CA2275911A1 - Pattern recognition-based geolocation - Google Patents
Pattern recognition-based geolocation Download PDFInfo
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- CA2275911A1 CA2275911A1 CA002275911A CA2275911A CA2275911A1 CA 2275911 A1 CA2275911 A1 CA 2275911A1 CA 002275911 A CA002275911 A CA 002275911A CA 2275911 A CA2275911 A CA 2275911A CA 2275911 A1 CA2275911 A1 CA 2275911A1
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- sub
- mobile terminal
- identifying
- comparing
- cells
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- 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
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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
- G01S5/02521—Radio frequency fingerprinting using a radio-map
Abstract
A technique for identifying a geographical location of a mobile terminal which includes receiving a set of characteristics from the mobile terminal, comparing the set of characteristics from the mobile terminal with a set of attributes for each of the cells, and identifying one of the sub-cells whose attributes most closely match the characteristics from the mobile terminal as the sub-cell in which the mobile terminal located. The attributes may include discrete RF attributes, such as average pilot strength, chip offset, or pilot strength.
The attributes may also include continuous features, such as signature waveforms.
The attributes may also include continuous features, such as signature waveforms.
Description
PATTERN RECOGNITION-BASED GEOLOCATION
Field of the Invention The present invention relates to a technique for performing pattern recognition-based geolocation under various RF propagation conditions, and more particularly, to a technique for locating a mobile caller within a cellular service area under various RF
propagation conditions.
Description of the Related Art 1o A cellular telephone system must be able to locate a mobile caller within a cellular service area under various RF propagation conditions.
Conventional methods are based on either a triangulation technique, which requires signals from or to three or more base stations, or an angle of arrival technique, which requires at least two base stations. In many areas, the number of base stations that the mobile unit can detect or can be detected by, is less than two.
Furthermore, both the triangulation and angle of arrival 2o techniques suffer from inaccuracies and signal fading which result from multi-path propagation.
Summary Of The Invention The present invention solves these problems by providing a method, apparatus, article of manufact~.~.
and propagated signal which utilize pattern recognition-based geolocation to locate a mobile caller within a cellular service area under various RF propagation conditions.
Field of the Invention The present invention relates to a technique for performing pattern recognition-based geolocation under various RF propagation conditions, and more particularly, to a technique for locating a mobile caller within a cellular service area under various RF
propagation conditions.
Description of the Related Art 1o A cellular telephone system must be able to locate a mobile caller within a cellular service area under various RF propagation conditions.
Conventional methods are based on either a triangulation technique, which requires signals from or to three or more base stations, or an angle of arrival technique, which requires at least two base stations. In many areas, the number of base stations that the mobile unit can detect or can be detected by, is less than two.
Furthermore, both the triangulation and angle of arrival 2o techniques suffer from inaccuracies and signal fading which result from multi-path propagation.
Summary Of The Invention The present invention solves these problems by providing a method, apparatus, article of manufact~.~.
and propagated signal which utilize pattern recognition-based geolocation to locate a mobile caller within a cellular service area under various RF propagation conditions.
The present invention matches the observed RF
characteristics associated with the signal transmitted by a mobile unit (or signal received and reported ba~' to a base station by a mobile unit) to a known set of RF
characteristics and other information of a particular location. The location of the mobile unit is determined or estimated when a closely match pattern is found.
Pattern recognition technology is utilized to find the matched pattern. In one embodiment, the pattern recognition technique is a Bayes formula for optimal estimation. The main advantages of the present invention are eliminating the need for detecting more than one base station and eliminating inaccuracies caused by multi-path propagation.
Brief Description Of The Drawings Figure 1 illustrates a cellular service area divided into cells;
Figure 2 illustrates the cells of Figure 1 further divided into sub-cells;
Figure 3 illustrates one embodiment of the present invention; and Figure 4 illustrates the structure and fields which make up the pattern database in one embodiment.
Detailed Description Of The Invention Figure 1 shows a cellular (or PCS) service area 10 including a plurality of cells 12. Figure 1 also illustrates a plurality of base stations BS1 ... BSS and at least one mobile switching center (MSC) 16. Figure 2 shows the cells 12 of service area 10 divided into sub-cells 20 represented by the squares formed by the grid lines. The numbers shown represent the sub-cell C1, C2, Cq, C5, and C6 respectively. Figure 3 shows the hardware architecture for determining the location of a mobile unit 30 in one embodiment.
In particular, Figure 3 illustrates a mobile phone 30, three base stations BS1, BS2, BS3, a mobile switching center 16, a geolocation server 32, and a data base 34.
to As illustrated in Figures 1 and 2, the cellular service area 10 is partitioned into cells 12 and then sub-cells C1...CM. A set of detectable RF characteristics are defined for each sub-cell, which are referred to a"
the attributes/properties of the sub-cell. The mobile unit 30 measures the RF signals that are associated with the attributes/properties and reports the results to a primary base station BS1 which in turn, reports to the geolocation server 32 illustrated in Figure 3 via the MSC 16. The geolocation server 32 statistically compares the measured values with the known attribute values of all sub-cells in a predefined area. The sub-cell that has the best matched set of attribute values with the measured values is the one that the mobile unit is repeated to be in. The known set of attribute 25 values in database 34 for each sub-cell account statistically for weather conditions, time of day, and other environmental variations that could affect the RF
characteristics. As the mobile unit 30 moves, the comparison is periodically performed and the location of 3o the mobile unit 30 can be determined at any given time.
As a result, the problem of geolocation can be solved by pattern recognition.
characteristics associated with the signal transmitted by a mobile unit (or signal received and reported ba~' to a base station by a mobile unit) to a known set of RF
characteristics and other information of a particular location. The location of the mobile unit is determined or estimated when a closely match pattern is found.
Pattern recognition technology is utilized to find the matched pattern. In one embodiment, the pattern recognition technique is a Bayes formula for optimal estimation. The main advantages of the present invention are eliminating the need for detecting more than one base station and eliminating inaccuracies caused by multi-path propagation.
Brief Description Of The Drawings Figure 1 illustrates a cellular service area divided into cells;
Figure 2 illustrates the cells of Figure 1 further divided into sub-cells;
Figure 3 illustrates one embodiment of the present invention; and Figure 4 illustrates the structure and fields which make up the pattern database in one embodiment.
Detailed Description Of The Invention Figure 1 shows a cellular (or PCS) service area 10 including a plurality of cells 12. Figure 1 also illustrates a plurality of base stations BS1 ... BSS and at least one mobile switching center (MSC) 16. Figure 2 shows the cells 12 of service area 10 divided into sub-cells 20 represented by the squares formed by the grid lines. The numbers shown represent the sub-cell C1, C2, Cq, C5, and C6 respectively. Figure 3 shows the hardware architecture for determining the location of a mobile unit 30 in one embodiment.
In particular, Figure 3 illustrates a mobile phone 30, three base stations BS1, BS2, BS3, a mobile switching center 16, a geolocation server 32, and a data base 34.
to As illustrated in Figures 1 and 2, the cellular service area 10 is partitioned into cells 12 and then sub-cells C1...CM. A set of detectable RF characteristics are defined for each sub-cell, which are referred to a"
the attributes/properties of the sub-cell. The mobile unit 30 measures the RF signals that are associated with the attributes/properties and reports the results to a primary base station BS1 which in turn, reports to the geolocation server 32 illustrated in Figure 3 via the MSC 16. The geolocation server 32 statistically compares the measured values with the known attribute values of all sub-cells in a predefined area. The sub-cell that has the best matched set of attribute values with the measured values is the one that the mobile unit is repeated to be in. The known set of attribute 25 values in database 34 for each sub-cell account statistically for weather conditions, time of day, and other environmental variations that could affect the RF
characteristics. As the mobile unit 30 moves, the comparison is periodically performed and the location of 3o the mobile unit 30 can be determined at any given time.
As a result, the problem of geolocation can be solved by pattern recognition.
In more detail, assume S is the area that the mobile unit 30 is known to be in. For example, S may be a sector that can be determined by the existing technology where the mobile unit 30 is. Further assume that {C1, C2, C3 . . . , Cm} is a set of sub-cells which: i. _~
a partition of 5, such that m S-~C;
and A={Al, A2, A3, . . ., A"} is the set of attributes of Ci for I=1 to m. For example, A1 is the PN code that to identifies a particular base station BSx, AZ is the strength of the PN signal, and A3 is the phase shift.
A1...A,, are random variables. Di is the domain of Ai which contains all possible.values of Ai and a~ denotes a property which is defined such that the attribute A~ has a certain value or a value set that can be used to characterize a sub-cell. For example, a~ may represent A~=v or A~>v where v is a value in D~. If P(ai) is the probability of the occurrence of ai over all sub-cells, P (ai ~ C~ ) is the probability of the occurrence of ai in C~, 2o P(C~) the probability that the mobile unit 30 is in C~
independent of properties, and P(Cy ai) is the probability the mobile unit 30 is in C~ given an observed property of ai. The goal of the present invention is to find the highest P(Ci~A*), namely the highest probability that the mobile unit 30 is in Ci given a set of measured or observed values A*, where A*={al, a2, a3, . . ., an} .
In one embodiment, P(Ci~A*) can be obtained using the following Bayes formula:
P(Ci~A*)=P(Ci)P(A*~Ci)/P(A*) .
P(Ci)can be assumed to be 1/m initially, a uniform distribution, since there is no a priori knowledge where the mobile unit 30 might be. P(A*) can be obtained using:
a partition of 5, such that m S-~C;
and A={Al, A2, A3, . . ., A"} is the set of attributes of Ci for I=1 to m. For example, A1 is the PN code that to identifies a particular base station BSx, AZ is the strength of the PN signal, and A3 is the phase shift.
A1...A,, are random variables. Di is the domain of Ai which contains all possible.values of Ai and a~ denotes a property which is defined such that the attribute A~ has a certain value or a value set that can be used to characterize a sub-cell. For example, a~ may represent A~=v or A~>v where v is a value in D~. If P(ai) is the probability of the occurrence of ai over all sub-cells, P (ai ~ C~ ) is the probability of the occurrence of ai in C~, 2o P(C~) the probability that the mobile unit 30 is in C~
independent of properties, and P(Cy ai) is the probability the mobile unit 30 is in C~ given an observed property of ai. The goal of the present invention is to find the highest P(Ci~A*), namely the highest probability that the mobile unit 30 is in Ci given a set of measured or observed values A*, where A*={al, a2, a3, . . ., an} .
In one embodiment, P(Ci~A*) can be obtained using the following Bayes formula:
P(Ci~A*)=P(Ci)P(A*~Ci)/P(A*) .
P(Ci)can be assumed to be 1/m initially, a uniform distribution, since there is no a priori knowledge where the mobile unit 30 might be. P(A*) can be obtained using:
5 P (A* ) _~ P (Cs) P (A* I Cs) , S=l, . . . , m and P(A*ICi) can be obtained using:
P (A* I C~ ) =P ( al I C~ ) P ( a2 I CO P ( a3 I C~ ) . . . P ( an I CO .
As the mobile unit 30 moves, the measurement is taken at time t+~t, and P (Ci) is updated with P (C; f A*t. ) 1o where A*r is the set of properties observed at t+~t.
The database 34 for the sub-cells is defined by a set of attributes. The database 34 can be implemented as an adjunct server at the MSC 16 or any base station BS1 . . . BS~r running any commercially available databac,<
management system. The database 34 is organized by cells and by sectors to provide efficient searching methods.
The attribute values may be obtained by a survey of the coverage area and the probability distribution function for A* can be constructed based on the survey results.
Given the size of a regular cell is 2km to 25km in radius, the size of a sub-cell with 125m radius will result in a reasonable size for the database. For example, if the cell has a radius of 25km, the number ~-~f sub-cells will be approximately 40,000 in a cell. Hence, there will be 40,000 records per base station which is a relatively small database.
P (A* I C~ ) =P ( al I C~ ) P ( a2 I CO P ( a3 I C~ ) . . . P ( an I CO .
As the mobile unit 30 moves, the measurement is taken at time t+~t, and P (Ci) is updated with P (C; f A*t. ) 1o where A*r is the set of properties observed at t+~t.
The database 34 for the sub-cells is defined by a set of attributes. The database 34 can be implemented as an adjunct server at the MSC 16 or any base station BS1 . . . BS~r running any commercially available databac,<
management system. The database 34 is organized by cells and by sectors to provide efficient searching methods.
The attribute values may be obtained by a survey of the coverage area and the probability distribution function for A* can be constructed based on the survey results.
Given the size of a regular cell is 2km to 25km in radius, the size of a sub-cell with 125m radius will result in a reasonable size for the database. For example, if the cell has a radius of 25km, the number ~-~f sub-cells will be approximately 40,000 in a cell. Hence, there will be 40,000 records per base station which is a relatively small database.
There are at least two methods that can be used to create the database 34.
One way is to use specially designed equipment to survey the service area 10 exhaustively. The equipment is used to gather RF characteristics for a location and record the data. The data records are then processed to create the database 34. This process is automated using a computer program to gather data and create the database 34. This method provides an accurate database.
l0 The database 34 can also be created by the construction of a statistical model. The statistic<v model is constructed by using an RF propagation formula.
The parameters in the model are verified by a limited number of observations of the RF characteristics in sample locations in the service area 10. The probability of certain observation can then be calculated and stored in the database 34.
Figure 4 shows an example of the database 34 that contains the statistical data for C1, C2, C3, C4, C5, and C6 as in the first column. The second column represents the attribute BS1 and its values, and the third column is the probability denoted by pl of a particular attribute:
value could be observed in a sub-cell. For example, BS1=161/232/16 has a probability of 1 in sub-cell C1. In other words, let al be BS1=161/232/16, then P (al/C1) has the value 1. The numbers 161/232/16, for example, separate by "/" represent the base station ID, the pilot ID, and the average pilot strength for the base station and pilot, respectively. Similarly, the fourth column and fifth column represent the attribute values for BS2 and their probabilities denoted by p2, and so on for BS3 and p3. The four numbers separated by "/" in the latter cases represent the base station ID, the pilot ID, the chip offset, and the pilot strength, respectively.
Example If an observation of interest is A*=(BS1=178/416j~~~;
BSZ=161/64/3/33, BS3=172/208/3/25), the probability th<~.t-this observation is from a particular cell can be calculated as follows:
P(A*/C1)=0*0*0=0 P(A*/CZ)=0*0*0=0 to P (A*/C3)=(21.6/24*1) * (26/33*0.18) * (24/25*0.18)= 0.022 P(A*/CQ)=(23.3/24*1) * (0) * (0)=0 P(A*/CS)=(19.25/24*1)*(0)*(0)=0 P(A*/C6)=(24.8/24*1) * (0) * (0)=0 Note that P (A*Ci ) >_P (A* /C~ ) for i~j P(Ci/A*)>_P(C~/A*). Therefore, it is concluded that A* is an observation from C3. Note also that the probability of P(a2/C3) is adjusted by a value 0.79 which is derived from 26/33, since a2 does not match exactly the pattern of BSZ in C3. In one embodiment, the present invention 2o uses the ratio of the pilot strength stored in the database 34 to the observed pilot strength to define tht measure of the closeness between the two values.
In the above embodiment, the RF attributes are discrete attributes, such as average pilot strength, chip offset, or pilot strength. However, in another embodiment, continuous attributes or features may be utilized, such as signature waveforms.
The set of attributes may be either replaced or enhanced by the signatures of the measured signal waveforms. Other pattern recognition techniques such a fuzzy logic can then be applied to analyze the continuous attributes.
Utilizing the pattern recognition-based teci,: ,Iue or fuzzy logic technique described above may be performed utilizing only one base station or more than one base station.
The foregoing merely illustrates the principles of the invention. It will thus be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are thus within its spirit and scope.
One way is to use specially designed equipment to survey the service area 10 exhaustively. The equipment is used to gather RF characteristics for a location and record the data. The data records are then processed to create the database 34. This process is automated using a computer program to gather data and create the database 34. This method provides an accurate database.
l0 The database 34 can also be created by the construction of a statistical model. The statistic<v model is constructed by using an RF propagation formula.
The parameters in the model are verified by a limited number of observations of the RF characteristics in sample locations in the service area 10. The probability of certain observation can then be calculated and stored in the database 34.
Figure 4 shows an example of the database 34 that contains the statistical data for C1, C2, C3, C4, C5, and C6 as in the first column. The second column represents the attribute BS1 and its values, and the third column is the probability denoted by pl of a particular attribute:
value could be observed in a sub-cell. For example, BS1=161/232/16 has a probability of 1 in sub-cell C1. In other words, let al be BS1=161/232/16, then P (al/C1) has the value 1. The numbers 161/232/16, for example, separate by "/" represent the base station ID, the pilot ID, and the average pilot strength for the base station and pilot, respectively. Similarly, the fourth column and fifth column represent the attribute values for BS2 and their probabilities denoted by p2, and so on for BS3 and p3. The four numbers separated by "/" in the latter cases represent the base station ID, the pilot ID, the chip offset, and the pilot strength, respectively.
Example If an observation of interest is A*=(BS1=178/416j~~~;
BSZ=161/64/3/33, BS3=172/208/3/25), the probability th<~.t-this observation is from a particular cell can be calculated as follows:
P(A*/C1)=0*0*0=0 P(A*/CZ)=0*0*0=0 to P (A*/C3)=(21.6/24*1) * (26/33*0.18) * (24/25*0.18)= 0.022 P(A*/CQ)=(23.3/24*1) * (0) * (0)=0 P(A*/CS)=(19.25/24*1)*(0)*(0)=0 P(A*/C6)=(24.8/24*1) * (0) * (0)=0 Note that P (A*Ci ) >_P (A* /C~ ) for i~j P(Ci/A*)>_P(C~/A*). Therefore, it is concluded that A* is an observation from C3. Note also that the probability of P(a2/C3) is adjusted by a value 0.79 which is derived from 26/33, since a2 does not match exactly the pattern of BSZ in C3. In one embodiment, the present invention 2o uses the ratio of the pilot strength stored in the database 34 to the observed pilot strength to define tht measure of the closeness between the two values.
In the above embodiment, the RF attributes are discrete attributes, such as average pilot strength, chip offset, or pilot strength. However, in another embodiment, continuous attributes or features may be utilized, such as signature waveforms.
The set of attributes may be either replaced or enhanced by the signatures of the measured signal waveforms. Other pattern recognition techniques such a fuzzy logic can then be applied to analyze the continuous attributes.
Utilizing the pattern recognition-based teci,: ,Iue or fuzzy logic technique described above may be performed utilizing only one base station or more than one base station.
The foregoing merely illustrates the principles of the invention. It will thus be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are thus within its spirit and scope.
Claims (50)
1. A method of identifying a geographical location of a mobile terminal, comprising the steps of:
comparing a set of RF characteristics from the mobile terminal with a set of RF attributes for each of a plurality of sub-cells; and identifying a sub-cell of the plurality of sub-cells whose set of RF attributes most closely match the set of RF characteristics from the mobile terminal as the sub-cell in which the mobile terminal is locate .
comparing a set of RF characteristics from the mobile terminal with a set of RF attributes for each of a plurality of sub-cells; and identifying a sub-cell of the plurality of sub-cells whose set of RF attributes most closely match the set of RF characteristics from the mobile terminal as the sub-cell in which the mobile terminal is locate .
2. The method of claim 1, wherein the set of RF
attributes for each of the plurality of sub-cells is stored at a mobile system base station or at a mobile switching center.
attributes for each of the plurality of sub-cells is stored at a mobile system base station or at a mobile switching center.
3. The method of claim 2, wherein only one mobile system base station is required to identify the location of the mobile terminal.
4. The method of claim 1, wherein the comparing and identifying steps are performed using pattern recognition.
5. The method of claim l, wherein the set of RF
attributes for each of the plurality of sub-cells vary based on weather conditions, time of day, and environmental factors.
attributes for each of the plurality of sub-cells vary based on weather conditions, time of day, and environmental factors.
6. The method of claim 1, wherein said comparing and identifying steps are repeated over time as the mobile terminal moves.
7. The method of claim 1, said comparing step further comparing the set of RF
characteristics from the mobile terminal with the set of RF attributes for each of the plurality of sub-cells and a waveform from the mobile terminal with at least one signature waveform for each of the plurality of sub-cells;
said identifying step identifying the sub-cell of the plurality of sub-cells whose set of RF attributes and at least one signature waveform most closely match the set of RF characteristics and the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
characteristics from the mobile terminal with the set of RF attributes for each of the plurality of sub-cells and a waveform from the mobile terminal with at least one signature waveform for each of the plurality of sub-cells;
said identifying step identifying the sub-cell of the plurality of sub-cells whose set of RF attributes and at least one signature waveform most closely match the set of RF characteristics and the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
8. A method of identifying a geographical location of a mobile terminal, comprising the steps of:
comparing a waveform from the mobile terminal with at least one signature waveform for each of a plurality of sub-cells; and identifying a sub-cell of the plurality of sub-cells whose at least one signature waveform most closely matches the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
comparing a waveform from the mobile terminal with at least one signature waveform for each of a plurality of sub-cells; and identifying a sub-cell of the plurality of sub-cells whose at least one signature waveform most closely matches the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
9. The method of claim 8, wherein the at least one signature waveform for each of the plurality of sub-cells is stored at a mobile system base station or at a mobile switching center.
10. The method of claim 9, wherein only one mobile system base station is required to identify the location of the mobile terminal.
11. The method of claim 8, wherein the comparing and identifying steps are performed using fuzzy logic.
12. The method of claim 8, wherein said comparing and identifying steps are repeated over time as the mobile terminal moves.
13. A processor for identifying a geographical location of a mobile terminal, comprising:
a comparing unit for comparing a set of RF
characteristics from the mobile terminal with a set of RF attributes for each of a plurality of sub-cells; and an identifying unit for identifying a sub-cell of the plurality of sub-cells whose set of RF attributes most closely match the set of RF characteristics from the mobile terminal as the sub-cell in which the mobile terminal is located.
a comparing unit for comparing a set of RF
characteristics from the mobile terminal with a set of RF attributes for each of a plurality of sub-cells; and an identifying unit for identifying a sub-cell of the plurality of sub-cells whose set of RF attributes most closely match the set of RF characteristics from the mobile terminal as the sub-cell in which the mobile terminal is located.
14. The processor of claim 13, wherein the set of RF attributes for each of the plurality of sub-cells is stored at a mobile system base station or at a mobile switching center.
15. The processor of claim 14, wherein only one mobile system base station is required to identify the location of the mobile terminal.
16. The processor of claim 13, wherein the comparing and identifying units utilize pattern recognition.
17. The processor of claim 13, wherein the set of RF attributes for each of the plurality of sub-cells vary based on weather conditions, time of day, and environmental factors.
18. The processor of claim 13, wherein said comparing and identifying units repeatedly operate over time as the mobile terminal moves.
19. The processor of claim 13, said comparing unit further comparing the set of RF
characteristics from the mobile terminal with the set of RF attributes for each of the plurality of sub-cells and a waveform from the mobile terminal with at least one signature waveform for each of the plurality of sub-cells;
said identifying unit identifying the sub-cell of the plurality of sub-cells whose set of RF attributes and at least one signature waveform most closely match the set of RF characteristics and the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
characteristics from the mobile terminal with the set of RF attributes for each of the plurality of sub-cells and a waveform from the mobile terminal with at least one signature waveform for each of the plurality of sub-cells;
said identifying unit identifying the sub-cell of the plurality of sub-cells whose set of RF attributes and at least one signature waveform most closely match the set of RF characteristics and the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
20. A processor for identifying a geographical location of a mobile terminal, comprising:
a comparing unit for comparing a waveform from the mobile terminal with at least one signature waveform for each of a plurality of sub-cells; and an identifying unit for identifying a sub-cell of the plurality of sub-cells whose at least one signature waveform most closely matches the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
a comparing unit for comparing a waveform from the mobile terminal with at least one signature waveform for each of a plurality of sub-cells; and an identifying unit for identifying a sub-cell of the plurality of sub-cells whose at least one signature waveform most closely matches the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
21. The processor of claim 20, wherein the at least one signature waveform for each of the plurality of sub-cells is stored. at a mobile system base station or at a mobile switching center.
22. The processor of claim 21, wherein only one mobile system base station is required to identify the location of the mobile terminal.
23. The processor of claim 20, wherein the comparing and identifying units utilize fuzzy logic.
24. The processor of claim 20, wherein said comparing and identifying units repeatedly operate over time as the mobile terminal moves.
25. A computer program embodied in a computer-readable medium for identifying a geographical location of a mobile terminal, comprising:
a comparing source code segment for comparing a set of RF characteristics from the mobile terminal with a set of RF attributes for each of a plurality of sub-cells; and an identifying source code segment for identifying a sub-cell of the plurality of sub-cells whose set of RF
attributes most closely match the set of RF
characteristics from the mobile terminal as the sub-cell in which the mobile terminal is located.
a comparing source code segment for comparing a set of RF characteristics from the mobile terminal with a set of RF attributes for each of a plurality of sub-cells; and an identifying source code segment for identifying a sub-cell of the plurality of sub-cells whose set of RF
attributes most closely match the set of RF
characteristics from the mobile terminal as the sub-cell in which the mobile terminal is located.
26. The computer program of claim 25, wherein the set of RF attributes for each of the plurality of sub-cells is stored at a mobile system base station or at a mobile switching center.
27. The computer program of claim 26, wherein only one mobile system base station is required to identify the location of the mobile terminal.
28. The computer program of claim 25, wherein the comparing and identifying source code segments utilize pattern recognition.
29. The computer program of claim 25, wherein the set of RF attributes for each of the plurality of sub-cells vary based on weather conditions, time of day and environmental factors.
30. The computer program of claim 25, wherein said comparing and identifying source code segments are executed repeatedly over time as the mobile terminal moves.
31. The computer program of claim 25, said comparing source code segment further comparing the set of RF characteristics from the mobile terminal with the set of RF attributes for each of the plurality of sub-cells and a waveform from the mobile terminal with at least one signature waveform for each of the plurality of sub-cells;
said identifying source code segment identifying the sub-cell of the plurality of sub-cells whose set of RF attributes and at least one signature waveform most closely match the set of RF characteristics and the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
said identifying source code segment identifying the sub-cell of the plurality of sub-cells whose set of RF attributes and at least one signature waveform most closely match the set of RF characteristics and the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
32. A computer program embodied in a computer-readable medium for identifying a geographical location of a mobile terminal, comprising:
a comparing source code segment for comparing a waveform from the mobile terminal with at least one signature waveform for each of a plurality of sub-cells;
and an identifying source code segment for identifying a sub-cell of the plurality of sub-cells whose at least one signature waveform most closely matches the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
a comparing source code segment for comparing a waveform from the mobile terminal with at least one signature waveform for each of a plurality of sub-cells;
and an identifying source code segment for identifying a sub-cell of the plurality of sub-cells whose at least one signature waveform most closely matches the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
33. The computer program of claim 32, wherein the at least one signature waveform for each of the plurality of sub-cells is stored at a mobile system base station or at a mobile switching center.
34. The computer program of claim 33, wherein only one mobile system base station is required to identify the location of the mobile terminal.
35. The computer program of claim 32, wherein the comparing and identifying source code segments utilize fuzzy logic.
36. The computer program of claim 32, wherein said comparing and identifying source code segments are executed repeatedly over time as the mobile terminal moves.
37. A computer data signal for identifying a geographical location of a mobile terminal, comprising:
a comparing signal segment for comparing a set of RF characteristics from the mobile terminal with a set of RF attributes for each of a plurality of sub-cells;
and an identifying signal segment for identifying a sub-cell of the plurality of sub-cells whose set of RF
attributes most closely match the set of RF
characteristics from the mobile terminal as the sure-cell in which the mobile terminal is located.
a comparing signal segment for comparing a set of RF characteristics from the mobile terminal with a set of RF attributes for each of a plurality of sub-cells;
and an identifying signal segment for identifying a sub-cell of the plurality of sub-cells whose set of RF
attributes most closely match the set of RF
characteristics from the mobile terminal as the sure-cell in which the mobile terminal is located.
38. The computer data signal of claim 37, wherein the set of RF attributes for each of the plurality of sub-cells is stored at a mobile system base station or at a mobile switching center.
39. The computer data signal of claim 38, wherein only one mobile system base station is required to identify the location of the mobile terminal.
40. The computer data signal of claim 37, wherein the comparing and identifying signal segments utilize pattern recognition.
41. The computer data signal of claim 37, wherein the set of RF attributes for each of the plurality of sub-cells vary based on weather conditions, time of day, and environmental factors.
42. The computer data signal of claim 37, wherein said comparing and identifying signal segments are repeated over time as the mobile terminal moves.
43. The computer data signal of claim 37, said comparing signal segment further comparing the set of RF characteristics from the mobile terminal with the set of RF attributes for each of the plurality of sub-cells and a waveform from the mobile terminal with at least one signature waveform for each of the plurality of sub-cells; said identifying signal segment identifying the sub-cell of the plurality of sub-cells whose set of RF attributes and at least one signature waveform most closely match the set of RF
characteristics and the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
characteristics and the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
44. A computer data signal for identifying a geographical location of a mobile terminal, comprising:
a comparing signal segment for comparing a waveform from the mobile terminal with at least one signature waveform for each of a plurality of sub-cells; and an identifying signal segment for identifying a sub-cell of the plurality of sub-cells whose at least one signature waveform most closely matches the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
a comparing signal segment for comparing a waveform from the mobile terminal with at least one signature waveform for each of a plurality of sub-cells; and an identifying signal segment for identifying a sub-cell of the plurality of sub-cells whose at least one signature waveform most closely matches the waveform from the mobile terminal as the sub-cell in which the mobile terminal is located.
45. The computer data signal of claim 44, wherein the at least one signature waveform for each of the plurality of sub-cells is stored at a mobile system base station or at a mobile switching center.
46. The computer data signal of claim 45, wherein only one mobile system base station is required to identify the location of the mobile terminal.
47. The computer data signal of claim 44, wherein the comparing and identifying signal segments utilize fuzzy logic.
48. The computer data signal of claim 44, wherein said comparing and identifying signal segments are repeated over time as the mobile terminal moves.
49. The computer data signal of claim 37, wherein the computer data signal is embodied in a carrier wave.
50. The computer data signal of claim 37, wherein the computer data signal is embodied in a carrier wave.
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|---|---|---|---|
| US09/139,107 US6496701B1 (en) | 1998-08-25 | 1998-08-25 | Pattern-recognition-based geolocation |
| US09/139,107 | 1998-08-25 |
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| Publication Number | Publication Date |
|---|---|
| CA2275911A1 true CA2275911A1 (en) | 2000-02-25 |
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ID=22485163
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002275911A Abandoned CA2275911A1 (en) | 1998-08-25 | 1999-06-22 | Pattern recognition-based geolocation |
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|---|---|
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| EP (1) | EP0982964B1 (en) |
| JP (1) | JP2000092556A (en) |
| KR (1) | KR100605781B1 (en) |
| CN (1) | CN1255812A (en) |
| AU (1) | AU4466899A (en) |
| BR (1) | BR9903778A (en) |
| CA (1) | CA2275911A1 (en) |
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- 1999-08-25 JP JP11237679A patent/JP2000092556A/en active Pending
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