Geolocation estimation method for CDMA terminals based on pilot strength measurements

• US 6,263,208 B1
• Filed: 05/28/1999
• Issued: 07/17/2001
• Est. Priority Date: 05/28/1999
• Status: Expired due to Term

• Current Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Original Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Inventors: Rege, Kiran M., Jeske, Daniel R., Chang, Kirk K.
• Primary Examiner(s): Urban, Edward F.
• Assistant Examiner(s): Jackson, Blane J.
• Application Number:
  US09/321,729
• Time in Patent Office: 781 Days
• Field of Search: 455/456, 455/436, 455/440, 455/524, 455/450, 455/426, 342/357.01, 342/357.02, 342/357.03, 342/357.04
• US Class Current: 455/456.3
• CPC Class Codes: G01S 5/02521 using a radio-map
• CPC Class Codes: G01S 5/02524 Creating or updating the radio-map
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

The location of a mobile unit in the service area of a CDMA wireless communications system is determined by a location probability distribution procedure that is based entirely on analytical results derived from an integrated model of the wireless communications system, its RF environment and attribute measurement. The mobile unit measures and reports attribute values of pilot signal strength of all pilot signals visible to the mobile unit at its present location, whereupon a location probability distribution is computed based on a Bayesian probability algorithm including a set of stored model parameters.

Claims

1. A method of estimating the location of a mobile communications unit in a wireless communications service area, comprising the steps of:

(a) receiving signal strength measurements transmitted from a mobile unit located in the service area, said signal strength measurements being specific to the location wherein the mobile unit is presently located;

(b) computing a location probability distribution using Bayes theorem of the mobile unit within the service area in response to the received signal strength measurements using analytical results derived from a model that accounts for distance loss, shadow fading, fast fading and measurement errors the service area being defined in terms of a set of stored parameters that are associated with each of said plurality of locations in the service area; and

(c) generating an output indicative of the probability that the mobile unit is located at each of said plurality of locations in the service area.

2. A method according to claim 1 wherein said model comprises an integrated model of the wireless communication system, its RF environment, and received signal strength measurements.

3. A method according to claim 1 and additionally including a step (d) prior to step (b) of reducing the effective size of the service area to a sub-region of present interest and using it in step (b) so as to reduce the number of computations required therein.

4. A method according to claim 3 wherein said mobile unit comprises a CDMA mobile unit.

5. A method according to claim 4 wherein said received signal strength measurements comprises power measurements of one or more pilot signals visible to the mobile unit in said sub-region.

6. A method according to claim 5 wherein said received signal strength measurements comprises power measurements of substantially all pilot signals visible to the mobile unit in said subregion.

7. A method according to claim 6 wherein the location probability distribution computed in step (b) comprises an a-posteriori location probability distribution.

8. A method according to claim 7 wherein the location probability distribution is iteratively updated on receiving new received signal strength measurements from the mobile unit and wherein the a-posteriori probability distribution computed in one iteration comprises an a-priori distribution for the next iteration.

9. A method according to claim 1 and prior to steps (a)-(c), additionally including the steps of:

(d) generating an RF environment and measurement model of the service area, said model being characterized by a set of parameters having initial values;

(e) performing measurements at a plurality of systematically selected locations within the service area and obtaining therefrom a set of measured signal strength values for said locations;

(f) adjusting said initial values of the model parameters until the model is able to predict signal strength values which substantially match the corresponding measured values; and

(g) storing the adjusted values of the model parameters for subsequent use in step (b).

10. A method according to claim 1 wherein said model comprises a probabilistic model.

11. Apparatus for estimating the location of a mobile communications unit in the service area of a wireless communications system comprising:

a plurality of base stations and at least one switching center providing a common access to said plurality of base stations;

computing apparatus including a memory, electrically connected to at least one of said base stations or said switching center, for storing a set of parameters of an integrated model of the wireless communications including the RF environment and wherein the values of the parameters are adjusted to obtain a substantial match between measured signal strength values at a predetermined number of locations in the service area and the corresponding values predicted by the model;

said computing apparatus further including a software for calculating, in response to signal strength values being measured and reported by the mobile unit from a specific location within the service area, a location probability distribution using Bayes theorem of the mobile unit within the service area using analytical results; and

,

circuit means coupled to said computing apparatus for generating an output indicative of the location probability distribution.

12. A system according to claim 11 wherein the location probability distribution comprises an a-posteriori location probability distribution.

13. A system according to claim 12 wherein the location probability distribution computation is iteratively updated on receiving new signal strength measurements from the mobile unit.

14. A system according to claim 13 wherein the a-posteriori location probability distribution computed in one iteration of a plurality of successive iterations comprises an a-priori location probability distribution of the next iteration.

15. A system according to claim 11 wherein the service area is subdivided into a plurality of sub-regions including at least one base station therein.

16. A system according to claim 11 wherein said mobile unit comprises a unit operable in connection with a CDMA wireless communications system.

17. A method of estimating the location of a mobile communications unit in a wireless communications service area, comprising the steps of:

(a) receiving measured quantities of a signal transmitted from a mobile unit located in the service area, said measured quantities being specific to the location wherein the mobile unit is presently located;

(b) computing a location probability distribution using Bayes theorem of the mobile unit within the service area in response to the received measured quantities using analytical results derived from a probabilistic model that accounts for distance loss, shadow fading, fast fading and measurement errors, the model being defined in terms of a set of stored parameters that are associated with the plurality of locations in the service area; and

(c) generating an output indicative of the probability that the mobile is located at each of said plurality of locations in the service area.

18. A method of estimating the location of a mobile communications unit in a wireless communications service area, comprising the iterative steps of:

(a) continually checking for the arrival of a location service request from a mobile unit in a predetermined region of said service area;

(b) identifying said region upon the arrival of a location request based on either the identity of a primary base station or the identity of the strongest pilot signal strength in said predetermined region reported by the mobile unit to computation apparatus located at a predetermined site;

(c) identifying a set of all pilot signals which are likely to be visible in said predetermined region;

(d) assigning a-priori probability values to a set of specific locations in said predetermined region;

(e) determining if step (d) is an initial iteration step;

(f) in the event that step (d) is the first iteration step computing, for each specific location in said region, the conditional probability of the mobile unit observing the pilot signal strengths at each said specific location as reported in the set of measurements by the mobile unit according to a first conditional probability function;

(g) if step (d) is not the first iteration step, computing the conditional probability of the mobile unit observing the pilot signal strengths at each said specific location as reported in the set of measurements by the mobile unit according to a second probability function;

(h) computing the joint probability of the mobile unit being at each said specific location and is observing the reported pilot measurements;

(i) computing the a-posteriori probability of the mobile unit being at each said specific location in said region given all of the measurements reported up to the present time by normalizing the joint probabilities computed in step (h) so that a summation of unity results when a summation is carried out over all said specific locations in said region;

(j) noting the arrival of any new measurements of signal strengths;

(k) in the event of the arrival of a new set of signal strength measurements, replacing the a-priori probability values of step (d) with the current a-posteriori location probability distribution of step (i) and repeating steps (e)-(i);

(l) in absence of the reception of additional sets of pilot strengths, computing an estimate of the location of the mobile unit using either the mean, the mode, or any other suitable function, of the a-posteriori location probability distribution; and

(m) generating an output indicative of the probability distribution of the location of the mobile unit in said region of the service area.

19. A method according to claim 18, wherein specific locations comprise a set of rectilinear grid points in said region of the service area.

20. A method according to claim 18 wherein the first conditional probability function comprises the expression:

$\mathrm{Pr}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left\{{\underset{\_}{\mathrm{\eta <br><br>}}}^{\left(I\right)}<br><br>\left(x,y\right)\right\}=\underset{\left(i,j\right)\in <br><br>V^{\left(1\right)}}{\prod <br><br>}<br><br>\mathrm{Pr}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left\{Z_{i,j}<br><br>\left(x,y\right)\ge <br><br>T\right\}·<br><br>\underset{\left(i,j\right)\in <br><br>\stackrel{\_}{V^{\left(1\right)}}}{\prod <br><br>}<br><br>\mathrm{Pr}<br><br>\left\{Z_{i,j}<br><br>\left(x,y\right)<<br><br>T\right\}$

where, η

⁽¹⁾ is the first set of thresholded pilot signal strength measurements received by the mobile unit, V⁽¹⁾ is the initial set of pilot signals that were reported as visible by the mobile unit, {overscore (V)}⁽¹⁾ is the set of pilot signals that were reported as not visible by the mobile unit, (x,y) is an arbitrary potential grid-point location for the mobile unit, Z_i,j(x,y) is a random variable representing the reported strength of the pilot signal corresponding to sector j of cell site i, given that the mobile unit is located at grid point (x,y), and T is the pilot signal visibility threshold.

21. A method according to claim 18 wherein the second conditional probability function comprises the expression:

$\mathrm{Pr}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left\{{\underset{\_}{\mathrm{\eta <br><br>}}}^{\left(n+1\right)}<br><br>\left(x,y\right),{\underset{\_}{\mathrm{\eta <br><br>}}}^{\left(n\right)}\right\}=\underset{\left(i,j\right)\in <br><br>K}{\prod <br><br>}<br><br>\mathrm{Pr}<br><br>\left\{{\mathrm{\eta <br><br>}}_{i,j}^{\left(n+1\right)}<br><br>\left(x,y\right),{\mathrm{\eta <br><br>}}_{i,j}^{\left(n\right)}\right\}$

where, n>

1 and denotes the current number of completed iterations, η

⁽ⁿ⁺¹⁾ is the next set of thresholded pilot signal strength measurements received by the mobile unit, η

⁽ⁿ⁾ is the most recent set of thresholded pilot signal strength measurements received by the mobile unit, (x,y) is an arbitrary potential grid-point location for the mobile unit, K is the set of all the pilot signals that are potentially visible to the mobile unit, η

_i,j⁽ⁿ⁾ is the most recent thresholded signal strength measurement from the pilot signal corresponding to sector j of cell site i, and η

_i,j⁽ⁿ⁺¹⁾ is the next thresholded pilot signal strength measurement from the pilot signal corresponding to sector j of cell site i.