基于凸组合的联合RSS-AOA定位方法

王奇; 陈辉; 齐思佳

doi:10.11959/j.issn.1000-0801.2025147

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基于凸组合的联合RSS-AOA定位方法

研究与开发 | 更新时间：2025-11-16

- 基于凸组合的联合RSS-AOA定位方法
- A combined RSS-AOA localization method based on convex combination
- 电信科学 2025年41卷第9期页码：165-176
- 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，西安石油大学电子工程学院讲师、硕士生导师，主要研究方向为无线信号定位、姿态测量等。 " ]
  [ "陈辉（2001- ），男，西安石油大学电子工程学院硕士生，主要研究方向为无线传感器网络定位方法。" ]
  [ "齐思佳（2000- ），女，西安石油大学电子工程学院硕士生，主要研究方向为无线传感器网络定位方法。" ]
- 基金信息：
  
  国家重点研发计划项目(2023YFC2810902);陕西省自然科学基础研究计划项目(2025JC-YBMS-775);陕西省教育厅科研计划项目(23JP128);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项目(2022-ZNDXZJ);西安石油大学科研创新团队项目(2022KYCXTD01)
- DOI：10.11959/j.issn.1000-0801.2025147
  中图分类号： TP393
- 收稿：2025-03-06，
  
  修回：2025-05-26，
  
  纸质出版：2025-09-20
- 稿件说明：
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王奇,陈辉,齐思佳.基于凸组合的联合RSS-AOA定位方法[J].电信科学,2025,41(09):165-176.

WANG Qi,CHEN Hui,QI Sijia.A combined RSS-AOA localization method based on convex combination[J].Telecommunications Science,2025,41(09):165-176.
王奇,陈辉,齐思佳.基于凸组合的联合RSS-AOA定位方法[J].电信科学,2025,41(09):165-176. DOI： 10.11959/j.issn.1000-0801.2025147.

WANG Qi,CHEN Hui,QI Sijia.A combined RSS-AOA localization method based on convex combination[J].Telecommunications Science,2025,41(09):165-176. DOI： 10.11959/j.issn.1000-0801.2025147.

摘要

针对无线传感器网络中的联合接收信号强度（received signal strength，RSS）和到达角度（angle of arrival，AOA）定位问题，提出一种全新的凸组合定位方法。该方法使用某些特定点（称为虚拟点）的凸组合来估计目标点的位置。提出了基于二次约束二次规划（quadratically constrained quadratic programming，QCQP）和半正定规划（semidefinite programming，SDP）两种虚拟点构造方法。在此基础上，将目标定位的极大似然（maximum likelihood，ML）估计问题进行凸化，得到组合系数，进一步得到目标定位结果。数值实验表明，所提出的凸组合方法比文献中的几种定位方法具有更高的精度，特别是相对于线性最小二乘（linear least squares，LLS）方法，精度最高提升约40%。此外，其定位结果可以作为ML估计方法的初始化，进一步提升定位性能。

Abstract

Aiming at the problem of the combined received signal strength (RSS)-angle of arrival (AOA) localization in wireless sensor networks

a new convex combination positioning method was proposed. This method estimated the target’s location by using a convex combination of several specific points

called virtual points. Two methods were provided to construct the virtual points

which employ quadratically constrained quadratic programming (QCQP) and semidefinite programming (SDP). On basis of this

the nonconvex problems with respect to the maximum likelihood (ML) estimation of the target location were converted to be convex

and the combination coefficients were obtained. Then the target was localized. Numerical examples demonstrate the better accuracy of the proposed method than several existing methods in the literature. Especially

the proposed method outperforms the linear least squares method by approximately 40%. Besides

its localization result can serve as a good initialization for the ML method to refine the localization performance.

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references

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