Method of sensor network localization through reconstruction of radiation pattern

• US 8,416,120 B2
• Filed: 02/17/2009
• Issued: 04/09/2013
• Est. Priority Date: 10/07/2008
• Status: Active Grant

• Current Assignee: Intellectual Discovery Co., Ltd.
• Original Assignee: Sungkyunkwan University Foundation For Corporate Collaboration (SungKyunKwan University)
• Inventors: Kim, Jong Tae, Lim, Chae Seok
• Primary Examiner(s): GREGORY, BERNARR E
• Application Number:
  US12/372,044
• Publication Number:
  US 20100085242A1
• Time in Patent Office: 1,512 Days
• Field of Search: 342/21, 342/22, 342/27, 342/28, 342/59, 342/118, 342134-145, 342/175, 342/195, 342450-465, 342/165, 342169-174, 342/126, 340/531, 340/539.1, 34053922-53926, 340/870.01, 340/870.02, 340/870.03, 340/870.11, 340/870.18, 367/118, 367/129, 701/1, 701/117, 709/223, 702/1, 702/33, 702/35, 702/36, 455/403, 455/422.1, 455/456.1, 455/456.3, 370/254, 370/310, 370/328, 370/338
• US Class Current: 342/59
• CPC Class Codes: G01S 5/0289 of multiple transceivers, e.g. in ad hoc networks

Abstract

Disclosed herein is a method of sensor network localization through reconstruction of a radiation pattern with a characteristic value of an antenna depending on orientation thereof. The method can minimize errors using an antenna characteristic value and a signal strength depending on the orientation. In addition, the method can minimize errors using an artificial neural network to characterize a distorted radiation pattern of an antenna and using it for the localization of a triangulation method. Furthermore, the method can increases the localization rate even in a passive localization method by characterizing an asymmetric antenna radiation pattern and constructing the antenna characteristic through an artificial neural network.

Claims

1. A method of localization through reconstruction of a radiation pattern, the method comprising:

providing a sensor network that includes a plurality of sensor nodes and a gateway, wherein each of the sensor nodes and the gateway includes an antenna for communicating with each other;

measuring a radiation characteristic value of each of the antennae which depends on transmission and reception sensitivity of a selected antenna varying with the orientation of the selected antenna in the sensor network;

measuring radiation patterns of a first sensor node and a second sensor node of the plurality of sensor nodes; and

measuring a position of a third sensor node based on the radiation characteristic value of an antenna corresponding to the third sensor node and the radiation patterns of the first and second sensor nodes.

2. The method according to claim 1, wherein measuring the radiation characteristic value of each of the antennae comprises:

measuring an angular radiation characteristic value at a selected angle from the gateway; and

deriving the radiation characteristic value of each of the antennae over a range of angles using learning and reasoning functionalities of an artificial neural network based on the angular radiation characteristic value.

3. The method according to claim 2, wherein measuring the angular radiation characteristic value comprises:

arranging the plurality of sensor nodes in a radial pattern at a constant distance from the gateway;

comparing a signal strength transmitted from the gateway with an actual signal strength received by each of the plurality of sensor nodes; and

comparing a signal strength transmitted from each of the plurality of sensor nodes with an actual signal strength received by the gateway.

4. The method according to claim 3, further comprising:

converting the signal strength transmitted from each of the plurality of sensor nodes into a sensor node transmitted signal strength in dBm;

converting the actual signal strength received by each of the plurality of sensor nodes into a sensor node received signal strength in dBm; and

determining the angular radiation characteristic value β

using the equation;

5. The method according to claim 2, wherein deriving the radiation characteristic value of each of the antennae over a range of angles using learning and reasoning functionalities of an artificial neural network based on the angular radiation characteristic value comprises:

deriving the radiation characteristic value of the selected antenna over the range of angles using an artificial neural network comprising an input node, a hidden node and an output node, the artificial neural network receiving the angle of the selected antenna at the input node and returning the radiation characteristic value of the selected antenna according to the angle of the selected antenna at the output node.

6. The method according to claim 1, wherein measuring the radiation patterns of the first and second sensor nodes comprises:

measuring relative positions and angles of the first and second sensor nodes and a distance between the first and second sensor nodes;

generating a first data pair by comparing a signal strength transmitted from the first sensor node with an actual signal strength received by the second sensor node; and

generating a second data pair by comparing a signal strength transmitted from the second sensor node with an actual signal strength received by the first sensor node.

7. The method according to claim 6, wherein measuring the position of the third sensor node comprises:

deriving an expected position of the third sensor node with respect to the first and second sensor nodes from the radiation characteristic value of the antenna corresponding to the third sensor node, the first data pair, and the second data pair; and

determining an actual position of the third sensor node using an antenna radiation characteristic value according to the expected position of the third sensor node, an artificial neural network, and triangulation.

8. The method according to claim 7, wherein determining the actual position of the third sensor node comprises:

forming a first triangle from the first sensor node, the second sensor node, and an expected position of the third sensor node according to the first sensor node;

forming a second triangle from the first sensor node, the second sensor node, and an expected position of the third sensor node according to the second sensor node;

deriving a first antenna characteristic value of the third sensor node with respect to the first sensor node using triangulation of the first triangle and the second triangle;

deriving a second antenna characteristic value of the third sensor node with respect to the second sensor node using triangulation of the first triangle and the second triangle; and

determining the actual position of the third sensor node that satisfies an artificial neural network module defined by

FindBetaFromAngle(θ

_init)==β

_n-1

FindBetaFromAngle(θ

_init+θ

_n)==β

_n-2,

wherein β

_n-1 indicates the first antenna characteristic value of the third sensor node with respect to the first sensor node,

wherein β

_n-2 indicates the second antenna characteristic value of the third sensor node with respect to the second sensor node,

wherein θ

_n indicates an angle formed by (a) a side between the third sensor node and the expected position of the third sensor node according to the second sensor node and (b) a side between the third sensor node and an antenna direction of the third sensor node, and

wherein θ

_init indicates an angle formed by both (a) a side between the third sensor node and the expected position of the third sensor node according to the first sensor node and (b) the side between the third sensor node and an antenna direction of the third sensor node.

9. The method according to claim 1, wherein the third sensor node is located within a signal transmission range of the first and second sensor nodes.