968293900000081
wherein E isrAnd NrRepresenting the east and north coordinates of the user in the ENU coordinate system, SrRepresenting the step size, alpha, of the steprIndicating 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 the acquisition position points within a certain distance R, namely the acquisition points in the fingerprint acquisition process, with the position points output by the PDR as the center, wherein R is an algorithm parameter, and the position points output by the PDR are the position points output by the 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.***.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:
Figure BDA0002968293900000091
wherein the content of the first and second substances,
Figure BDA0002968293900000092
representing the signal source k (Wi-Fi signal, iBeacon signal, and geomagnetic signal), the discrimination Score (discrimination Score) between candidate points i and j,
Figure BDA0002968293900000093
the Matching Score for alternative point i (Matching Score),
Figure BDA0002968293900000094
match Score representing alternate point j (Matching Score); 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:
Figure BDA0002968293900000095
wherein N represents the number of the alternative points,
Figure BDA0002968293900000096
a high signal source k has a higher discrimination at the alternative point i and will
Figure BDA0002968293900000097
Normalization processing is carried out to obtain weight fraction
Figure BDA0002968293900000098
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:
Figure BDA0002968293900000101
wherein the content of the first and second substances,
Figure BDA0002968293900000102
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 FSiThe 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)*xPDR+α*xRF-MAG
y＝(1-α)*yPDR+α*yRF-MAG
wherein α is:
Figure BDA0002968293900000103
wherein D (P)PDR，PPrev) Indicates the distance, D (P), of the PDR position from the last positionRF-MAG，PPrew) 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 usedRF-MAGThe 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 ***glass, 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 in a large scale, the invention provides a lightweight geomagnetic positioning algorithm integrating radio waves and an inertial sensor in an indoor environment.
For the mobile terminal, the electric quantity is a valuable resource, namely an iOS operating system closed by apple Inc. or an iOS operating system opened by Google Inc., or 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 is difficult to further popularize in the consumer electronics market, and the limitation and monitoring can be correspondingly reduced through the geomagnetic algorithm, so that the power consumption of the mobile terminal can be reduced, and the power use duration 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 (5)

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-free 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 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 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:
Figure FDA0002968293890000021
wherein the content of the first and second substances,
Figure FDA0002968293890000022
representing the signal source k, the discrimination score between candidate points i and j,
Figure FDA0002968293890000023
a matching score representing the alternative point i,
Figure FDA0002968293890000024
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:
Figure FDA0002968293890000025
wherein N represents the number of the alternative points,
Figure FDA0002968293890000026
a high signal source k has a higher discrimination at the alternative point i and will
Figure FDA0002968293890000027
Normalization processing is carried out to obtain weight fraction
Figure FDA0002968293890000028
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:
Figure FDA0002968293890000029
wherein the content of the first and second substances,
Figure FDA00029682938900000210
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 FSiThe 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)*XPDR+α*XRF-MAG
y＝(1-α)*yPDR+α*yRF-MAG
wherein α is:
Figure FDA0002968293890000031
wherein D (P)PDR，PPrev) Indicates the distance, D (P), of the PDR position from the last positionRF-MAG，PPrew) 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 usedRF-MAGThe 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.
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 indoor geomagnetic positioning method fusing radio wave and inertial sensor according to claim 1 or 2, wherein the step length calculated by the mobile terminal in the 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 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.
4. The indoor geomagnetic positioning method based on fusion of radio wave and inertial sensor, according to claim 1, wherein the pedestrian heading calculation formula PDR is:
Figure FDA0002968293890000041
Erand NrRepresenting the east and north coordinates of the user in the ENU coordinate system, SrRepresenting the step size, alpha, of the steprIndicating 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.
5. The indoor geomagnetic positioning method fusing radio wave and inertial sensor, according to claim 1, 2 or 4, 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, a personal navigation device, a wearable device, smart glasses, a smart watch, a virtual display device or a display enhancement device.
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