Location estimation method

• US 7,957,750 B2
• Filed: 03/22/2010
• Issued: 06/07/2011
• Est. Priority Date: 01/06/2006
• Status: Active Grant

• Current Assignee: MediaTek, Inc.
• Original Assignee: MediaTek, Inc.
• Inventors: Feng, Kai-Ten, Chen, Chao-Lin
• Primary Examiner(s): Chang; Kent
• Assistant Examiner(s): Eisner; Ronald
• Application Number:
  US12/728,375
• Publication Number:
  US 20100173648A1
• Time in Patent Office: 442 Days
• Field of Search: 370/331, 370/332, 4554561-457
• US Class Current: 455/456.1
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

A location estimation method is provided. The method locates coordinates of a mobile station (MS) by referencing a plurality of base stations (BS). A geometric distribution of the BS is analyzed to provide a list of conditional equations. A virtual BS is allocated, having a virtual distance to the MS to provide a constraint equation. The MS location is derived from the conditional equations and the constraint equation.

Claims

1. A location estimation method for locating a mobile station (MS) by a plurality of base stations (BSs), comprising:

analyzing a geometric distribution of the BSs to provide a list of conditional equations;

rendering a geometric dilution of precision (GDOP) contour based on the analysis of the geometric distribution;

allocating at least one virtual BS to a position according to the GDOPcontour, said virtual BS having a virtual distance to the MS to provide a constraint equation; and

estimating the MS location based on the conditional equations and the constraint equations.

2. The location estimation method as claimed in claim 1, wherein analysis of the geometric distribution comprises:

transmitting coordinates of each BS to the MS;

estimating time-of arrival (TOA) of signals transmitted to or from the BSs to obtain measured distances correspondingly;

measuring noise level of each transmission to calculate standard deviations of the measured distances; and

generating the conditional equations based on the measured distances and the standard deviations.

3. The location estimation method as claimed in claim 2, further comprising calculating an initial estimate of the MS location based on the standard deviations and coordinates of the BSs.

4. The location estimation method as claimed in claim 3, further comprising determining the virtual distances based on the initial estimate of the MS location, the coordinates of the BSs and a plurality of virtual coefficients each corresponding to a BS.

5. The location estimation method as claimed in claim 4, further comprising rendering the GDOP contour based on the analysis of the geometric distribution, statistically presenting measurement error distribution of the BSs.

6. The location estimation method as claimed in claim 5, wherein allocation of the virtual BS comprises:

observing peak values distributed in the GDOP contour; and

allocating the virtual BSs to the positions where at least one peak value is causally smoothed away.

7. The location estimation method as claimed in claim 6, wherein the positions of the virtual BSs are determined by adjusting the virtual coefficients.

8. The location estimation method as claimed in claim 6, wherein the constraint equations is formed by the coordinates of the virtual BS and the virtual distances.

9. The location estimation method as claimed in claim 6, wherein estimation of the MS location comprises substituting the conditional equations and the constraint equations into a two-step least square algorithm.

10. The location estimation method as claimed in claim 9, wherein estimation of the MS location further comprises:

for a first step of the two-step least square algorithm, providing a variable equivalent to the square sum of the MS location;

β

=x²+y², where (x,y) are the coordinates of the MS location, and β

is the variable;

generating a first linear vector from the variable and the coordinates of MS location;

performing a maximum likelihood search using the first linear vector, the conditional equation and the constraint equations; and

obtaining a preliminary solution comprising a preliminary coordinate of the MS location.

11. The location estimation method as claimed in claim 10, wherein estimation of the MS location comprises:

for a second step of the two-step least square algorithm, providing a second linear vector from the preliminary coordinate of the MS location;

performing the maximum likelihood search using the second vector, such that a final solution is obtained.