Optimized Wireless Sensor Network Positioning Technology

BIAN Guo-long; HUANG Hai-song; GE Zhi-zheng; LIU Pei-chen

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Optimized Wireless Sensor Network Positioning Technology

更新时间：2026-09-04

- Optimized Wireless Sensor Network Positioning Technology
- Vol. 34, Issue 4, Pages: 49-54(2017)
- 作者机构：
  
  1. 贵州大学现代制造技术教育部重点实验室机构,贵州,贵阳,550025
  2. 中国海洋大学管理学院,山东,青岛,266100
  3. 青岛大学机构机电工程学院,山东,青岛,266071
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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卞国龙, 黄海松, 葛至峥, 刘培晨. 无线传感器网络定位技术的优化处理[J]. 微电子学与计算机, 2017, 34(4): 49-54.

BIAN Guo-long, HUANG Hai-song, GE Zhi-zheng, et al. Optimized Wireless Sensor Network Positioning Technology[J]. 2017, 34(4): 49-54.
卞国龙, 黄海松, 葛至峥, 刘培晨. 无线传感器网络定位技术的优化处理[J]. 微电子学与计算机, 2017, 34(4): 49-54. DOI：

BIAN Guo-long, HUANG Hai-song, GE Zhi-zheng, et al. Optimized Wireless Sensor Network Positioning Technology[J]. 2017, 34(4): 49-54. DOI：

摘要

无线网络节点定位技术给人员提供了安全保障

在研究传统无线定位的基础上

同时针对现有的基于神经网络定位算法的精度不高等问题

提出了一种新型的基于PSO-BP网络的定位算法.为了提高系统精度首先采用卡尔曼算法进行滤波处理

然后通过一种PSO-BP算法对BP网络初始权值和阈值进行优化

并对比现有的RSSI算法

分析不同算法的性能.BP神经网络权值的修正依赖于非线性梯度值

易形成局部极小值

同时学习次数较多.实验证明

改进的PSO-BP算法在误差反传调整权值的基础上

采用改进的PSO算法的学习机制修正权值

增加了BP算法收敛速度和全局收敛性

提高了BP网络的学习能力.

Abstract

On the basis of research on traditional wireless location

while not high for the existing positioning accuracy of neural network algorithm

we propose a new type of location-based algorithm PSO-BP network. In order to improve the accuracy of the system first Kalman filtering algorithm

and then by means of a PSO-BP algorithm BP initial weights and thresholds to optimize and compare existing RSSI algorithm to analyze the performance of different algorithms. Fixed BP neural network weights depends on the non-linear gradient value

easy to form a local minimum

while learning more frequently. Experimental results show that the improved PSO-BP algorithm based on the error back propagation adjustment weights on the use of learning mechanism correction weights improved PSO algorithm

an increase of BP algorithm convergence speed and global convergence

improved BP network learning ability.

关键词

Keywords

references

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叶苗, 程小辉, 邓昀. 基于μ+λ进化计算的传感器网络定位算法[J]. 微电子学与计算机, 2013, 30(2): 9-12. DOI: 9

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