CN103543434B - Indoor locating system, mobile phone and localization method - Google Patents

Abstract

The invention provides an indoor positioning system, which includes a light emitting end and a light receiving end. The optical transmitting end sends optical signals; the optical receiving end includes an optical sensor, an electronic compass, and a processor. The light receiving end rotates around a point in space for at least three gestures. The light sensor receives light signal data; the electronic compass collects magnetic force data and acceleration data; the processor calculates at least three light intensity values from the light signal data, and calculates magnetic force correction parameters and at least three heading angles from the magnetic force data and acceleration data , and calculate the unit normal vector of each attitude of the light sensor; the processor uses the light intensity model to establish a system of equations according to the at least three light intensity values and the corresponding unit normal vectors, and solve the coordinate values of the light receiving end. The invention also provides an indoor positioning mobile phone and an indoor positioning method thereof. By integrating optical, magnetic and acceleration sensors, the application requirements of indoor positioning are met, and the positioning accuracy is high, the stability is good, and the cost is low.

Description

Indoor positioning system, mobile phone and positioning method

technical field

The invention relates to positioning, in particular to an indoor positioning system, a mobile phone and a positioning method.

Background technique

With the popularization of information and communication technology, people's demand for indoor positioning information is increasing day by day. Some public places, such as shopping malls, airports, exhibition halls, office buildings, warehouses, underground parking lots, etc., need to use accurate positioning information. For example, location information is needed in scenarios such as shopping guides in shopping malls, people tracing in public places, and large warehouse management. Accurate indoor positioning information can realize efficient management of available space and inventory materials; it can navigate police, firefighters, soldiers, and medical staff to complete specific indoor tasks; smart spaces and ubiquitous computing are inseparable from location services, so Indoor positioning has broad application prospects.

Research on indoor positioning technology at home and abroad is relatively rich. According to positioning principles, there are proximity detection [1], fingerprint matching [2] and polygon/angle method [3], etc. The proximity detection method takes the detected position of the signal source as the positioning position, and the accuracy is low. The fingerprint matching method can obtain better positioning accuracy by using the signal feature matching in the indoor environment, but the positioning results are easily affected by indoor multipath effects and environmental changes, and the establishment of a fingerprint database is cumbersome. The multilateral/angle method needs to accurately measure the distance/angle and other information from the positioning point to the reference point through algorithms such as TOA, TDOA, and AOA, and then use trilateration to locate the target. If the position information of the reference node is accurate and the measurement distance is accurate, the position of the target node can be accurately measured, but this conclusion is the best result in theory, and there will be errors in actual measurement, so that the result is inaccurate.

[1] L.Ni, Y.Liu, C.Yiu, and A.Patil. LANDMARC: Indoor Location Sensing Using Active RFID. In WINET, 2004.

[2]M.Youssef and A.Agrawala.The Horus WLAN Location Determination System.In MobiSys,2005.

[3] N.B.Priyantha, A.Chakraborty, and H.Balakrishnan. The CricketLocation-Support System. In MobiCom, 2000.

Contents of the invention

In view of this, we provide an indoor positioning system, an indoor positioning mobile phone, and an indoor positioning method to achieve accurate and simple indoor positioning.

The indoor positioning system in the present invention includes a light emitting end and a light receiving end. The light-emitting end is used to send light signals; the light-receiving end includes a light sensor, an electronic compass and a processor, and the light-receiving end rotates at least three attitudes around a point in space; wherein the light sensor is used to The optical signal data when collecting the at least three attitudes; the electronic compass is used to collect at least three sets of magnetic force data and acceleration data during the rotation; the processor calculates at least Three light intensity values, calculating at least three sets of unit normal vectors of the light sensor from the magnetic force data and acceleration data, using the light intensity model, and establishing a system of equations based on the at least three light intensity values and the corresponding unit normal vectors , to solve the coordinate value of the light receiving end.

Preferably, the light emitting end is connected to a power supply, including a light source, and a frequency controller connected between the light source and the power supply, the frequency controller is used to control the power switch at a preset frequency, so that the light source emits an optical signal with a stable frequency .

Preferably, the light intensity model is $\mathrm{the\; s}=f_d\left(d\right)*f_{\mathrm{\μ}}\left(\arcsin \left(\frac{d^{\′}}{d}\right)\right)*f_{\mathrm{\ω}}\left(\arccos \left(\frac{z0-z}{d}\right)\right),$ Among them, let (x ₀ , y ₀ , z ₀ ) be the coordinate value of the light emitting end, (x, y, z) be the coordinate value of the light receiving end, s is the light intensity value, and d is the distance from the light emitting end to The distance between the light receiving ends, μ is the incident angle of light entering the light sensor and ω is the exit angle of light at the light emitting end, d' is the distance from the light emitting end to the plane where the light sensor is located, f _d , f _μ , f _ω are the relationship functions between light intensity and d, μ, ω respectively.

Preferably, the calculation formula of the magnetic force correction parameter is: Among them, x, y, z are the magnetic force data, R is the constant of the geomagnetic field strength, x0, y0, z0, a, b, c are the magnetic force correction parameters to be obtained.

Preferably, the calculation process of the heading angle includes: using the magnetic force correction parameters for data recovery, the three-axis components in the Cartesian coordinate system can be obtained as M _x , M _y , M _z , and the normalized data can be obtained as M _x1 , M _y1 ,M _z1 . The normalized data of the acceleration sensor in the Cartesian coordinate system of the carrier is A _x1 , A _y1 , A _z1 ;

Calculated:

Pitch angle ρ=arc sin(-A _x1 ),

Rolling angle γ=arc sin(-A _y1 /cosρ),

Use the formula:

M _X2 =M _X1 cosρ+M _Z1 sinρ,

M _y2 =M _X1 sinγsinρ+M _y1 cosγ-M _z1 sinγcosρ,

M _z2 =-M _x1 cosγsinρ+M _y1 sinγ+M _z1 cosγcosρ, and

That is, the heading angle is obtained.

Preferably, the planes where the at least three attitude light sensors are located are linearly independent.

Preferably, the electronic compass includes a magnetic sensor and the acceleration sensor.

The indoor positioning mobile phone of the present invention is integrated with the light receiving end of the above-mentioned indoor positioning system.

The indoor positioning method of the present invention includes a light emitting end fixed indoors and a movable light receiving end, including the following steps: preset a frequency at the light emitting end, and output an optical signal of a preset frequency; The receiving end rotates at least three attitudes around a point in space, receives the optical signal of each attitude, and the processor calculates the light intensity value of each attitude; collects at least three sets of magnetic force data and acceleration data, and the processor calculates the magnetic force Calibrate the parameters, and calculate the unit normal vector of the plane where the light sensor is located for each attitude; the processor uses the light intensity model, the at least three light intensity values and the corresponding unit normal vectors to establish a system of equations to solve the coordinates of the light receiving end value.

According to the light intensity model, the present invention uses the receiving end device integrated with the light sensor, can measure the position of the receiving end more accurately in complex indoor environments, can meet the application requirements of many indoor positioning, and has high positioning accuracy and good stability , the cost is lower.

Description of drawings

FIG. 1 is a schematic diagram of a structure of a light emitting end in an embodiment of the present invention.

Fig. 2 is a structural example diagram of an optical receiving end in an embodiment of the present invention.

Fig. 3a to Fig. 3c are respectively demonstration diagrams of linear independence among three planes in the embodiment of the present invention.

Fig. 4 is an analysis diagram of a light intensity model in an embodiment of the present invention.

Fig. 5 is a flowchart of a positioning method in an embodiment of the present invention.

detailed description

An indoor positioning system includes a light emitting end 10 and a light receiving end 20 . Wherein, the optical transmitting end 10 is used for sending optical signals. The light receiving end 20 rotates at least three attitudes around a point in space, receives the optical signal data, magnetic force data and acceleration data of each attitude, and uses the light intensity model to calculate and obtain the current coordinate value of the light receiving end 20 .

Embodiment 1 Optical Transmitter

As shown in FIG. 1 , the light emitting end 10 includes a power supply (or a power interface connected to the power supply) 11 , a light source 13 and a frequency controller 12 .

The light source 13 used in the present invention is an LED lamp, which provides visible light or infrared rays. The light-emitting chips of these LED lights are relatively small, so they can be used as point light sources. The frequency controller 12 is used to connect between the light source 13 and the power source 11, and controls the power switch at a preset frequency so that the light source 13 emits an optical signal with a stable frequency.

Embodiment 2 Optical receiving end

As shown in FIG. 2 , the light receiving end 20 includes a light sensor 21 , an electronic compass 22 , a processor 23 , a memory chip 24 , and a power module 25 . Wherein, the electronic compass 22 includes a magnetic sensor and an acceleration sensor. In this embodiment, the light sensor 21 and the electronic compass 22 are integrated together to make a separate sensor sub-board, and then the sensor sub-board is connected to the single-chip microcomputer bottom board to make a dedicated positioning receiving end or the light sensor and electronic compass The compass is integrated on the mobile phone as the light receiving end 20 .

In order to obtain optical, magnetic, and acceleration data, the light receiving end 20 rotates around a point in space for at least three attitudes. The planes where the light sensor 21 is located in the at least three attitudes are linearly independent, as shown in FIG. 3 .

The light sensor 21 is used to receive the light signal data of the at least three postures, and the processor 23 uses the light intensity model to calculate the light intensity value of each posture;

The light intensity model is $\mathrm{the\; s}=f_d\left(d\right)*f_{\mathrm{\μ}}\left(\arcsin \left(\frac{d^{\′}}{d}\right)\right)*f_{\mathrm{\ω}}\left(\arccos \left(\frac{z0-z}{d}\right)\right),$

Wherein, let (x ₀ , y ₀ , z ₀ ) be the coordinate value of the light emitting end 10, (x, y, z) be the coordinate value of the light receiving end, s is the light intensity value, and d is the light emitting The distance between the end 10 and the light receiving end 20, μ is the incident angle of the light entering the sensor and ω is the exit angle of the light at the light emitting end, d' is the distance from the light emitting end to the plane where the light sensor 21 is located, f _d , f _μ , fω are the relationship functions between light intensity and d, μ, ω respectively.

The magnetic sensor in the electronic compass 22 is used to collect at least three sets of magnetic force data in the rotation, calculate and output the magnetic force correction parameter; the calculation formula of the magnetic force correction parameter is: Among them, x, y, z are magnetic force data, R is a constant of the geomagnetic field strength, x ₀ , y ₀ , z ₀ , a, b, c are the magnetic force correction parameters to be obtained.

For the acceleration sensor in the electronic compass 22, the processor calculates and outputs at least three sets of heading angles through the magnetic correction parameters and the collected acceleration values, and calculates the unit normal vector of each attitude plane of the optical sensor 21.

The calculation process of the heading angle includes:

Using the magnetic force correction parameters for data recovery, the three-axis components on the rectangular coordinate system can be M _x , M _y , M _z , and the normalized data can be M _x1 , M _y1 , M _z1 . The normalized data of the acceleration sensor in the Cartesian coordinate system of the carrier is A _x1 , A _y1 , A _z1 ; the calculation is:

Pitch angle ρ=arc sin(-A _x1 ), roll angle γ=arc sin(-A _y1 /cosρ),

Use the formula:

M _X2 =M _X1 cosρ+M _Z1 sinρ,

M _y2 =M _X1 sinγsinρ+M _y1 cosγ-M _z1 sinγcosρ,

M _z2 =-M _X1 cosγsinρ+My1sinγ+M _z1 cosγcosρ, and

That is, the heading angle is obtained.

The processor 23 calculates the light intensity value and the unit normal vector, etc., and uses the light intensity model to establish a system of equations according to the at least three light intensity values and the corresponding unit normal vectors to solve the coordinate value of the light receiving end 20 .

The calculation process of the coordinates includes:

light intensity model $\mathrm{the\; s}=f_d\left(d\right)*f_{\mathrm{\μ}}\left(\arcsin \left(\frac{d^{\′}}{d}\right)\right)*f_{\mathrm{\ω}}\left(\arccos \left(\frac{z0-z}{d}\right)\right),$ According to the obtained at least three light intensity values si and unit normal vectors (A _i , B _i , C _i ), where i=1, 2, 3,..., the equations are composed of the following equations:

S ₁ =(k/d ² )*((A ₁ (x ₁ -x)+B ₁ (y ₁ -y)+C ₁ (z ₁ -z))/d)*((z ₁ -z) /d) (1)

S ₂ =(k/d ² )*((A ₂ (x ₂ -x)+B ₂ (y ₂ -y)+C ₂ (z ₂ -z))/d)*((z ₂ -z) /d) (2)

S ₃ =(k/d ² )*((A ₃ (x ₃ -x)+B ₃ (y ₃ -y)+C ₃ (z ₃ -z))/d)*((z ₃ -z) /d) (3)

...

The coordinate values of the light receiving end 20 can be obtained by solving the equations.

Embodiment 3 indoor positioning method

As shown in Fig. 5, S511 is the setting step of the light emitting end fixed in the room, and step S521-step S525 is the step of receiving the light signal and calculating the coordinate value of the movable light receiving end.

In step S511, a frequency controller is connected between the light source and the power supply at the light emitting end, and a frequency is preset in the frequency controller to control the power switch, so that the light source emits an optical signal with a stable frequency.

The frequency of the optical signal should not be too low, it should not affect the normal work and rest of people in the indoor environment, and the frequency should not be too high, so that the receiving device can completely collect the optical signal, and its frequency should also avoid the existing interference frequency, such as the 100hz frequency of fluorescent lamps, etc. After testing, the optical signal frequency should be at least greater than 30hz.

In step S521, the light receiving end is rotated around a point in space for at least three gestures. In the at least three attitudes, the planes where the light sensors are located are linearly independent, as shown in FIG. 3 .

In step S522, a light intensity model is established, the light signal of each gesture is received, and at least three light intensity values are obtained by calculation.

According to the experimental results, it can be known that the light intensity value s received by the light sensor at the current position and the distance d between the current position and the light source, and the incident angle μ of the light entering the sensor (μ represents the angle between the light entering the light sensor and the sensor plane) It is related to the exit angle ω of the light at the light source (ω represents the angle between the light entering the light sensor and the center light perpendicular to the ground from the light source when it is emitted at the light source). As shown in Figure 4, (x ₀ , y ₀ , z ₀ ) are the coordinates of the point light source, (x, y, z) are the position coordinates of the light sensor, and the sensor plane has a certain angle with the horizontal plane. The functions f _d (d), f _μ (μ), and f _ω (ω) are used to represent the relationship between the light intensity s and d, μ, and ω, respectively. As we all know, the light intensity attenuation is inversely proportional to the square of the distance. Use k to represent the light sensor vertically facing the center light of the light source (as shown in the center line of Figure 4), and measure the light intensity value at a distance of 1 meter from the light source, then f _d (d) =k/d ² , where, $d=\sqrt{{(x_0-x)}^2+{({\mathrm{the\; y}}_0-\mathrm{the\; y})}^2+{(z_0-z)}^2}.$ The light sensor is placed on the center light at a distance of d meters from the light source, the incident angle μ=90°, and the plane of the light sensor is deflected at equal angular intervals (such as every 10°), so that the incident angle μ of the central light entering the light sensor changes from 90° when it is vertical °equal angle interval (such as 10°) is reduced to 0°, at this time the central light is parallel to the sensor plane, and the light intensity value is tested after each deflection, then the relationship between light intensity s and μ is f _μ (μ)=f _μ ( arc sin(d′/d)), where

d′=|A(x ₀ -x)+B(y ₀ -y)+C(z ₀ -z)|, represents the distance from the light source to the sensor plane, (A, B, C) is the The unit normal vector of the sense plane. Place the light sensor on the central ray at a distance of d meters from the light source, and deflect the light source at equal angular intervals (such as 10°) at the exit angle ω=0°. The direction of the central ray deflects with the light source, and the central ray exit angle ω increases from 0° to 90°, test the light intensity value after each deflection, then the relationship between light intensity s and ω, According to the experimental results and the light intensity model analysis diagram in Figure 4, the light intensity s=f _d (d)*f _μ (μ)*f _ω (ω), then

$\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; arcsin</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; d</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; arccos</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; z</span>}\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; z</span>}}{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

In step S523, the magnetic force data of each posture is received, and correction parameters are obtained by correcting the magnetic force data.

If the magnetic sensor is not disturbed by any disturbance, the magnetic sensor will rotate in all directions around a certain point in space. The collected magnetic force data is distributed on a spherical surface with (0, 0, 0) as the center of the sphere, but in fact the magnetic sensor will be disturbed by various magnetic fields of the equipment and environment, and the data collected during its rotation A tilted ellipsoid that deviates from the center of the sphere will be formed, which needs to be corrected to obtain correction parameters, so as to correct the magnetic data and restore the corrected magnetic data to the center of the sphere (0, 0, 0). on the sphere. The sensor sub-board or mobile phone is located at a certain point in space, and the sensor sub-board or mobile phone is rotated in all directions around this point at a certain speed by hand or machinery, and the magnetic force data and acceleration data are collected at the same time. The corrected expression of the disturbed magnetic data is:

Among them, x, y, z are the magnetic force data, R is the constant of the geomagnetic field strength, x0, y0, z0, a, b, c are the correction parameters to be obtained. Rewrite the above formula into matrix form: x ² =[xyz -y ² -z ² 1]

$<span\; class="notranslate">\; *</span><span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; b</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; b</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; b</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>{{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>{{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; ]</span>}^{\mathrm{<span\; class="notranslate">\; T</span>}}$

Let W _nx1 =x ² , |H| _nX6 =[xyz -y ² -z ² 1],

${\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 6</span>}\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; b</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; b</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; b</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>{{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>{{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; ]</span>}^{\mathrm{<span\; class="notranslate">\; T</span>}}$

Then W _nx1 =[H] _nx6 *X _6x1 , according to the least square method, X=[H ^T H] ^-1 H ^T *W, from which the values of x0, y0, z0, a, b, c can be obtained, namely Calibration parameters.

In step S524, at least three sets of acceleration data are collected, combined with the magnetic force correction parameters, and the unit normal vector of the plane where the light sensor is located for each attitude is calculated.

Use the correction parameters in the previous step to correct the magnetic force data, and the data can be restored to the spherical surface with (0, 0, 0) as the center of the sphere, and its components on the x, y, and z axes of the carrier Cartesian coordinate system are M _x , M _y , M _z , normalize the data to get M _x1 , M _y1 , M _z1 . The data of the acceleration sensor on the three axes of the carrier Cartesian coordinate system x, y, and z are A _x , A _y , A _z , and the normalized data are A _x1 , A _y1 , A _z1 .

Use the formula: ρ=arc sin(-A _x1 ), γ=arc sin(-A _y1 /cosρ), and calculate the pitch angle ρ and roll angle γ.

Use the formula: M _X2 =M _X1 cosρ+M _Z1 sinρ,

M _y2 =M _X1 sinγsinρ+m _y1 cosγ-M _z1 sinγcosρ,

M _z2 =-M _x1 cosγsinρ+M _y1 sinγ+M _z1 cosγcosρ, and

That is, the heading angle is obtained, and the unit normal vector of the light sensor plane of the current attitude can be calculated through the heading angle.

In step S525, the processor uses the light intensity model, the at least three light intensity values and the corresponding unit normal vectors to establish a system of equations to solve the coordinate values of the light receiving end.

For each attitude, after the single-chip or mobile phone collects a certain number (such as 128) of the original light intensity data, it performs Fourier transform, removes other interference frequencies in the environment in the frequency domain (such as the 100Hz frequency of ordinary fluorescent lamps), and takes out the signal The value corresponding to the frequency is processed by inverse Fourier transform to obtain the light intensity value of the corresponding light source. Transform at least three different attitudes according to step 6, and when there are at least three attitudes, the corresponding light sensor planes are linearly independent, and at least three light intensity values and their corresponding heading angles can be obtained. Combining the light intensity value, the heading angle, the coordinate value of the point light source and the light intensity model, using formula (1) to establish an equation system of at least three equations, and solving the equation system to obtain the coordinates of the light receiving end.

According to the light intensity model, the present invention uses the receiving end device integrated with the light sensor, can measure the position of the receiving end more accurately in complex indoor environments, and can meet the application requirements of many indoor positioning, and except the signal source and the receiving end In addition, there is no need to arrange other auxiliary equipment, no need to collect indoor fingerprints, high positioning accuracy, good stability, and low cost.

The present invention uses infrared rays or visible light for positioning. The positioning accuracy is higher than that of proximity detection and fingerprint matching methods. The average accuracy is about 0.4m. The positioning result is stable, and there is no need to collect indoor fingerprints.

The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications should also be considered Be the protection scope of the present invention.

Claims (8)

1. An indoor positioning system, comprising an optical transmitter and an optical receiver, characterized in that: The optical transmitting end is used to send an optical signal; The light receiving end includes a light sensor, an electronic compass and a processor, and the light receiving end rotates around a point in space for at least three gestures; wherein: The optical sensor is used for collecting the optical signal data of the at least three gestures; The electronic compass is used to collect at least three sets of magnetic force data during the rotation process, calculate and output magnetic force correction parameters, and calculate and output at least three sets of heading angles through the magnetic force correction parameters and the collected acceleration values; The processor calculates at least three light intensity values from the light signal data, calculates at least three sets of unit normal vectors of the light sensor from the magnetic force data and acceleration data, uses the light intensity model, and according to the at least three light intensity values and corresponding unit normal vectors to establish a system of equations to solve the coordinate values of the light receiving end; Wherein, the planes where the at least three attitude light sensors are located are linearly independent. 2. The indoor positioning system according to claim 1, wherein the light emitting end is connected to a power supply, includes a light source, and a frequency controller connected between the light source and the power supply, and the frequency controller is used to preset The frequency control power switch is set to make the light source emit light signals with stable frequency. 3. The indoor positioning system according to claim 2, wherein the light intensity model is $\mathrm{the\; s}=f_d\left(d\right)*f_{\mathrm{\&mu;}}\left(arc\mathrm{the\; s}i\mathrm{no}\right(\frac{d^{\&prime;}}{d}\left)\right)*f_{\mathrm{\&omega;}}\left(arcco\mathrm{the\; s}\right(\frac{z_0-z}{d}\left)\right),$ Wherein, let (x ₀ , y ₀ , z ₀ ) be the coordinate value of the light emitting end, (x, y, z) be the coordinate value of the light receiving end, s is the light intensity value, and d is the distance from the light emitting end to The distance between the light receiving ends, μ is the incident angle of light entering the light sensor and ω is the exit angle of light at the light emitting end, d' is the distance from the light emitting end to the plane where the light sensor is located, f _d , f _μ , f _ω are the relationship functions between light intensity and d, μ, ω respectively. 4. The indoor positioning system according to claim 2, wherein the calculation formula of the magnetic force correction parameter is: Among them, x, y, z are magnetic force data, R is a constant of the geomagnetic field strength, x ₀ , y ₀ , z ₀ , a, b, c are the magnetic force correction parameters to be obtained. 5. The indoor positioning system according to claim 4, wherein the calculation process of the heading angle comprises: Using the magnetic correction parameters for data recovery, the three-axis components on the Cartesian coordinate system can be M _x , M _y , M _z , and the normalized data can be M _x1 , M _y1 , M _z1 . The normalized data of the system are A _x1 , A _y1 , A _z1 ; Calculated: pitch angle ρ=arc sin(-A _x1 ), Rolling angle γ＝arc sin(-A _y1 /cosρ), Use the formula: M _X2 =M _X1 cosρ+M _Z1 sinρ, M _y2 ＝M _X1 sinγsinρ+M _y1 cosγ-M _z1 sinγcosρ, M _z2 ＝-M _X1 cosγsinρ+M _y1 sinγ+M _z1 cosγcosρ, and That is, the heading angle is obtained. 6. The indoor positioning system according to claim 1, wherein the electronic compass comprises a magnetic sensor and the acceleration sensor. 7. An indoor positioning mobile phone, characterized in that it is integrated with the light receiving end of the indoor positioning system according to any one of claims 1 to 6. 8. An indoor positioning method, comprising a light emitting end fixed indoors and a movable light receiving end, characterized in that, comprising the following steps: Presetting the frequency at the optical transmitting end, outputting an optical signal of the preset frequency; Rotate the light receiving end around a point in space for at least three attitudes, receive the optical signal at each attitude, and calculate the light intensity value at each attitude by the processor; Collect at least three sets of magnetic force data and acceleration data, the processor calculates the magnetic force correction parameters, and calculates the unit normal vector of the plane where the light sensor is located for each attitude; The processor uses the light intensity model, the at least three light intensity values and the corresponding unit normal vectors to establish a system of equations to solve the coordinate values of the light receiving end; Wherein, the planes where the at least three attitude light sensors are located are linearly independent.