基于半正定松弛的到达频率差目标定位方法

毛晓婷; 吴晓平

doi:10.11959/j.issn.1000-0801.2024076

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基于半正定松弛的到达频率差目标定位方法

研究与开发 | 更新时间：2024-06-05

- 基于半正定松弛的到达频率差目标定位方法
- Target localization method based on semi-definite relaxation with frequency difference of arrival
- 电信科学 2024年40卷第3期页码：104-115
- 作者机构：
  
  湖州师范学院信息工程学院，浙江湖州 313000
- 作者简介：
  
  [ "毛晓婷（2000- ），女，湖州师范学院信息工程学院硕士生，主要研究方向为无线传感器网络、最优化理论和MIMO雷达定位。" ]
  [ "吴晓平（1977- ），男，博士，湖州师范学院信息工程学院副教授、硕士生导师，主要研究方向为物联网、通信与信号处理、普适移动计算等。" ]
- 基金信息：
  
  浙江省自然科学基金资助项目(LY22F010016)
- DOI：10.11959/j.issn.1000-0801.2024076
  中图分类号： TN929.5
- 收稿日期：2023-12-12，
  
  修回日期：2024-01-31，
  
  纸质出版日期：2024-03-20
- 稿件说明：
移动端阅览
毛晓婷,吴晓平.基于半正定松弛的到达频率差目标定位方法[J].电信科学,2024,40(03):104-115.

MAO Xiaoting,WU Xiaoping.Target localization method based on semi-definite relaxation with frequency difference of arrival[J].Telecommunications Science,2024,40(03):104-115.
毛晓婷,吴晓平.基于半正定松弛的到达频率差目标定位方法[J].电信科学,2024,40(03):104-115. DOI： 10.11959/j.issn.1000-0801.2024076.

MAO Xiaoting,WU Xiaoping.Target localization method based on semi-definite relaxation with frequency difference of arrival[J].Telecommunications Science,2024,40(03):104-115. DOI： 10.11959/j.issn.1000-0801.2024076.

摘要

采用频率测量实现目标定位具有成本低、可靠性高的特点，仅利用到达频率差（frequency difference of arrival，FDOA）测量，提出了一种静态目标位置的精确定位方法。针对所建立的频率测量方程的高度非线性这一问题，通过引入辅助变量，将其转化为矩阵形式的伪线性方程；然后利用半正定松弛（semi-definite relaxation，SDR）方法将非凸的加权最小二乘（weighted least square，WLS）问题松弛为半正定规划（semi-definite programming，SDP）问题，从而进一步精确估计未知变量；最后对所提出方法的均方根误差（root-mean-square error，RMSE）进行了分析，以验证其性能。仿真结果表明，在较低的高斯噪声水平下，所采用的半正定松弛方法的性能能够达到克拉美罗下界（Cramer-Rao lower bound，CRLB），且该算法对几何形状具有较高的鲁棒性；此外，在使用较少数量的传感器时，其RMSE性能要优于两阶段加权最小二乘（two-stage weighted least square，TSWLS）法。

Abstract

The use of frequency measurements to achieve target positioning is characterized by low cost and high reliability. Using only frequency difference of arrival (FDOA) measurements

a precise localization method for static target position was proposed. To address the highly nonlinear of the established frequency measurement equation

it was transformed into a pseudo-linear equation in matrix form by introducing auxiliary variables. Then the non-convex weighted least squares (WLS) problem was relaxed into a semi-definite programming (SDP) problem by using the semi-definite relaxation (SDR) method

so as to further accurately estimate the position of unknown variables. Finally

the root mean square error (RMSE) of the proposed method was analyzed to verify its performance. The simulation results show that the performance of the adopted semi-definite relaxation method is able to reach the Cramer-Rao lower bound (CRLB) at lower Gaussian noise levels and the algorithm is highly robust to geometry. In addition

its RMSE performance is better than that of the two-stage weighted least square (TSWLS) method for a smaller number of sensors.

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references

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