METHOD AND SYSTEM FOR LOCATING AN OBJECT

• US 20160195401A1
• Filed: 08/12/2014
• Published: 07/07/2016
• Est. Priority Date: 08/12/2013
• Status: Abandoned Application

• Current Assignee: Chigoo Interactive Technology Co., Ltd.
• Original Assignee: Chigoo Interactive Technology Co., Ltd.
• Inventors: JIA, Shangjie, LU, Xiaohuan, WANG, Xinheng
• Application Number:
  US14/911,865
• Publication Number:
  US 20160195401A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01S 5/0278 involving statistical or probabilistic considerations G01S5/0252, G01S5/0294 take precedence
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04

Abstract

A method and system for locating an object are disclosed. The method comprises generating a plurality of sampling points in an activity area map for an object; obtaining a conditional probability density of the object'"'"'s locating at the plurality of sampling points according to an initial electromagnetic field intensity of the plurality of sampling points and a current electromagnetic field intensity of the object to be located; conducting resampling and updating for the sampling points according to the conditional probability density; and determining a position of the object to be located based on coordinate values of sampling points after the updating. The present invention can improve the accuracy of the locating or tracking on an indoor object.

Claims

1. A method for locating an object, comprising:

generating a plurality of sampling points in an activity area map for an object to be located;

obtaining a conditional probability density of the object'"'"'s locating at the plurality of sampling points according to an initial electromagnetic field intensity of the plurality of sampling points and a current electromagnetic field intensity of the object to be located;

conducting resampling and updating for the sampling points according to the conditional probability density; and

determining a position of the object to be located based on coordinate values of sampling points after the updating.

2. The method of claim 1, wherein said obtaining a conditional probability density of an object'"'"'s locating at the plurality of sampling points comprises:

updating initial coordinates of the plurality of sampling points according to a Monte Carlo action model, and

obtaining a conditional probability density of the object'"'"'s locating at the plurality of sample points by means of Monte Carlo measuring model according to the initial electromagnetic field intensity of the plurality of sampling points after the updating and the current electromagnetic field intensity.

3. The method of claim 1, wherein in the activity area map of the object, the plurality of sampling points are generated with random function.

4. The method of claim 1, wherein said generating a plurality of sampling points in an activity area map of the object comprises:

generating the plurality of sampling points in the activity area map of the object according to information of historical sampling points by means of BSAS algorithm.

5. The method of claim 4, wherein said generating the plurality of sampling points according to information of historical sampling points by means of BSAS algorithm comprises:

establishing an initial cluster m=1, Cm={Xm} according to a set threshold θ

;

judging whether an Euler distance d(x_(i),C_k)=min<sub>1≦

j≦

m</sub>d(x_(i),C_j) between said historical sampling point and said initial cluster is less than the threshold θ

, and if yes then joining the multiple historical sampling points into the initial cluster, or if not then establishing a new cluster m=m+1, Cm={X (i)};

extracting the sampling points of the cluster according to a set number of cluster particles if the number of sampling points of the cluster is greater than the set number of cluster particles; and

determining the sampling points of the cluster as a plurality of sampling points.

6. The method of claim 2, wherein said updating initial coordinates of the plurality of sampling points based on the Monte Carlo action model comprises:

updating two-dimensional initial coordinates of the plurality of sample points by using the Monte Carlo action model.

7. The method of claim 2, wherein said obtaining a conditional probability density of the object'"'"'s locating at the plurality of sample points comprises:

for each sampling point, adding a current magnetic field intensity value z_t into the Monte Carlo measuring model m_t⁽ⁿ⁾=m_t-1⁽ⁿ⁾p(z_t|(x_t,y_t)⁽ⁿ⁾) to obtain a conditional probability value m_tⁱ=p(z_t|s′

_tⁱ) of each sampling point.

8. The method of claim 1, wherein said conducting resampling and updating for the sampling points according to the conditional probability density comprise:

conducting resampling and updating of the sampling points by using a random sampling function to obtain the current sampling point according to the set times of sampling and the conditional probability density of the sampling point.

9. The method of claim 1, wherein said determining a position of the object to be located based on coordinate values of the updated sampling points comprises:

determining a position of the object to be located in accordance with the coordinate values, or

determining a position of the object to be located in accordance with two-dimensional coordinate values of the updated sampling point and the corresponding conditional probability density m with formula

10. A system for locating an object, comprising:

a sampling point generating module configured to generate a plurality of sampling points in an activity area map of an object to be located,

a conditional probability density obtaining module configured to obtain a conditional probability density of the object'"'"'s locating at the plurality of sampling points according to initial electromagnetic field intensity of the plurality of sampling points and a current electromagnetic field intensity of the object;

a resampling module configured to conduct resampling and updating for the sampling points according to the conditional probability density, and

a locating module configured to determine a position of the object based on coordinates of the updated sampling points.

11. The system of claim 10, wherein the conditional probability density obtaining module comprises:

a Monte Carlo action model unit configured to update initial coordinates of the plurality of sampling points by means of a Monte Carlo action model, and

a Monte Carlo measuring model unit configured to obtain the conditional probability density of the object'"'"'s locating at the plurality of sample points by means of Monte Carlo measuring model according to initial electromagnetic field intensity of the plurality of updated sampling points and a current electromagnetic field intensity of the object.

12. The system of claim 10, wherein the sampling point generating module comprises:

a sampling point randomly generating unit configured to generate the plurality of sampling points with a random function in the activity area map of the object, and

a historical sampling point updating unit configured to generate the plurality of sampling points with BSAS algorithm according to information of the historical sampling points in the activity area map of the object.