面向不确定量测的鲁棒RSS定位方法

王奇; 胡光; 李飞; 陈辉

doi:10.11959/j.issn.1000-0801.2025029

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面向不确定量测的鲁棒RSS定位方法

研究与开发 | 更新时间：2025-03-11

- 面向不确定量测的鲁棒RSS定位方法
- A robust RSS-based localization method with uncertain measurement
- 电信科学 2025年41卷第2期页码：111-119
- 作者机构：
  
  1.西安石油大学电子工程学院，陕西西安 710065
  2.西安市油气及新能源开发装备智能化重点实验室，陕西西安 710065
  3.中海油集团测井与定向钻井重点实验室定向钻井分室，陕西西安 710065
- 作者简介：
  
  [ "王奇（1988- ），男，博士，西安石油大学讲师、硕士生导师，主要研究方向为无线信号定位、姿态测量等。" ]
  [ "胡光（1997- ），女，西安石油大学硕士生，主要研究方向为无线传感器网络定位方法。" ]
  [ "李飞（1977- ），男，博士，西安石油大学教授、博士生导师，主要研究方向为姿态测量、井场设备定位等。" ]
  [ "陈辉（2001- ），男，西安石油大学硕士生，主要研究方向为无线传感器网络定位方法。" ]
- 基金信息：
  
  国家自然科学基金企业创新发展联合基金重点项目(U20B2029);国家重点研发计划项目(2023YFC2810902);陕西省教育厅科研计划项目(23JP128);陕西省自然科学基金青年项目(2023-JC-QN-0405);陕西秦创原“科学家+工程师”团队项目(2022KXJ-125);陕西高校青年创新团队(2022-ZNDXZJ);西安石油大学科研创新团队项目(2022KYCXTD01)
- DOI：10.11959/j.issn.1000-0801.2025029
  中图分类号： TP393
- 收稿：2024-10-29，
  
  修回：2025-01-16，
  
  纸质出版：2025-02-20
- 稿件说明：
移动端阅览
王奇,胡光,李飞等.面向不确定量测的鲁棒RSS定位方法[J].电信科学,2025,41(02):111-119.

WANG Qi,HU Guang,LI Fei,et al.A robust RSS-based localization method with uncertain measurement[J].Telecommunications Science,2025,41(02):111-119.
王奇,胡光,李飞等.面向不确定量测的鲁棒RSS定位方法[J].电信科学,2025,41(02):111-119. DOI： 10.11959/j.issn.1000-0801.2025029.

WANG Qi,HU Guang,LI Fei,et al.A robust RSS-based localization method with uncertain measurement[J].Telecommunications Science,2025,41(02):111-119. DOI： 10.11959/j.issn.1000-0801.2025029.

摘要

在基于接收信号强度（received signal strength，RSS）的定位中，传感器量测的系统偏差及锚节点位置的不确定性会对定位结果造成严重影响。对此，提出一种面向不确定量测的鲁棒定位方法。首先，针对传感器量测有偏差及锚节点位置不确定的定位问题，建立相应的量测模型；其次，基于经典的极大似然估计准则建立关于目标位置的估计问题；最后，对所建立的非凸位置估计问题，采用合理的近似、松弛数学手段，将其转化为凸的半正定规划问题，从而保证得到全局最优解。仿真实验表明，在不同定位场景和条件下，所提方法的定位精度相比文献中的几种定位方法均有明显的优势，最高可提升约50%，证明其能有效降低量测不确定性对定位结果的不利影响，具有良好的鲁棒性。

Abstract

In received signal strength (RSS)-based localization

systematic bias in sensor measurements and anchor position uncertainty will heavily affect the localization result. To deal with this problem

a robust localization method with uncertain measurements was proposed. Firstly

the measurement model for the localization problem was formulated

where the sensor measurements contained biases and the anchor positions were uncertain. Secondly

the estimation problem of target location was provided using the criterion of maximum likelihood. Finally

proper approximation and relaxation were applied to convert the nonconvex estimation problem of target location to convex semidefinite programming (SDP) problem such that the global optimum could be guaranteed. Simulation results show that the proposed method outperforms some existing methods in the literature for different scenarios and conditions in terms of localization accuracy

of which the improvement achieves up to 50%. It verifies the robustness of the proposed method

and shows that it can efficiently reduce the negative effect of uncertain measurements on localization accuracy.

关键词

Keywords

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