LOCATION ESTIMATION METHOD

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.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be more fully understood by reading the subsequent detailed description and examples with references made to the accompanying drawings, wherein:

FIG. 1 shows a Time-of-Arrival (TOA) based location estimation with three base stations;

FIG. 2 shows an embodiment of virtual BS allocation,

FIG. 3 a shows a GDOP contour associated with the three BSs of FIG. 1;

FIG. 3 b shows an altered GDOP contour associated with the original BS and an additional virtual BS; and

FIG. 4 is a flowchart of the location estimation method.

Claims

1. A location estimation method for locating a mobile station (MS) by a plurality of base stations (BS), comprising: analyzing a geometric distribution of the BSs to provide a list of conditional equations,allocating virtual BSs having virtual distances to the MS to provide constraint equations, andestimating 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; andgenerating 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 a 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; andallocating 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; andobtaining 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.