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Radio frequency tomography method base on background learning

A technology of radio frequency tomography and background learning, applied in the field of wireless sensors, can solve problems such as difficult to model RSS measurement values

Inactive Publication Date: 2013-09-04
BEIJING INST OF SPACECRAFT SYST ENG +1
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  • Claims
  • Application Information

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Problems solved by technology

[0012] To sum up, radio frequency tomography is a kind of method for RSS-based multi-target passive positioning and tracking, but the existing methods are difficult to analyze the changes of RSS measurement value caused by moving objects in multipath environment. modeling

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  • Radio frequency tomography method base on background learning
  • Radio frequency tomography method base on background learning
  • Radio frequency tomography method base on background learning

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Embodiment Construction

[0058] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0059] A radio frequency tomography method based on background learning, comprising the following steps:

[0060] Step 1. According to the received signal strength of the wireless sensor network, use the mixed Gaussian background learning algorithm or the kernel density estimation (KDE) background learning algorithm to establish the distribution model of the received signal strength value (RSS) of each link, and determine whether each link is affected;

[0061] This embodiment takes figure 1 The given experimental scene is taken as an example. The experimental scene is an outdoor scene covered by a wireless sensor network, and the information is closely related to the location of the target. an area Covered by a wireless sensor network, N sensor nodes communicate with each other. Each node is placed at a certain position to form a 2-dimensional Cartesian...

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Abstract

The invention relates to a radio frequency tomography method base on background learning. The radio frequency tomography method base on the background learning comprises the following steps: 1, establishing the distribution model of the received signal strength value of each link and judging whether each link is influenced according to the received signal strength of a wireless sensor network by using a mixture Gaussian background learning algorithm or a kernel density estimation background learning algorithm; 2, carrying out image reconstruction according to the distribution model of the received signal strength value of each link by using Tikhonov regularization. The mixture Gaussian background learning algorithm or the kernel density estimation background learning algorithm is applied to radio frequency tomography to estimate the distribution of the RSS measured values of each link, and a multiple target detecting and tracing function is achieved. The radio frequency tomography method base on the background learning has the advantages that higher accuracy and effectiveness can be obtained in multiple targets and time varying environments, and an offline training process is not needed.

Description

technical field [0001] The invention belongs to the technical field of wireless sensors, in particular to a radio frequency tomography method based on background learning. Background technique [0002] In recent years, the development of information technology and network technology has brought huge and profound changes to various fields of human society and national economy. The information network represented by the Internet has a greater and greater impact on people's lifestyles, and will continue to develop and increase its influence in various fields in the future. Wireless Sensor Networks (WirelessSensorNetworks, WSN) is a new type of network technology that integrates sensor technology, micro-electromechanical system technology, wireless communication technology and distributed information processing technology. The information of the object is sensed, collected and processed in real time, and the processed information is transmitted to the interested network end use...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W64/00H04W84/18
Inventor 刘崇华杨波张弓黎杨薛建飞刘航门爱东袁媛胡雪麟
Owner BEIJING INST OF SPACECRAFT SYST ENG
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