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.

Description:
BACKGROUND OF THE INVENTION 
     1. 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. 
     2. Description of the Related Art 
     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 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 manufacture, 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 back 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 
     FIG. 1 illustrates a cellular service area divided into cells; 
     FIG. 2 illustrates the cells of FIG. 1 further divided into sub-cells; 
     FIG. 3 illustrates one embodiment of the present invention; and 
     FIG. 4 illustrates the structure and fields which make up the pattern database in one embodiment. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1 shows a cellular (or PCS) service area  10  including a plurality of cells  12 . FIG. 1 also illustrates a plurality of base stations BS 1  . . . BS N  and at least one mobile switching center (MSC)  16 . FIG. 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 C 1 , C 2 , C 4 , C 5 , and C 6  respectively. FIG. 3 shows the hardware architecture for determining the location of a mobile unit  30  in one embodiment. 
     In particular, FIG. 3 illustrates a mobile phone  30 , three base stations BS 1 , BS 2 , BS 3 , a mobile switching center  16 , a geolocation server  32 , and a data base  34 . 
     As illustrated in FIGS. 1 and 2, the cellular service area  10  is partitioned into cells  12  and then sub-cells C 1  . . . C M . A set of detectable RF characteristics are defined for each sub-cell, which are referred to as 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 BS 1  which in turn, reports to the geolocation server  32  illustrated in FIG. 3 via the MSC  16 . The geolocation server  32  statistically  25  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  30  is reported to be in. The known set of attribute 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 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 {C 1 , C 2 , C 3  . . . , C m } is a set of sub-cells which is a partition of S, such that        S   ≅       ∑     i   -   1     m          C   i                              
     and A={A 1 , A 2 , A 3 , . . . , A n } is the set of attributes of C i  for I=1 to m. For example, A 1  is the PN code that identifies a particular base station BS x , A 2  is the strength of the PN signal, and A 3  is the phase shift. A 1  . . . A n  are random variables. D i  is the domain of A i  which contains all possible values of A i  and a j  denotes a property which is defined such that the attribute A j  has a certain value or a value set that can be used to characterize a sub-cell. For example, a j  may represent A j =v or A j &gt;v where v is a value in D j . If P(a i ) is the probability of the occurrence of a i  over all sub-cells, P(a i |C j ) is the probability of the occurrence of a I  in C j , P(C j ) the probability that the mobile unit  30  is in C j  independent of properties, and P(C j |a i ) is the probability the mobile unit  30  is in C j  given an observed property of a i . The goal of the present invention is to find the highest P(C i |A*), namely the highest probability that the mobile unit  30  is in C i  given a set of measured or observed values A*, where A*={a 1 , a 2 , a 3 , . . . , a n }. In one embodiment, P(C i |A*) can be obtained using the following Bayes formula: 
     
       
           P ( C   i   |A *)= P ( C   i ) P ( A*|C   i )/ P ( A *) 
       
     
     P(C i )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: 
     
       
           P ( A *)=Σ P ( C   s ) P ( A*|C   s ), S=1, . . . , m and 
       
     
     P(A*IC i ) can be obtained using: 
     
       
           P ( A*|C   i )= P (a 1   |C   i ) P ( a   2   |C   i ) P ( a   3   |C   i ) . . .  P ( a   n   |C   i ). 
       
     
     As the mobile unit  30  moves, the measurement is taken at time t+Δt, and P(C i ) is updated with P(C i |A* t ) where A* t  is the set of properties observed at t+Δt. 
     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 BS 1  . . . BS N  running any commercially available database 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 2 km to 25 km in radius, the size of a sub-cell with 125 m radius will result in a reasonable size for the database. For example, if the cell has a radius of 25 km, the number of 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. 
     The database  34  can also be created by the construction of a statistical model. The statistical 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 observations can then be calculated and stored in the database  34 . 
     FIG. 4 shows an example of the database  34  that contains the statistical data for C 1 , C 2 , C 3 , C 4 , C 5 , and C 6  as in the first column. The second column represents the attribute BS 1  and its values, and the third column is the probability denoted by p 1  of a particular attribute value could be observed in a sub-cell. For example, BS 1 =161/232/16 has a probability of 1 in sub-cell C 1 . In other words, let a 1  be BS 1 =161/232/16, then P(a 1 /C 1 ) 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 BS 2  and their probabilities denoted by p 2 , and so on for BS 3  and p 3 . 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*=(BS 1 =178/416/24, BS 2 =161/64/3/33, BS 3 =172/208/3/25), the probability that this observation is from a particular cell can be calculated as follows: 
     
       
           P ( A*/C   1 )=0*0*0=0 
       
     
     
       
           P ( A*/C   1 )=0*0*0=0 
       
     
     
       
           P ( A*/C   1 )=(21.6/24*1)*(26/33*0.18)*(24/25*0.18)=0.022 
       
     
     
       
           P ( A*/C   1 )=(23.3/24*1)*(0)*(0)=0 
       
     
     
       
           P ( A*/C   1 )=(19.25/24*1)*(0)*(0)=0 
       
     
     
       
           P ( A*/C   1 )=(24.8/24*1)*(0)*(0)=0 
       
     
     Note that P(A*C i )≧P(A*/C j ) for i≠j →P(C i /A*)≧P(C j /A*). Therefore, it is concluded that A* is an observation from C 3 . Note also that the probability of P(a 2 /C 3 ) is adjusted by a value 0.79 which is derived from  26 / 33 , since a 2  does not match exactly the pattern of BS 2  in C 3 . In one embodiment, the present invention uses the ratio of the pilot strength stored in the database  34  to the observed pilot strength to define the 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 as fuzzy logic can then be applied to analyze the continuous attributes. 
     Utilizing the pattern recognition-based technique 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.