United States Patent [19J
`Olsson
`
`[54] METHOD FOR LOCATING MOBILE
`STATIONS IN A DIGITAL TELEPHONE
`NETWORK
`
`[75]
`
`Inventor: Bo Olsson, Haninge, Sweden
`
`[73] Assignee: Telia AB, Farsta, Sweden -
`
`[21] Appl. No.: 251,366
`
`[22] Filed:
`
`May 31, 1994
`
`[30]
`
`Foreign Application Priority Data
`
`Jun. 21, 1993
`
`[SE]
`
`Sweden .................................. 9302140
`
`Int. Cl.6
`................................ H04B 1/00; H04B 7/00
`[51]
`[52] U.S. Cl . ........................ 455/54.1; 455/54.2; 455/67.1
`[58] Field of Search .................................. 455/54.1, 67.1,
`455/54.2, 56.1, 33.1; 342/357, 457; 364/449,
`460, 461; 395/22
`
`[56]
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`2/1991 Young et al ............................ 342/357
`4,990,922
`6/1992 Barnard ................................... 342/457
`5,119,102
`5,155,490 10/1992 Spradley, Jr. et al ................... 342/357
`
`I lllll llllllll Ill lllll lllll lllll lllll lllll 111111111111111111111111111111111
`US005564079A
`[llJ Patent Number:
`[45J Date of Patent:
`
`5,564,079
`Oct. 8, 1996
`
`5,359,521 10/1994 Kyrtsos et al .......................... 364/449
`
`FOREIGN PATENT DOCUMENTS
`
`470151 11/1993 Sweden .
`
`Primary Examiner-Reinhard J. Eisenzopf
`Assistant Examiner-Gertrude Arthur
`Attorney, Agent, or Firm-Oblon, Spivak,
`Maier & Neustadt, P.C.
`[57]
`ABSTRACT
`
`McClelland,
`
`The invention relates to a method for locating mobile
`stations i-n a digital telecommunication network, especially
`the GSM network. According to the invention, reference
`measurements are earned out on relevant traffic routes with
`the aid of a measuring mobile in order to provide position
`information related to measured signals. With the aid of
`these reference data and the position information, an adap(cid:173)
`tive neural network is trained, which network, with the aid
`of corresponding measurement data which are transmitted
`from the mobile station to a respective base station, canies
`out the localization of the mobile station. Use of the adaptive
`neural network provides a more accurate position determi(cid:173)
`nation than earlier systems which were only based on the TA
`(timing advance) value.
`
`3 Claims, 2 Drawing Sheets
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`3
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`~1 ~4
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`1ifil
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`1--rliJ
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`Apple Inc. Exhibit 1010 Page 1
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`

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`U.S. Patent
`
`Oct. 8, 1996
`
`Sheet 1of2
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`5,564,079
`
`-.;:I-\
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`Apple Inc. Exhibit 1010 Page 2
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`

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`U.S. Patent
`
`Oct. 8, 1996
`
`Sheet 2 of 2
`
`5,564,079
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`INPUT REFERENCE DATA
`FROM MM
`
`LET ANN CALCULATE
`POSITIONS USING THE
`REFERENCE DATA FROM MM
`
`COMPARE THE CALCULATED
`POSITIONS WITH THE POSI(cid:173)
`TION ACCORDING TO GPS
`IN THE MM
`
`YES
`
`INPUT MEASUREMENT
`DATA FROM MS
`
`LET ANN CALCULATE THE
`POSITION OF MS USING
`THE MEASUREMENT DATA
`OF MS
`
`NO
`
`ADJUST WEIGHTS
`OF ANN
`
`FIC.2
`
`Apple Inc. Exhibit 1010 Page 3
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`

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`5,564,079
`
`1
`METHOD FOR LOCATING MOBILE
`STATIONS IN A DIGITAL TELEPHONE
`NETWORK
`
`FIELD OF THE INVENTION
`
`The present invention relates to a method for locating
`mobile stations in a digital telephone network, especially the
`GSM network. The invention utilizes a combination of
`reference measurements using a measuring mobile and
`anadaptive neural network which is trained by means of the
`reference data. The neural network then uses existing mea(cid:173)
`surement data from mobile stations in order to locate the
`latter.
`
`BACKGROUND OF THE INVENTION
`
`2
`SUMMARY OF THE INVENTION
`
`The present invention thus provides a method for locating
`a mobile station in a digital telecommunication network.
`5 According to the invention, reference measurements are
`carried out on relevant traffic routes in order to provide
`position information related to measured signals. An adap(cid:173)
`tive neural network is trained with the aid of measured
`reference data and position information. Corresponding
`10 measurement data are transmitted from the mobile station to
`a respective base station. The trained adaptive neural net(cid:173)
`work is coupled to the base station and carries out the
`localisation of the mobile station with the aid of measure(cid:173)
`ment data from the mobile station.
`The invention is defined in greater detail in the subsequent
`patent claims.
`
`15
`
`Various systems for locating are already known.
`EP 0 512 789 shows a locating system of high accuracy 20
`which utilizes the global positioning system (GPS) for
`determining position, speed and time information for, for
`example, a vehicle in a mobile radio system. To increase the
`accuracy in position determination, reference measurements
`can be made at known locations. These reference measure- 25
`ments are utilized for increasing the locating accuracy in the
`areas around these locations. The system also has the
`possibility of storing a digital map of the area in question
`and utilizing this for facilitating the position calculations.
`EP 0 335 558 describes a radio collllllunications system 30
`with fixed and mobile stations. The mobile stations can be
`located by measuring the transit time for signals between the
`mobile stations and at least two fixed stations. The accuracy
`of the system can be improved by storing data which
`represent a route map of the area in question in the base 35
`stations. The calculated position can then be adjusted in
`accordance with the map by assuming that the mobile station
`is located on a road.
`U.S. Pat. No. 5,166,694 describes a system for locating
`vehicles. The system is provided with good accuracy by 40
`utilizing all the information which is available in the fixed
`receivers. The system then selects an optimum array of
`information which provides the smallest error and uses this
`array for calculating the position. The calculation time can
`be shortened by only using part of the available information. 45
`U.S. Pat. No. 5,043,736 relates to a cellular telephone
`system with the possibility of obtaining the current position
`of a person in the system. The mobile station, which is
`located with the aid of satellites, transmits information about 50
`its position to the cellular telephone system. The position of
`the mobile station can be centrally monitored with the aid of
`this information.
`EP 0 431 956 shows a way of monitoring a mobile
`telephone system. The system has the possibility of receiv- 55
`ing, via the base stations, data such as position, signal
`quality and signal strength for a mobile during an ongoing
`call. This information can then be presented visually in such
`a manner that it is easy to follow where the traffic is being
`generated and what coverage there is in the system.
`The present invention permits a further improvement of
`the accuracy in locating by using an adaptive neural network
`which is trained with reference data and measurement data
`from mobile stations. The accuracy is high, especially in
`comparison with earlier systems which only utilize the 65
`timing advance (TA) information which can give consider(cid:173)
`able errors due to time dispersion.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1 is a schematic diagram of the digital telecollllllu(cid:173)
`nications network of this invention including traffic routes
`and radio units;
`FIG. 2 is a flow chart of a location method according to
`this invention.
`
`DETAILED DESCRIPTION OF PREFERRED
`EMBODIMENTS
`
`In the Figures provided, Base stations, are identified with
`the legend BS 1 with the Base Station Controller being
`labeled as BSC. A measuring mobile is labeled MM 2 using
`a Global Positioning System GPS is shown. The mobile
`stations are labeled MS 3, wherein measurements are taken
`along route 4.
`The localisation of the mobile stations is of interest to an
`operator of a digital telecommunication network from three
`aspects:
`1. It is possible to measure where the mobile telephone
`traffic is being generated, that is to say where the mobile
`stations are located when they are loading the system with
`traffic. This provides information on how the system is to be
`dimensioned.
`2. It is possible to locate where a normal mobile station is
`located by calling it. This can be an additional service.
`3. It is possible to estimate road or street traffic for the
`larger traffic routes by making reference models for these. It
`is consequently possible to estimate how many calls are
`occurring on the stretch of road and what the mean speed of
`the mobile stations, that is to say the cars, is.
`The invention utilizes information which is regularly
`measured in existing systems without needing to add inter(cid:173)
`nal functions. The information is used by further processing
`with the aid of the invention.
`The invention utilizes reference measurement of the tele(cid:173)
`communication network. A measuring mobile carries out
`measurements over all relevant traffic routes. An adaptive
`60 neural network is trained with the aid of the collected
`reference data in order to obtain a correlation between the
`measured reference data and position information. In exist(cid:173)
`ing telecommunication systems, the mobile stations continu(cid:173)
`ous! y transmit measurement data to the base stations. Using
`these measurement data, the adaptive neural network can
`carry out accurate localisation due to the training with
`reference data.
`
`Apple Inc. Exhibit 1010 Page 4
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`5,564,079
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`5
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`10
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`3
`The reference data for the most interesting traffic thor(cid:173)
`oughfares are collected by field measurements with the aid
`of a measuring mobile equipped with a GPS receiver and set
`of instruments (dead reckoning) for positioning. The equip(cid:173)
`ment is available today and has an inaccuracy of approxi-
`mately 60 m. Reference measurements should be done on all
`larger traffic thoroughfares. The stored reference data con(cid:173)
`sists, apart from the position information, for example the
`national network, of collected measurement data from the
`measuring mobile. The data include:
`received signal strength from the communicating base
`station with identity, cell identity CGI (cell global
`identity) timing advance (TA) which provides an
`approximate distance signal strength from neighboring
`base stations with respective frequency and colour 15
`code, base station colour code, BCC. In GSM, signal
`strengths are reported which have been measured by
`the mobile stations from up to six neighboring base
`stations. BCC and frequency (ARFCN) are converted
`prior to storage and use to a cell identity (CGI) so that 20
`these reference data can be used even after a frequency
`shift in the system. There must consequently exist a
`cross reference list between frequency and identity
`which is updated with each frequency shift;
`handover to a new base station with time and position is 25
`implicit in the log and can also be used.
`In the GSM system, the mobile station, with an estab(cid:173)
`lished call, transmits measurement data which correspond to
`the reference data of the system once per 480 ms period.
`This is used by the base station controller (BSC) in order to 30
`regulate the power of the mobile station and order handover
`between base stations. These so-called measurement reports
`can today be read at the interface between the base station
`and the controller. The possibility is foreseen of reading this
`information directly in the controller in the near future.
`A separate computer is connected to the base station
`controller BSC and is equipped with software for an adap(cid:173)
`tive neural network. In order to be able to use the neural
`network, it must be trained on reference data, that is to say
`all the reference data are fed into the network without 40
`position information and the output result, position, is com(cid:173)
`pared with the position which is obtained with the GPS
`equipment. If the output result deviates from the reference
`position, the weightings in the adaptive neural network are
`adjusted in accordance with an algorithm. After a sufficiently 45
`long learning sequence, the adaptive neural network can, if
`it has sufficiently many states, solve complex patterns, for
`example the signal strength and TA pattern for the localisa(cid:173)
`tion of a mobile station, that is to say locate a mobile station.
`Localisation of the mobile station can consequently be 50
`carried out by processing the abovementioned measurement
`report. By following mobile stations during a call, the mean
`
`35
`
`4
`speed of the important traffic thoroughfares can also be
`estimated, in addition to the position.
`The present invention thus offers the advantage that all
`available information is compared with the reference data
`instead of only depending on TA (timing advance), which
`can give rise to considerable errors due to time dispersion.
`Furthermore, no changes in the existing system are needed,
`but only access to the measurment data. The accuracy is
`high, at least for the streets and roads which are used for the
`reference measurements. The scope of the invention is only
`limited by the patent claims below.
`I claim:
`1. Method for locating a mobile station in a digital
`telecommunication network, comprising the step of:
`measuring no-location reference data indicative of signal
`strength and timing advance signals from plural base
`stations, wherein the measuring is performed at a
`reference mobile station on relevant traffic routes at
`known positions of the mobile station,
`training an adaptive neural network using the measured
`non-location reference data as inputs and the known
`positions of the reference mobile station as desired
`outputs,
`measuring at a subscriber mobile station non-location data
`indicative of signal strength and timing advance signals
`from said plural base stations, wherein the measured
`non-location data are transmitted from the subscriber
`mobile station to at least one of said plural base
`stations, and
`determining a location of the subscriber mobile station
`using the trained adaptive neural network, wherein the
`trained neural network is coupled to the plural base
`stations and determines the location of the subscriber
`mobile station using the measured non-location data
`from the subscriber mobile station.
`2. Method according to claim 1, wherein the step of
`measuring non-location reference data comprises:
`using the reference mobile station equipped with GPS
`receiver to measure the non-location reference data.
`3. Method according to claim 1 or 2, wherein the step of
`training the adaptive neural network comprises:
`training the adaptive neural network by inputting the
`measured non-location reference data,
`calculating a position from the measured non-location
`reference data,
`comparing the calculated position with a respective one of
`the known positions of the reference mobile station,
`and
`adjusting the adoptive neural network based on the com(cid:173)
`parison.
`
`* * * * *
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`Apple Inc. Exhibit 1010 Page 5