WO2015084154A1 - Système et procédé de localisation de dispositif mobile - Google Patents

Système et procédé de localisation de dispositif mobile Download PDF

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Publication number
WO2015084154A1
WO2015084154A1 PCT/MY2014/000195 MY2014000195W WO2015084154A1 WO 2015084154 A1 WO2015084154 A1 WO 2015084154A1 MY 2014000195 W MY2014000195 W MY 2014000195W WO 2015084154 A1 WO2015084154 A1 WO 2015084154A1
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WO
WIPO (PCT)
Prior art keywords
location
mobile device
coordinates
rxls
location server
Prior art date
Application number
PCT/MY2014/000195
Other languages
English (en)
Inventor
Keeratpal SINGH
Fazreen Binti Mohd Apan Affiza
Original Assignee
Mimos Berhad
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mimos Berhad filed Critical Mimos Berhad
Publication of WO2015084154A1 publication Critical patent/WO2015084154A1/fr

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Classifications

    • 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
    • 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
    • G01S5/02521—Radio frequency fingerprinting using a radio-map
    • 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
    • G01S5/0295—Proximity-based methods, e.g. position inferred from reception of particular signals
    • 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
    • G01S2205/06—Emergency

Definitions

  • the present invention relates to a system and method for locating a mobile device. More particularly, the present invention relates to a system and method for locating a mobile device by using heterogeneous neural network.
  • a mobile device is typically provided with a location-based service application wherein the application may include features to find directions or to detect location of the mobile device.
  • the application may include features to find directions or to detect location of the mobile device.
  • US Publication No. 2013/0072215 A1 An example of such system and method is disclosed in US Publication No. 2013/0072215 A1 , whereby the location prediction device provides a location prediction of a mobile device based on an adaptively compiled visitation history.
  • the location prediction may be performed without the use of large amounts of system resources.
  • the location prediction may be used in conjunction with any mobile device application known in the art.
  • the prior art includes the use of global positioning system (GPS) to provide the location for the mobile device.
  • GPS global positioning system
  • a mobile device includes a location determination system for determining a location of mobile device and a system for sending location information of the mobile device.
  • the location determination system includes a GPS receiver.
  • the mobile device can comprise a system for displaying the location of the second mobile device and/or perform a task of a non-location specific application, such as a music player.
  • the system and device implement GPS to assist the location-based service application.
  • GPS requires a clear admission to the satellites to provide the location tracking and maps on its devices.
  • the GPS is not available if the mobile device is in a closed area such as inside a building or a tunnel.
  • the operation of such location-based service application is limited.
  • the present invention provides a system for locating a mobile device.
  • the system comprises of at least a mobile device (10), a mobile enterprise platform (20), at least one telecommunication service provider (30).
  • the mobile enterprise platform (20) is characterised in that it further includes a location server (21) for locating the mobile device (10) in different types of networks; a database (22) to store collected data including Cell Identification (ID), receive signal levels (RXLs) and the location coordinates of the mobile device (10); a Phone Listener Application (23) to respond to request from the location server (21) and collect RXL of the mobile device (10); a Collector Application to collect RXLs and configuration setting sent by the location server (21); and a Quality of Positioning (QOP) Mapper (25) to map the quality of positioning (QOP) level with the type of network required.
  • the Phone Listener Application (23) includes a location engine application to provide estimation without involving the server of the location server (21).
  • the present invention also provides a method for locating a mobile device (10).
  • the method is characterised by the steps of collecting and processing data including Cell Identification (ID), signal levels (RXLs) and location coordinates of the mobile device (10); performing a neural network training pattern and optimization; performing a location request with a Quality of Positioning (QOP) Mapper (25); performing a location request with the telecommunication service provider (30); searching for location server (21); operating the mobile device (10) with the Phone
  • ID Cell Identification
  • RXLs signal levels
  • QOP Quality of Positioning
  • (10) includes sending a command to the Collector Application (24) for collecting RXLs and configuration setting by the location server (21); changing network settings by turning on the GPS; recording Cell ID and RXLs in a Training Table by the Collector Application (24); sending the recorded Cell ID and RXLs to the location server (21) within pre-configured time interval; and turning off the GPS by the Collector Application (24) when a maximum sample size (N) set by the location server (21) is achieved.
  • the step of processing a neural network training pattern and optimization includes arranging the collected data based on Location Area Code (LAC); arranging the RXLs based on the Cell IDs, network services, GPS coordinates, time and date; training neural network algorithm to derive a pattern for a particular location area based on a predefined percentage of samples of the collected data; testing the neural network algorithm using a predefined percentage of samples of the collected data; determining a smooth parameter ( ⁇ ) based on a General Regression Neural Network (GRNN); plotting a Cumulative Distribution Function (CDF) graph by using the collected data; measuring the distance between two points in the CDF graph to find the difference of the location coordinates; comparing the smooth parameter ( ⁇ ) with a benchmark; calculating a new correction factor (p) if the smooth parameter ( ⁇ ) is within the benchmark or the difference (Diff **) between the first iteration value and the second iteration value is too small; and storing the value of p in the database (22).
  • LAC Location Area Code
  • the step of performing a location request with the QOP Mapper (25) includes mapping a Quality of Positioning (QOP) level with the type of network services required by the QOP Mapper (25); sending the configuration setting to change the network service based on the QOP level required and the location request to the Phone Listener Application (23) by the location server (21); measuring the Cell ID and RXLs based on the QOP level required by the Phone Listener Application (23); translating the Wi-Fi coordinates to latitude and longitude coordinates in a map by the location server (21).
  • QOP Quality of Positioning
  • the step of measuring the Cell ID and RXLs when the QOP level is low suitably includes obtaining the Cell ID, RXLs and Wi-Fi information; and translating the Cell ID coordinates to a location coordinates by the location server (21), wherein if the mobile device (10) is in indoor area, the Wi-Fi coordinates is used as the proximity location.
  • the step of measuring the Cell ID and RXLs when the QOP level is medium suitably includes obtaining the RXLs of that particular network service and Wi-Fi network service; sending the RXLs of that particular network service and Wi-Fi network service to the location server (21); matching the RXLs of that particular network service and Wi-Fi network service with the stored RXLs using a Statistical Method and Free Space Propagation Loss Formula, wherein the RXLs are converted into distance; and triangulating at least three base stations coordinates to estimate the location coordinates.
  • the step of measuring the Cell ID and RXLs when the QOP level is high suitably includes obtaining Cell IDs and RXLs of the networks setting by changing the network services; sending the Cell IDs and RXLs of the networks setting to the location server (21); loading the value of p, and GPS coordinates of the Cell IDs that have been processed by the neural based on the collected data to estimate the current location of the mobile device (10); and estimating the current location by comparing the new Cell IDs or RXLs with the trained Cell IDs or RXLs.
  • the step of performing a location request with the telecommunication service provider (30) includes sending the enterprise phone number of the mobile device (10) to the telecommunication service provider (30) by the location server (21); searching for the mobile device (10) by using a Mobile Location Centre by the telecommunication service provider (30); sending the coordinates of the mobile device (10) to the location server (21) if the SIM card is installed in the mobile device (10); sending the last active location of the mobile device (10) by the telecommunication service provider (30); and translating the location coordinates to latitude and longitude coordinates in a map by the location server (21).
  • the step of operating the mobile device (10) with the Phone Listener Application (23) includes the steps of monitoring battery and connectivity status of the mobile device (10) by the Phone Listener Application (23); comparing the stored Cell IDs with the Wi-Fi IDs within the enterprise or comparing the stored Cell IDs with frequent travelled areas if there is no internet connection; locking the mobile device (10) within a pre-configured time interval if there is internet connection, Cell ID and Wi-Fi IDs; estimating the coordinates from the heterogeneous networks of RXLs by using any QOP level by the location server (21); and sending the coordinates of the mobile device (10) to the administrator if the Phone Listener Application (23) in the mobile device (10) is not able to do the server search.
  • the step of searching the location server (21) includes sending a message or performing a voice call by the location server (21) if the location server (21) detects no active communication from the last location of the mobile device (10) within a preconfigured time interval; intercepting the message or voice call from the enterprise number and reading request command with high QOP level by the Phone Listener Application (23); obtaining the Cell IDs and RXLs of the networks settings by changing networks; sending the enterprise message number to the location server (21), wherein the enterprise message number is a pre-stored phone number connected to the enterprise location server; reading the messages by the location server (21), wherein the messages are extracted to proceed with location search using the General Regression Neural Network for the current Location Area Code (LAC); determining the proximity coordinates of the mobile device (10) by using the Cell ID coordinates if there is no trained data for the LAC; and translating the location coordinates to latitude and longitude coordinates in a map by the location server (21).
  • LAC General Regression Neural Network for the current Location Area Code
  • the step of performing a server search call if there is no internet connection and the coordinates of the last location of the mobile device (10) is not available in the database (22) includes performing a voice call by the location server (21) if the location server (21) detects no active communication from the last location of the mobile device (10) within a pre-configured time interval; playing a recording to request the returning of the mobile device (10) to the address sent by the location server (21) via message if the voice call is answered; intercepting the voice call from the enterprise number by the Phone Listener Application (23); identifying the location of the mobile device (10) based on International Mobile Station Equipment Identity (IMEI) of the mobile device (10) by the telecommunication service provider (30) if the phone call or message of the mobile device (10) provides no response to the location server (21); obtaining the Cell IDs and RXLs of the network settings by changing networks if the GPS cannot be activated to provide location coordinates; sending the enterprise message number to the location server (21); reading the messages by the location server (21) if the message
  • FIG. 1 illustrates a system for locating a mobile device (10) according to an embodiment of the present invention.
  • FIGS. 2(a-h) illustrate flowcharts of a method for locating a mobile device (10) according to an embodiment of the present invention. DESCRIPTION OF THE PREFFERED EMBODIMENT
  • FIG. 1 illustrates a system for locating a mobile device (10) according to an embodiment of the present invention.
  • the system comprises of at least one mobile device (10), a mobile enterprise platform (20) and at least one telecommunication service provider (30).
  • the mobile enterprise platform (20) further includes a location server (21), a database (22), a Phone Listener Application (23), a Collector Application (24) and a Quality of Positioning (QOP) Mapper (25).
  • the location server (21) is used for locating the mobile device (10) in different types of network services such as Global System for Mobile Communications (GSM), Global Positioning System (GPS), Third Generation telecommunication network (3G), High-Speed Downlink Packet Access (HSDPA) and Wi-Fi.
  • the location server (21) communicates with the mobile device ( 0) via mobile enterprise platform (20).
  • the database (22) stores relevant collected data such as Cell Identification (ID), receive signal levels (RXLs) and the location coordinates of the mobile device (10).
  • the Phone Listener Application (23) is a standby application which responds to request from the location server (21) and collects RXL of the mobile device (10).
  • the Phone Listener Application (23) functions with a light location engine neural network application.
  • Light location engine does not involve comprehensive computations which the location server (21) essentially processes for neural network calculations.
  • the light location engine neural network application is defined as Neural Network Lite that operates when the mobile device (10) provides estimation with basic features of neural network without involving the location server (21).
  • Such basic features of the neural network include a predicting method for heavy calculation on the server and a light calculation by using a Structured Query Language for mobile device (10).
  • Heavy calculation involves the step of comparing the current measured sample with the training samples that were optimized, whereas light calculation involves the step of comparing measured samples of cell ID and RXL with the previous data stored in the mobile device (10).
  • the Collector Application (24) is used to collect the RXLs and receive configuration setting sent by the location server (21).
  • the QOP Mapper (25) is used to map the quality of positioning (QOP) level with the type of network service required such as GSM, GPS, 3G, HSDPA and Wi-Fi.
  • QOP quality of positioning
  • FIG. 2(a) shows a flowchart of a method for locating a mobile device (10) according to an embodiment of the present invention.
  • relevant data such as Cell ID and RXLs are collected wherein the collected data is then processed for performing predictions and comparisons of the system performance.
  • the collected data is used in General Regression Neural Network algorithm.
  • a neural network training pattern and optimization is performed as in step 200.
  • a location request is performed with the Quality of Positioning (QOP) Mapper (25).
  • the location request is performed with the telecommunication service provider (30).
  • the mobile device (10) with the Phone Listener Application (23) operates with the Neural Network Lite.
  • the method searches for location server (21) if there is no internet connection.
  • a server search call is performed if there is no internet connection and the coordinates of the last location of the mobile device (10) is not available in the database (22).
  • FIG. 2(b) shows a flowchart of a method for processing collected data such as Cell ID and RXLs as in step 100 of FIG. 2(a).
  • the location server (21) sends a command to collect RXLs and configuration setting to the Collector Application (24) as in step 101.
  • the configuration setting is the command setting in changing the type of network service from 2G to 3G, 3G to 2G, LTE to 2G, or to turn on the Wi-Fi as well as GPS in order to collect the signal strength or RXLs.
  • the Collector Application (24) changes network settings by turning on the GPS and records Cell ID and RXLs in a Training Table.
  • Table 1 and Table 2 show the examples of Training Table for collected data and validation format.
  • the table is used to collect relevant data and parameters used in the General Regression Neural Network.
  • the data is collected for several seconds in a static and moving motion so that proximity coordinates and entire area coordinates are collected.
  • the user Cell ID and the Neighbours' Cell ID are recorded as N1 till N8 for all network services from 2G (GSM) during idle mode (no voice or no activity) and different mode such as 3G and HSDPA cells.
  • the Neighbours' Cell ID are collected and recorded for the purpose of sampling.
  • the RXLs for the corresponding Cell ID in Table 1 are recorded at the columns provided for the related network services.
  • the recorded Cell ID and RXLs are stored in the database (22) by the Collector Application (24) and sent to the location server (21) within pre- configured time interval as in step 103.
  • the Collector Application (24) turns off the GPS as in step 104.
  • FIG. 2(c) shows a flowchart of a method for processing a neural network training pattern and optimization as in step 200 of FIG. 2(a).
  • the collected data is arranged based on Location Area Code (LAC) accordingly as in step 201.
  • the RXLs are arranged based on the Cell IDs, network services, GPS coordinates, time and date as in step 202.
  • step 203 80% of N samples of the collected data are used as Training Samples and 20% of N samples from the collected data are used as Estimator Samples.
  • the percentage of the sample is predefined based on the collected data.
  • Training samples are samples used for training the neural network algorithm to derive a pattern for that particular location area.
  • the estimation or prediction of the proximity location of the mobile device (10) can be obtained. For example, if a grid of area is taken as 50 meter by 50 meter, the grid has different combination of RXLs and Cell ID as compared to the neighbouring grids (which are considered as different location). Thus, it is possible to estimate which grid area or location based on the RXLs and Cell IDs of the mobile device (10) and the accuracy of estimation increases if there is more than one RXLs and Cell IDs of various network types are involved in the estimation. On the other hand, Estimator samples are used to re-evaluate and test robustness of the trained neural network algorithm.
  • a smooth parameter ( ⁇ ) is determined based on a General Regression Neural Network (GRNN).
  • GRNN is used to train the ⁇ ] with [y], wherein the GRNN is defined as GRNN ( ⁇ , T, y).
  • another set of data of [T] is collected and known as [X].
  • smooth parameter ( ⁇ ) is tuned for GRNN ( ⁇ , T, y) in order to get a new [y(x)] output coordinate(s).
  • Euclidean Distance to compare the initial collected [y(T)] to current [y(x)]'s error distance.
  • the collected data is used to plot a Cumulative Distribution Function (CDF) graph.
  • CDF Cumulative Distribution Function
  • the CDF graph describes a probability that a real-valued random variable (X) with a given probability distribution will be found at a value less than or equal to (X).
  • the distance between two points in the CDF graph is measured by using a Euclidean Distance method to find the difference of the location coordinates as in step 204.
  • the CDF of Location Estimate is computed to analyze the difference of the location coordinates.
  • the Location Estimate refers to an estimation of the location of the mobile device (10) with respect to the actual location request. A benchmark is obtained in order to compare with the smooth parameter ( ⁇ ).
  • the benchmark refers to the Federal Communications Commission USA (FCC) benchmark, wherein 67% of Estimator Samples from the CDF graph has less than 100m location accuracy and 95% of Estimator Samples from the CDF graph has less than 300m location accuracy as in step 205.
  • iteration value is obtained from the repetition calculation of the location estimate.
  • decision 206 if the smooth parameter ( ⁇ ) is not within the benchmark or the difference (Diff **) between the first iteration value and the second iteration value is not too small, the method returns to step 101 of FIG. 2(a).
  • FIG. 2(d) shows a flowchart of a method for performing a location request with the QOP Mapper (25) as in step 300 of FIG. 2(a).
  • the QOP Mapper (25) maps the QOP level with the type of network services required.
  • the location server (21) sends the configuration setting to change the network service based on the QOP level required as well as the location request to the Phone Listener Application (23) for estimating the current location of the mobile device (10) as in step 301.
  • the Phone Listener Application (23) starts the measurement process of the Cell ID and RXLs based on the QOP level required if the mobile device (10) has internet connection as in step 302.
  • decision 303 if the QOP level is low, the Cell ID, RXLs and/or Wi-Fi information are obtained as in step 304.
  • the Wi-Fi information is obtained if the mobile device is in an indoor area, wherein the Wi-Fi coordinates is used as the proximity location.
  • the location server (21) Based on the Cell ID, RXLs and/or Wi- Fi information, the location server (21) translates the Cell ID coordinates or the Wi-Fi coordinates to location coordinates as in step 305. Then, the location server (21) translates the Wi-Fi coordinates to latitude and longitude coordinates in a map such as Google Map or Geographic Information System (GIS) as in step 314. If the QOP level is medium, the RXLs of that particular network service and Wi-Fi network service are obtained and sent to the location server (21) as in step 306. Thereon, current information is matched with the stored RXLs using a Statistical Method and Free Space Propagation Loss Formula, wherein the RXLs are converted into distance as in step 307.
  • a map such as Google Map or Geographic Information System (GIS)
  • Statistical method includes the maximum possibility, average of the Cell ID obtained with the same RXLs and the Cell ID available in the database (22) to estimate the best coordinates for the mobile device (10).
  • Free Space Propagation Loss Formula stated that Free-space path loss is proportional to the square of the distance between the transmitter and receiver, and also proportional to the square of the frequency of the radio signal.
  • step 308 at least three base stations coordinates are triangulated to estimate the location coordinates.
  • the location server (21) translates the obtained coordinates to latitude and longitude coordinates in a map such as Google Map or Geographic Information System (GIS) as in step 314.
  • a map such as Google Map or Geographic Information System (GIS)
  • GPS is used to assist the location request.
  • the location server (21) translates the obtained coordinates in the map such as Google Map or Geographic Information System (GIS) as in step 314.
  • GIS Geographic Information System
  • the GPS is turned off, Cell IDs and RXLs of the networks setting are obtained by changing the network services as shown in Table 1 and then sent to the location server (21) as in step 311.
  • Based on the collected data, value of p, and GPS coordinates of the Cell IDs that have been processed by the neural network are used in the GRNN to estimate the current location of the mobile device (10) as in step 312.
  • the General Regression Neural Network is used to estimate the current location by comparing the new Cell IDs or RXLs with the trained Cell IDs or RXLs as in step 313. Then the location server (21) translates the obtained coordinates to latitude and longitude coordinates in a map such as Google Map or Geographic Information System (GIS) as in step 314.
  • a map such as Google Map or Geographic Information System (GIS) as in step 314.
  • FIG. 2(e) shows a flowchart of a method for performing a location request with the telecommunication service provider (30) as in step 400 of FIG. 2(a).
  • the location server (21) sends the enterprise phone number of the mobile device (10) that needs to be located for emergency, public safety or phone loss to the telecommunication service provider (30).
  • the telecommunication service provider (30) uses its Mobile Location Centre to search for the location of the mobile device (10) as in step 402. Thereon, the telecommunication service provider (30) sends the coordinates of the mobile device (10) to the location server (21) if the SIM card is attached to the phone or the telecommunication service provider (30) sends the last active location of the mobile device (10) as in step 403.
  • the location server (21) translates the received coordinates to latitude and longitude coordinates in a map such as Google Map or Graphic Information System (GIS) as in step 404.
  • FIG. 2(f) shows a flowchart of a method for operating the mobile device (10) with the Phone Listener Application (23) which operates with the Neural Network Lite as in step 500 of FIG. 2(a).
  • the Phone Listener Application (23) monitors battery and connectivity status of the mobile device (10).
  • the Phone Listener Application (23) is operating with the Neural Network Lite, wherein the Neural Network Lite compares the stored Cell IDs with the Wi-Fi IDs within the enterprise or compares the stored Cell IDs with frequent travelled areas if there is no internet connection as in step 501.
  • the method ends. If there is an internet connection, Cell ID and Wi-Fi IDs, the mobile device (10) locks the phone immediately or within a pre-configured time interval as in step 503. All access to the application of the mobile device (10) is denied and contents are wiped within a pre-configured time interval accordingly.
  • the location server (21) uses any QOP level to estimate the coordinates wherein the initial default of QOP level is high as in step 504. A password or pin code is required to unlock the mobile device (10). Later on, a message is sent to the administrator to inform the unattended mobile device's coordinates as in step 505 if the Phone Listener (23) application in the mobile device (10) is not able to do the server search.
  • FIG. 2(g) shows a flowchart of a method for searching the location server (21) if there is no internet connection as in step 600 of FIG. 2(a).
  • the location server (21) detects no active communication from the last location of the mobile device (10) for several hours or within a preconfigured time interval, the location server (21) sends a message or performs a voice call as in step 601.
  • the Phone Listener Application (23) intercepts the message or voice call from the enterprise number and reads request command such as wipe, lock or location search with High QOP level wherein the mobile device (10) is assumed that it never undergoes any factory reset as in step 602.
  • the Cell IDs and RXLs of the networks settings are obtained by changing networks and thereon, the enterprise message number are sent to the location server (21) as multiple message characters as in step 603 wherein the enterprise message number is a pre-stored phone number connected to the enterprise location server.
  • the location server (21) reads the messages wherein the messages are extracted to proceed with location search using the General Regression Neural Network for the current LAC as in step 604.
  • decision 605 if there is no trained data for the LAC, Cell ID coordinates are used to find the proximity coordinates of the mobile device (10) as in step 606.
  • the location server (21) translates the received coordinates in the map such as Google Map or Graphic Information System (GIS) as in step 607.
  • the map such as Google Map or Graphic Information System (GIS)
  • the collected data, value of p and stored GPS coordinates of Cell IDs and RXLs are loaded from the LAC and the Cell IDs as in step 608. Later on, the General Regression Neural Network is used to estimate the current location by comparing the new Cell IDs or RXLs with the Cell IDs or RXLs as in step 609. Then, the location server (21) translates the received coordinates to latitude and longitude coordinates in a map such as Google Map or Graphic Information System (GIS) as in step 607.
  • a map such as Google Map or Graphic Information System (GIS) as in step 607.
  • FIG. 2(h) shows a flowchart of performing a server search call if there is no internet connection and the coordinates of the last location of the mobile device (10) is not available in the database (22) as in step 700 of FIG. 2 (a).
  • the location server (21) detects no active communication from the last location of the mobile device (10) within a pre-configured time interval, the location server (21) performs a voice call as in step 701. If the voice call is answered, a recording is played to request the returning of the mobile device (10) to the address sent by the location server (21) via message.
  • the Phone Listener Application (23) intercepts the voice call from the enterprise number regardless if the mobile device (10) is answered or not wherein the mobile device (10) is assumed that it never undergoes any factory reset as in step 702.
  • the telecommunication service provider (30) identifies the location of the mobile device (10) based on International Mobile Station Equipment Identity (IMEI) of the mobile device (10). If the GPS cannot be activated to provide location coordinates, the Cell IDs and RXLs of the network settings are obtained by changing networks and the enterprise message number are sent to the location server (21) as multiple message characters as in step 704.
  • IMEI International Mobile Station Equipment Identity
  • the location server (21) reads the messages wherein the text messages are extracted to proceed with location search using the General Regression Neural Network for the current LAC as in step 706. If the message is not successfully sent, the method determines whether there is trained data in the Neural Network Lite as in decision 707. If there is no trained data in the Neural Network Lite, text parameters such as the Cell ID, LAC, IMEI and RXLs are provided to a text-to-speech component for converting the text parameters to speech as in step 708.
  • the Neural Network Lite If there is trained data in the Neural Network Lite, the trained data of the Neural Network Lite, value of p and stored GPS coordinates of trained Cell IDs and RXLs are loaded from the LAC and the Cell IDs as in step 709.
  • the Neural Network Lite is used to predict location coordinates if the GPS is not activated.
  • text-to-speech program is used to speak the text and IMEI repeatedly as in step 711.
  • the Emergency Number is called and the text to speech voice result is voice read.
  • the emergency centre personnel takes action and utilises the telecommunication service provider (30) to locate the mobile device (10) based on the IMEI when the emergency centre personnel received the voice reading as in step 712 and 713.
  • the user can utilize the location search server connected to the telecommunication service provider (30) to track the lost IMEI.
  • the location search server connected to the telecommunication service provider (30) to track the lost IMEI.

Abstract

La présente invention concerne un système et un procédé pour localiser un dispositif mobile. Plus particulièrement, l'invention concerne un système et un procédé pour localiser un dispositif mobile en utilisant un réseau de neurones hétérogène. Le système comprend au moins un dispositif mobile (10), une plateforme d'entreprise mobile (20) et au moins un fournisseur de service de télécommunication (30). La plateforme d'entreprise mobile (20) comprend en outre un serveur de localisation (21), une base de données (22), une application de surveillance (Phone Listener Application) (23), une application de collecte (Collector Application) (24), et un dispositif de mappage de qualité de positionnement (QOP Mapper) (25).
PCT/MY2014/000195 2013-12-04 2014-06-26 Système et procédé de localisation de dispositif mobile WO2015084154A1 (fr)

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CN108830131A (zh) * 2018-04-10 2018-11-16 中科院微电子研究所昆山分所 基于深度学习的交通目标检测与测距方法
CN110225453A (zh) * 2019-06-24 2019-09-10 鲸数科技（北京）有限公司 移动终端定位方法、装置、电子设备及存储介质
CN112738716A (zh) * 2021-01-19 2021-04-30 青岛海信日立空调系统有限公司 室外机

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US5917449A (en) * 1995-06-07 1999-06-29 Sanconix Inc. Enhanced position calculation
US20040075606A1 (en) * 2002-10-22 2004-04-22 Jaawa Laiho Method and system for location estimation analysis within a communication network
US20090197612A1 (en) 2004-10-29 2009-08-06 Arto Kiiskinen Mobile telephone location application
US20130072215A1 (en) 2010-06-03 2013-03-21 Sony Ericsson Mobile Communications Ab Method and apparatus for location prediction
US8600674B1 (en) * 2007-08-15 2013-12-03 University Of South Florida Using pattern recognition in real-time LBS applications

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US5917449A (en) * 1995-06-07 1999-06-29 Sanconix Inc. Enhanced position calculation
US20040075606A1 (en) * 2002-10-22 2004-04-22 Jaawa Laiho Method and system for location estimation analysis within a communication network
US20090197612A1 (en) 2004-10-29 2009-08-06 Arto Kiiskinen Mobile telephone location application
US8600674B1 (en) * 2007-08-15 2013-12-03 University Of South Florida Using pattern recognition in real-time LBS applications
US20130072215A1 (en) 2010-06-03 2013-03-21 Sony Ericsson Mobile Communications Ab Method and apparatus for location prediction

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108830131A (zh) * 2018-04-10 2018-11-16 中科院微电子研究所昆山分所 基于深度学习的交通目标检测与测距方法
CN108830131B (zh) * 2018-04-10 2021-05-04 昆山微电子技术研究院 基于深度学习的交通目标检测与测距方法
CN110225453A (zh) * 2019-06-24 2019-09-10 鲸数科技（北京）有限公司 移动终端定位方法、装置、电子设备及存储介质
CN112738716A (zh) * 2021-01-19 2021-04-30 青岛海信日立空调系统有限公司 室外机

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