WO2018233692A1 - Procédé de positionnement, support d'informations et système de positionnement - Google Patents

Procédé de positionnement, support d'informations et système de positionnement Download PDF

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Publication number
WO2018233692A1
WO2018233692A1 PCT/CN2018/092439 CN2018092439W WO2018233692A1 WO 2018233692 A1 WO2018233692 A1 WO 2018233692A1 CN 2018092439 W CN2018092439 W CN 2018092439W WO 2018233692 A1 WO2018233692 A1 WO 2018233692A1
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Prior art keywords
positioning
led
wifi
terminal
grid point
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PCT/CN2018/092439
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English (en)
Chinese (zh)
Inventor
杨坤
贾倩
李秋婷
吴传喜
余万涛
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中兴通讯股份有限公司
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Publication of WO2018233692A1 publication Critical patent/WO2018233692A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0252—Radio frequency fingerprinting
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
    • G01S11/12—Systems for determining distance or velocity not using reflection or reradiation using electromagnetic waves other than radio waves
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S2205/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S2205/01—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations specially adapted for specific applications
    • G01S2205/02—Indoor

Definitions

  • the present disclosure relates to the field of positioning, and in particular to a positioning method, a storage medium, and a positioning system.
  • indoor location services which are increasingly urgent for demand, have developed rapidly in indoor positioning technology. They are research hotspots in the era of mobile internet, and gradually play a role in all walks of life, which has a certain impact on people's daily life.
  • the indoor environment is limited by the positioning time, positioning accuracy, and indoor complex environment. The positioning effect is still not ideal in practical applications.
  • the common methods of indoor positioning mainly include the following:
  • the infrared ray is positioned by the infrared ray emitted by the infrared ray transmitter received by the optical sensor in the room.
  • the Active Badge System an infrared outdoor positioning system developed by AT&T Labs at the University of Cambridge, is known as the first generation of indoor positioning systems.
  • Ambiplex proposed in 2011 that the IR.Loc system is positioned by measuring thermal radiation, with a positioning accuracy of 20 to 30 cm in the range of 10 m.
  • the implementation of the infrared indoor positioning system consists of two parts: an infrared emitter and an infrared receiver.
  • the infrared emitter is a fixed node of the network
  • the infrared receiver is mounted on the target to be positioned as a mobile terminal.
  • the advantages of infrared indoor positioning are high positioning accuracy, responsiveness, and low cost for a single device.
  • the Bluetooth indoor positioning is located by the fingerprint positioning algorithm according to the measurement terminal device signal strength.
  • iBeacon is a protocol technology developed by Apple for Bluetooth positioning. The positioning accuracy is 2 ⁇ 3m.
  • the shopping application Shopkick is deployed in the mall.
  • iBeacon is applied in real life. China's “find deer” and “Guangfa easy go” and other APPs This mode is also used for positioning.
  • Bluetooth positioning technology has high security, low cost, low power consumption and small size. At present, most mobile terminals have their own Bluetooth modules, which is easy to popularize and deploy.
  • Radio Frequency Identification (RFID) positioning technology uses radio frequency signals for non-contact two-way communication to exchange data for identification and positioning purposes.
  • RFID positioning systems include the Cricket system developed by the MIT Oxygen project, the SpotON system of the University of Washington, and the RADAR system of Microsoft Corporation.
  • RFID technology has a large transmission range and low cost.
  • the infrared positioning method is limited to the line of sight positioning, and the infrared information is greatly attenuated in the air, and can only be used for short
  • the distance positioning and positioning accuracy are easily affected by other light sources
  • the Bluetooth indoor positioning method is susceptible to interference from external noise signals, the signal stability is poor, and the communication range is small
  • the RF indoor positioning method has a short working distance, and the longest is only tens of meters.
  • the radio frequency signal does not have the communication capability, and only the radio frequency identification technology cannot be used for indoor positioning, and must be combined with other auxiliary technologies to complete.
  • the technical problem to be solved by the present disclosure is to provide a positioning method, a storage medium, and a positioning system, which are used to solve the problem that the positioning accuracy in the prior art is not high.
  • the present disclosure provides a positioning method, including:
  • the final positioning position of the positioning terminal is obtained according to the weight position corresponding to the WiFi positioning position, the LED positioning position, and the two positioning positions.
  • the positioning area is divided into a plurality of grid points in advance, the WiFi weight value corresponding to the WiFi positioning on each grid point is obtained, and the LED corresponding to the LED positioning on each grid point is obtained.
  • the WiFi weight value corresponding to the grid point closest to the WiFi positioning position is used as the weight value corresponding to the WiFi positioning position; and the grid point closest to the LED positioning position is corresponding to The LED weight value is used as the weight value corresponding to the LED positioning position.
  • the WiFi weight value corresponding to the WiFi location on each grid point is obtained, including:
  • a WiFi classifier that uses the RSSI data for location prediction positioning is obtained
  • the method further includes:
  • the WiFi fingerprint library is divided into a training set and a verification set according to a set ratio
  • the support vector machine algorithm SVM nonlinear classifier is selected and trained in the Python language to obtain the WiFi classifier.
  • the positioning terminal is predicted and located according to the WiFi classifier, and the WiFi positioning position of the positioning terminal is obtained.
  • the LED weight values corresponding to the LED positioning on each grid point are obtained, including:
  • the LED fingerprint library is obtained
  • an LED classifier for position prediction positioning using LED positioning data is obtained
  • the method further includes:
  • the LED fingerprint library is divided into a training set and a verification set according to a set ratio
  • the support vector machine algorithm SVM nonlinear classifier is selected and trained in the Python language to obtain the LED classifier.
  • the LED positioning data includes: a heading angle, a pitch angle, and a roll angle of the device that collects the LED positioning data, and position coordinates of one or more LED lights captured by the device on the image. .
  • the positioning terminal is predicted and positioned to obtain an LED positioning position of the positioning terminal.
  • the final positioning position of the positioning terminal is obtained by formula (1):
  • the present disclosure also provides a method for acquiring an LED positioning signal, including
  • the heading angle, the pitch angle and the roll angle of the positioning terminal, and the coordinates of the identified LED lamp in the image form a positioning signal at the position and position of the positioning terminal.
  • image processing is performed to obtain a blinking frequency of each LED light in the image, including:
  • the spacing of the black and white stripes is counted by the radon transform, and the blinking frequency of the LED corresponding to the contour of the graphic is calculated according to the spacing.
  • the present disclosure also provides a storage medium storing a positioning program for performing positioning, the program comprising:
  • the final positioning position of the positioning terminal is obtained according to the weight position corresponding to the WiFi positioning position, the LED positioning position, and the two positioning positions.
  • the positioning area is divided into a plurality of grid points in advance, the WiFi weight value corresponding to the WiFi positioning on each grid point is obtained, and the LED corresponding to the LED positioning on each grid point is obtained.
  • the WiFi weight value corresponding to the grid point closest to the WiFi positioning position is used as the weight value corresponding to the WiFi positioning position; and the grid point closest to the LED positioning position is corresponding to The LED weight value is used as the weight value corresponding to the LED positioning position.
  • the WiFi weight value corresponding to the WiFi location on each grid point is obtained, including:
  • a WiFi classifier that uses the RSSI data for location prediction positioning is obtained
  • the program includes:
  • the WiFi fingerprint library is divided into a training set and a verification set according to a set ratio
  • the support vector machine algorithm SVM nonlinear classifier is selected and trained in the Python language to obtain the WiFi classifier.
  • the positioning terminal is predicted and located according to the WiFi classifier, and the WiFi positioning position of the positioning terminal is obtained.
  • the LED weight values corresponding to the LED positioning on each grid point are obtained, including:
  • the LED fingerprint library is obtained
  • an LED classifier for position prediction positioning using LED positioning data is obtained
  • the program further comprises:
  • the LED fingerprint library is divided into a training set and a verification set according to a set ratio
  • the support vector machine algorithm SVM nonlinear classifier is selected and trained in the Python language to obtain the LED classifier.
  • the LED positioning data includes: a heading angle, a pitch angle, and a roll angle of the device that collects the LED positioning data, and position coordinates of one or more LED lights captured by the device on the image. .
  • the positioning terminal is predicted and positioned to obtain an LED positioning position of the positioning terminal.
  • the final positioning position of the positioning terminal is obtained by formula (1):
  • the present disclosure also provides a storage medium storing a program for acquiring an LED positioning signal, the program comprising:
  • the heading angle, the pitch angle and the roll angle of the positioning terminal, and the coordinates of the identified LED lamp in the image form a positioning signal at the position and position of the positioning terminal.
  • image processing is performed to obtain a blinking frequency of each LED light in the image, including:
  • the spacing of the black and white stripes is counted by the radon transform, and the blinking frequency of the LED corresponding to the contour of the graphic is calculated according to the spacing.
  • the present disclosure also provides a positioning system including a plurality of wireless routers disposed in a positioning area, a plurality of LED lights, and a positioning terminal, a monitoring terminal, and a server:
  • the positioning terminal collects the WiFi positioning signal and the LED positioning signal of the location grid point, and sends the positioning signal to the server;
  • the server locates the positioning terminal according to the WiFi positioning signal and the LED positioning signal, and sends the positioning information to the monitoring terminal;
  • the monitoring terminal receives the positioning information and displays the location of the positioning terminal.
  • the server includes a storage medium storing a fusion locator.
  • the positioning terminal includes the storage medium stored above for an image processing program.
  • the LEDs are modulated to a square wave signal, and the modulation frequencies of the individual LED lamps are different.
  • the present disclosure proposes a fusion positioning method and system based on WiFi and visible light, which combines the advantages of the two positioning methods, and realizes the complementary advantages of different positioning methods, thereby effectively combining WiFi positioning and visible light positioning, thereby improving positioning accuracy and stability.
  • FIG. 3 is a schematic structural view of a positioning system in an embodiment of the present disclosure.
  • the embodiment of the present disclosure proposes a dynamic fingerprint fusion positioning method based on WiFi and visible light.
  • a classifier for performing WiFi positioning and LED positioning is separately constructed, and then a system for performing fusion positioning is constructed. This process is referred to as an "offline phase" in this embodiment. Using the built system to perform the process of fusion positioning, this process is called the “online phase”.
  • the embodiment includes two major parts, and the first part is a fusion positioning system that separately constructs a WiFi classifier and an LED classifier, and fuses the two positioning methods together.
  • the second part is to use the built-in WiFi classifier and LED classifier, as well as the fusion positioning system for specific positioning.
  • the room Positioning area
  • the grid point size is 1m ⁇ 1m.
  • each newly acquired RSSI data is input into the WiFi classifier, and a predicted positioning result of the RSSI data collection location is obtained, and the positioning result can locate the target location into a specific grid point.
  • a square wave signal (a square wave higher than 100 Hz, the human eye does not notice the flicker, and does not cause visual discomfort, but only The brightness will be appropriately reduced, and the square wave signal is easy to handle, and the anti-interference ability is strong), usually modulated to 200 Hz-4000 Hz, and each LED lamp is modulated to a different frequency.
  • the circular panel light is selected in this scheme. If it is a normal lamp, the light-emitting area can be enlarged by adding a lamp cover.
  • the positioning terminal sets the camera exposure time (shutter) (the normal mobile phone camera can adjust the exposure time), so that the LED lights in the captured image show light and dark stripes; the remaining objects will be presented on the image. It is a black background (due to the short exposure time, the effect of non-illuminating objects such as ceilings and furniture forming a "blackout" in the image), which is more advantageous for image processing. As shown in Figure 1.
  • the structure classifier needs to extract features first.
  • the feature In the "WiFi positioning link", the feature is directly available, that is, the value of RSSI.
  • the LED positioning the amount of image data captured is very large, and the really useful information is very small, so it is necessary to pre-process the image, extract the feature, and the fingerprint library stores the feature instead of the entire image.
  • the frequency of each LED lamp is different, and the performance in the captured image is that the stripe pitch (width corresponds to the modulated frequency) is different, as shown in FIG. 2, therefore, by image processing, the corresponding image of the lamp can be identified.
  • the frequency is equal to the identification of the LED light.
  • the position coordinates of each LED lamp are fixed, so the imaging position of different LED lights in the image can be a feature of positioning.
  • binarization processing is to set a global threshold T, and use T to image the data. Divided into two parts: a pixel group larger than T and a pixel group smaller than T. The pixel value of the pixel group larger than T is set to white or black, and the pixel value of the pixel group smaller than T is set to black or white.
  • each LED light Separates each LED light, extract the contour information of each LED light, and find the center position of each LED (the pixel position in the image). Within each contour, the radon transform can be used to calculate the pitch of the black and white stripes, thereby calculating the blinking frequency of the LED and performing LED lamp recognition based on the blinking frequency.
  • the range that can be captured is limited, and it is difficult to capture multiple LEDs at the same time, so the orientation cannot be located. If the direction cannot be determined, the positioning accuracy will be greatly reduced.
  • the angle at which the phone takes a photo also affects the imaging position of the LED in the image.
  • Using the three-dimensional angle as another feature of LED positioning can improve the accuracy of positioning.
  • smartphones Android, IOS
  • the API for 3D tilt is open, so the 3D tilt feature is easy to obtain.
  • the first three features represent the three-dimensional tilt angle
  • a fingerprint library requires collecting data (LED positioning data) in each grid point, extracting the information in the image according to the feature format shown above, and obtaining the three-dimensional tilt angle, storing it, and each grid point. After the collection is completed, the LED fingerprint library can be constructed. Unlike WiFi positioning, only data is collected once at each location because the data obtained by the camera is relatively stable.
  • the method is the same as WiFi positioning.
  • the LED positioning also selects the SVM classifier, but the feature dimension is different (WiFi positioning 4D features, LED positioning 11-dimensional features).
  • regression regression
  • the mobile phone automatically takes a photo, recognizes the LED according to the image processing method described above, and then acquires the three-dimensional tilt angle to jointly form the 11-dimensional feature. It is possible to input the LED classifier constructed in (3) and output a predicted positioning result based on the LED positioning.
  • LED positioning is directly dependent on the position of the LED in the image, and the LED is sometimes invisible, that is, the mobile phone camera can not capture the LED, and therefore can not get the positioning result, then it needs to rely more on WiFi positioning.
  • ⁇ (x r (i)) represents the squared error of the predicted value coordinate x r (i) of the i-th acquired sample at the lattice point r and the true coordinate x r .
  • the predicted value coordinate x r (i) is predicted by the classifier obtained above, and the real coordinate x r is obtained by actual measurement.
  • the grid coordinates predicted by the system are (1, 2), and the actual grid coordinates are (1, 1), then Therefore, if the index is small, that is, the closer the predicted position and the real position are, the better the positioning accuracy of the system is represented.
  • Equation (2) is the weight calculation formula, and argmin is the w rj value obtained by the minimum of the above formula.
  • N is the number of samples collected at grid point r
  • r represents the grid location
  • j represents the fingerprint library
  • the weight coefficient of the fingerprint library j at the grid point r can be calculated. Specifically, in the offline stage, assuming that we have collected the WiFi location fingerprint database and the LED location fingerprint database, we now take 80% of the data in the fingerprint library for classifier training, and the remaining 20% of the data is used to calculate the weight value. .
  • 2 , 0 ⁇ w ⁇ 1 to be the smallest. In this example, w 0.5, that is, when we take this value, we can make ⁇ (w*x1) the smallest, which is 0.
  • w 11 refers to the optimization weight of the WiFI positioning of the grid 1
  • w 12 refers to the optimized weight of the LED positioning of the grid 1 . That is to say, each grid point can calculate the optimization weight of the corresponding WiFi positioning and the optimization weight of the LED positioning by the above method.
  • the positioning terminal collects data of its location
  • f j (x) refers to the most similar grid r coordinate obtained by the data collected on the line matching the fingerprint library j, and then multiplied by the weight w rj of the corresponding fingerprint corresponding to the corresponding position obtained under the line to obtain the final positioning position p.
  • the result of the positioning prediction by the WiFi classifier is (1, 2)
  • the result of the positioning prediction by the LED classifier is (2, 1)
  • the grid point in the fingerprint database that is most similar to the prediction result is found.
  • the solution in this embodiment dynamically adjusts the positioning result according to the weight, thereby improving the performance of the entire system; combining the advantages of the two positioning methods to achieve complementary advantages of different positioning methods, thereby enabling WiFi
  • the combination of positioning and visible light positioning improves the accuracy and stability of positioning.
  • an embodiment of the present disclosure also relates to a positioning system including a wireless local area network and visible light communication.
  • the positioning system comprises a positioning end, a server end and a monitoring end, wherein the positioning end is carried by the monitored target, which is used for locating the location of the target and transmitting the location information to the server end; the server side calculates the positioning result, realizes the positioning fusion, and The positioning result is sent to the monitoring end; the monitoring end is carried by the monitor, and it can view the position information of the monitored target in real time.
  • the communication system includes two parts: a wireless local area network and a visible light communication.
  • the wireless local area network includes a plurality of APs (Access Points), which transmit a WiFi signal as a transmitting end, and the mobile phone serves as a receiving end to perform wireless positioning by using signal strength.
  • APs Access Points
  • the visible light communication system includes a plurality of LED lights of different frequencies, and the mobile phone recognizes different LED lights through visible light communication for positioning.
  • the positioning system of this embodiment can also be used for multi-target positioning and monitoring, that is, one monitoring terminal mobile phone can monitor a plurality of different positioning terminals.
  • FIG. 4 The process of positioning using the system is as shown in FIG. 4, including:
  • Step 401 the positioning terminal firstly realizes the collection of the WiFi RSSI and the LED visible light signal, and the LED visible light signal is the image captured by the camera. After the image processing, the frequency and position information contained in the LED image are extracted to form the LED positioning information; The positioning terminal then sends the WiFi positioning information and the LED positioning information to the server.
  • Step 402 The server calculates a final positioning result, and performs predictive positioning on the positioning terminal according to the wireless local area network WiFi positioning signal sent by the positioning terminal, to obtain a WiFi positioning position of the positioning terminal.
  • the final positioning position of the positioning terminal is obtained according to the weight position corresponding to the WiFi positioning position, the LED positioning position, and the two positioning positions.
  • Step 403 The server sends the positioning result to the monitoring terminal, and displays the location of the positioning terminal to implement positioning.
  • the embodiment also relates to a storage medium disposed on the server side, and the storage medium may be a server, or a storage, calling, and processing device of a data installed on the server, used to establish a WiFi fingerprint database, and construct a WiFi classification. And perform WiFi positioning, establish LED fingerprint library, LED classifier and LED positioning, and use to establish weight matrix, and integrate WiFi positioning and LED positioning to determine the final positioning result.
  • the storage medium stores a positioning program for performing positioning, the program comprising:
  • the final positioning position of the positioning terminal is obtained according to the weight position corresponding to the WiFi positioning position, the LED positioning position, and the two positioning positions.
  • the positioning area is divided into a plurality of grid points in advance, the WiFi weight values corresponding to the WiFi positioning on each grid point are obtained, and the LED weight values corresponding to the LED positioning on each grid point are obtained;
  • the WiFi weight value corresponding to the grid point closest to the WiFi positioning position is used as the weight value corresponding to the WiFi positioning position; and the grid point closest to the LED positioning position is corresponding to The LED weight value is used as the weight value corresponding to the LED positioning position.
  • the WiFi weight value corresponding to the WiFi location on each grid point is obtained, including:
  • a WiFi classifier that uses the RSSI data for location prediction positioning is obtained
  • the method further includes:
  • the WiFi fingerprint library is divided into a training set and a verification set according to a set ratio
  • the support vector machine algorithm SVM nonlinear classifier is selected and trained in the Python language to obtain the WiFi classifier.
  • the positioning terminal is predicted and located, and the WiFi positioning position of the positioning terminal is obtained.
  • obtaining LED weight values corresponding to LED positioning on each grid point including:
  • the LED fingerprint library is obtained
  • an LED classifier for position prediction positioning using LED positioning data is obtained
  • the method further includes:
  • the LED fingerprint library is divided into a training set and a verification set according to a set ratio
  • the support vector machine algorithm SVM nonlinear classifier is selected and trained in the Python language to obtain the LED classifier.
  • the LED positioning data includes: a heading angle, a pitch angle, and a roll angle of the device that collects the LED positioning data, and position coordinates of the one or more LED lights captured by the device on the image.
  • the predictive positioning of the positioning terminal is performed according to the LED classifier, and the LED positioning position of the positioning terminal is obtained.
  • the embodiment of the present disclosure further relates to a storage medium, which is located at the positioning terminal side, and may be a positioning terminal, or a hardware device for data storage and processing of the positioning terminal.
  • the storage medium stores a program for acquiring an LED positioning signal, the program comprising:
  • the heading angle, the pitch angle and the roll angle of the positioning terminal, and the coordinates of the identified LED lamp in the image form a positioning signal at the position and position of the positioning terminal.
  • image processing is performed to obtain the flicker frequency of each LED lamp in the image, including:
  • the spacing of the black and white stripes is counted by the radon transform, and the blinking frequency of the LED corresponding to the contour of the graphic is calculated according to the spacing.
  • the present disclosure proposes a fusion positioning method and system based on WiFi and visible light, which combines the advantages of the two positioning methods, and realizes the complementary advantages of different positioning methods, thereby effectively combining WiFi positioning and visible light positioning, thereby improving positioning accuracy and stability.
  • the advantages of the two positioning methods based on WiFi and visible light are integrated, and the advantages of different positioning methods are complemented, thereby effectively combining the WiFi positioning and the visible light positioning, thereby improving the positioning accuracy and stability.

Abstract

L'invention concerne un procédé de positionnement, un support d'informations et un système de positionnement. Le procédé de positionnement comprend : la réalisation d'un positionnement prédictif d'un terminal à positionner selon un signal de positionnement WiFi de réseau local sans fil, envoyé par le terminal à positionner, de façon à obtenir une position à base de WiFi du terminal à positionner ; effectuer un positionnement prédictif du terminal à positionner selon un signal de positionnement de diode électroluminescente (DEL) envoyé par le terminal à positionner, de façon à obtenir une position à base de DEL du terminal à positionner ; et obtenir la position finale du terminal à positionner en fonction de la position à base de WiFi, de la position à base de DEL et des poids des deux positions. Le procédé de positionnement combine les avantages des deux procédés de positionnement pour obtenir les avantages complémentaires de différents procédés de positionnement, et par conséquent, le positionnement WiFi et le positionnement à la lumière visible sont combinés efficacement, ce qui permet d'améliorer la précision et la stabilité de positionnement.
PCT/CN2018/092439 2017-06-22 2018-06-22 Procédé de positionnement, support d'informations et système de positionnement WO2018233692A1 (fr)

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CN113840228B (zh) * 2021-08-25 2024-04-02 北京航空航天大学杭州创新研究院 一种基于定位匹配的行人室内定位方法
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