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WSN (wireless sensor network) intra-network data fusion method based on kernel density estimation and non-parameter belief propagation

A technology of non-parametric confidence and kernel density estimation, which is applied in the field of data fusion in WSN network based on kernel density estimation and non-parametric confidence propagation, which can solve the problems of sensor node perception vulnerability and measurement inaccuracy.

Inactive Publication Date: 2011-05-11
GUANGDONG UNIV OF PETROCHEMICAL TECH
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  • Application Information

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Problems solved by technology

For the WSN-MTT system, due to various unavoidable factors such as random noise interference, specific physical environment deviation, sensor node perception vulnerability, measurement inaccuracy, network transmission impact, etc., the information collected by the system has many uncertainties

Method used

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  • WSN (wireless sensor network) intra-network data fusion method based on kernel density estimation and non-parameter belief propagation
  • WSN (wireless sensor network) intra-network data fusion method based on kernel density estimation and non-parameter belief propagation
  • WSN (wireless sensor network) intra-network data fusion method based on kernel density estimation and non-parameter belief propagation

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experiment example

[0083] Next, a simulation experiment is performed on the performance of the detection data fusion algorithm of the present invention (KDE-NBP). The simulation is carried out on a PC in the MATLAB7.8 programming environment. The machine configuration is Windows XP professional operating system, Intel(R) Core(TM) 2CPU, T5200@1.60GHz, 1G memory, 80G hard disk, and the main frequency is 1.60GHz . The simulation experiment parameter settings are shown in Table 1.

[0084] Table 1 Simulation environment and parameter settings

[0085]

[0086] There are three moving targets in the monitoring area, and the moving targets are selected from two widely cited classic moving models [59，69，79，85，] (one is a simple linear model, the other is a strong nonlinear model) and a self-designed complex nonlinear model, the three targets cross multiple times within 50 sampling periods, and their state equations are

[0087] T 1 :x 1 =x t-1 +[0.5;0.5x t-1 (2,1) / (1+(x t-1 (2,1)) 2 )]+[0; 2...

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Abstract

The invention discloses a WSN (wireless sensor network) intra-network data fusion method based on kernel density estimation and non-parameter belief propagation, which comprises data acquisition and data fusion. The data acquisition is that monitoring unions which are respectively composed of no less than three sensor nodes for gathering the monitoring data are constructed in a monitoring region, each monitoring union is corresponding provided with a union header node for collecting the monitoring data, the sensor nodes in each monitoring union are respectively used for gathering the monitoring data of an object entering the monitoring region; and the data fusion is that the gathered monitoring data are subjected to KDE (kool desktop environment) processing by the sensor nodes in the monitoring unions respectively, the processed data are transmitted and collected to the union header nodes through NBP (name bind protocol) processing, the collected data are subjected to gauss mixing by the union header nodes, the data after gauss mixing are subjected to Gibbs sampling fusion, and the fused result is acted as a characteristic of the monitoring data. The accuracy of the monitoring data can be improved under a noisy or an uncertain environment, and the accurate fusion characterization of the monitoring data of the multi-node unions can be realized.

Description

technical field [0001] The invention relates to the field of wireless sensor networks, in particular to a data fusion method in a WSN network based on kernel density estimation and non-parameter belief propagation. Background technique [0002] A wireless sensor network (WSN) is a distributed network formed by a large number of randomly distributed low-cost, low-power miniature wireless sensor nodes through self-organization. It can sense, collect and monitor network coverage in real time. The information of the monitored objects in the region has broad application prospects in both military and civilian fields. Target positioning and tracking is an important application of wireless sensor network (WSN). Accurate and effective monitoring data is the basis for precise target positioning and tracking. Data fusion is the key technology of WSN target positioning and tracking. Detection-level data fusion in the WSN network means that in the WSN multi-sensor distributed detection...

Claims

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Application Information

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IPC IPC(8): H04W24/00H04W84/18H04L12/24
Inventor 刘美徐小玲贺婷
Owner GUANGDONG UNIV OF PETROCHEMICAL TECH
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