Geolocation estimation method for CDMA terminals based on...

Telecommunications – Radiotelephone system – Zoned or cellular telephone system

Reexamination Certificate

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C455S426100, C342S357490

Reexamination Certificate

active

06263208

ABSTRACT:

BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a method for locating a mobile telephone unit within a cellular service area, and more particularly to a method for estimating the location of a CDMA mobile unit based upon the probability of its being at a particular location of the service area in response to a sequential set of attributes observed by the mobile unit and reported back to a base station.
2. Description of Related Art
A cellular telephone system must be able to locate a mobile unit within a cellular service area under various RF propagation conditions such, for example, when an E911 call is made from the mobile unit. Conventional methods for locating a mobile unit are typically based on either a triangulation technique which requires signals from three or more base stations within a designated service area, or an angle of arrival technique which requires at least two base stations. In many areas, the number of base stations the mobile unit can detect is less than two. Furthermore, both the triangulation and angle of arrival techniques inherently suffer from inaccuracies and signal fading which result from multi-path propagation.
In the first above-noted related patent application Ser. No. 09/139,107 entitled “Pattern Recognition-Based Geolocation”, RF characteristics pertaining to one or more pilot signals radiated from a base station and specific to a particular location within the service area are detected by a mobile unit and transmitted back to a base station where they are matched to a known set of RF characteristics and other information stored in a database located, for example, in a base station server. The database contains what is known in the art as attribute information and includes, for example, RF characteristics, e.g. pilot signal strength measurements indicative of power and phase-offset, time delay, angle of arrival and round-trip delay of pilot signals which differentiates one location from another. For convenience, the cellular service area is divided into a rectilinear grid and an exhaustive survey consisting of measurements of different attributes is carried out at each of the grid points (sub-cells) to identify the attribute values associated therewith. This information is stored in the database. It should be noted that collection of this information is an inherently time consuming and costly procedure.
In the second above-noted related patent application Ser. No. 09/294,997 entitled “A Bayesian-Update Based Location Prediction Method For CDMA systems”, the invention is directed to a method of estimating, by a Bayesian probability computation procedure, the location of a mobile unit in the service area of a CDMA cellular telephone system using a model based approach which, among other things, simplifies the generation of a database containing a pilot signal visibility probabilities. This eliminates the need for repeated attribute measurements at all of the grid points (sub-cells) in the cellular service area. The database is generated by running a simulation for all grid-points or sub-cells in the service area with probability values then being stored in a memory. A probability distribution is then computed using two sets of probabilities, one set comprising the pilot signal visibility probabilities stored in the database, and the other set comprising phase-offset probabilities which are generated in real time without the need for a stored database. The mobile unit typically reports multiple sets of measurements made at relatively small time intervals and an “a-posteriori” location probability distribution is generated to provide an estimate of the mobile unit's location. The location probability distribution is computed sequentially so that the “a-posteriori” distribution computed at the end of one iteration is used as the “a-priori” distribution for the next iteration.
SUMMARY OF THE INVENTION
The present invention is directed to a method of estimating the location of a mobile unit in the service area of a CDMA cellular telephone system also using a model based approach, but which now eliminates the need for a stored database containing pilot signal visibility probabilities for all of the grid points or sub-cells in the cellular service area as described above with respect to the above referenced related Bayesian-Update invention, U.S. Ser. No. 09/294,997.
In the present invention, pilot power measurements are utilized and comprise measurements made by the mobile unit of all of the received pilot signals from the base stations which are visible to the mobile unit in its neighbor set. The mobile unit then reports these measurements, typically in the form of pilot strength measurement messages. These messages are then used by computing apparatus at one of the base stations, or at a mobile switching center, which provides a common access point to a plurality of base stations, to perform a sequential location estimation procedure based on a Bayesian probability framework. The estimation procedure is based entirely on analytical results involving one or more key approximations derived, for example, from an integrated model of the wireless communications system, its RF environment, and attribute measurement. These analytical results are obtained by digital computer apparatus using compact, straightforward computer code, i.e., software, thus eliminating the need for a relatively large database of stored visibility probabilities heretofore required.
The model based approach of this invention requires identifying the underlying RF and attribute measurement model for the coverage area. These models are represented by a small set of parameters which are determined by carrying out measurements at a plurality of systematically selected locations. These measurements are then used to adjust the values of the parameters until an acceptable match between the pilot strength values predicted by the model and the corresponding measurements exist. These model parameters are then stored for subsequent location prediction iterations which involve computing an estimate of the mobile unit's location using a mean or mode of the “a-posteriori” location probability distribution following a computation of the conditional probability of the mobile unit observing the reported pilot measurements if the mobile unit were located at each grid point in the zone.
While pilot strength measurements are typically used, other measurements which can be reported to a base station by the mobile, such as phase offsets between different pilots and angle of arrival, can be included or used in place of pilot strength measurements.


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