Sequence Correlation Optimized Monte Carlo Localization

LIU Zhi-hua; XI Zhen-zhen; ZHANG Shuang; CHEN Jia-xing

doi:10.3969/j.issn.0372-2112.2015.10.033

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Sequence Correlation Optimized Monte Carlo Localization

更新时间：2025-07-16

- Sequence Correlation Optimized Monte Carlo Localization
- Acta Electronica Sinica Vol. 43, Issue 10, Pages: 2110-2116(2015)
- 作者机构：
  
  1. 河北师范大学信息技术学院,河北,石家庄,050024
  2. 河北师范大学数学与信息科学学院,河北,石家庄,050024
  3. 河北师范大学信息技术学院,河北,石家庄,050024
  4. 河北师范大学数学与信息科学学院,河北,石家庄,050024
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2015.10.033
  CLC： TP393
- Published：2015
- 稿件说明：
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LIU Zhi-hua, XI Zhen-zhen, ZHANG Shuang, et al. Sequence Correlation Optimized Monte Carlo Localization[J]. Acta Electronica Sinica, 2015, 43(10): 2110-2116.
DOI：

LIU Zhi-hua, XI Zhen-zhen, ZHANG Shuang, et al. Sequence Correlation Optimized Monte Carlo Localization[J]. Acta Electronica Sinica, 2015, 43(10): 2110-2116. DOI： 10.3969/j.issn.0372-2112.2015.10.033.

摘要

蒙特卡罗定位算法在无线传感器网络移动节点定位中具有重要的作用.为了提高定位精度

提出了基于序列相关值的蒙特卡罗优化算法SCMCL.以接收的RSSI信号值对移动节点初定位

并将其作为新的采样中心

SCMCL可以优化蒙特卡罗系列算法的采样区域

同时将移动节点收到的锚节点信号值存储为目标序列

通过比较样本序列和目标序列间的相关值来过滤样本点

并将相关值作为加权标准来计算移动节点的坐标.仿真验证

SCMCL算法在相同的锚节点密度和最大速度下和同类算法相比

定位误差均减少了10%左右.

Abstract

Monte Carlo Localization(MCL) has a decisive role for the mobile nodes'localization in Wireless Sensor Networks(WSN).In order to improve the positioning accuracy

an improved algorithm called Sequence Correlation Optimized Monte Carlo Localization(SCMCL) is proposed.Adopting the mobile node's location based on the received signal strength indicator(RSSI)as the new sampling center

SCMCL can optimize the sampling area of MCL and etc.The signal values are stored as a target sequence

and by comparing the correlation values between samples' sequences and the target sequence

samples can be filtered out.Also the correlation values are used as the weighted standards to calculate coordinates of the mobile nodes.Extensive simulation results confirm that the new localization approach outperforms MCL and etc.The SCMCL algorithm reduces the localization error by about 10% under the same density of beacon nodes and the maximum speed of mobile nodes.

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