Passive source localization using importance sampling based on TOA and FOA measurements

Rui-rui LIU; Yun-long WANG; Jie-xin YIN; Ding WANG; Ying WU

doi:10.1631/FITEE.1601657

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Passive source localization using importance sampling based on TOA and FOA measurements

Regular Papers | Updated：2022-05-19

- Passive source localization using importance sampling based on TOA and FOA measurements
- 基于重要性采样的TOA与FOA无源定位算法
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 8, Pages: 1167-1179(2017)
- Affiliations：
  
  National Digital Switching System Engineering & Technology Research Center, Zhengzhou 450001, China
- Author bio：
  
  Yun-long WANG, E-mail: chriswayulo@sina.com
- Funds：
- DOI：10.1631/FITEE.1601657
  CLC： TN91
- Received：25 October 2016，
  
  Revised：2017-;8-18，
  
  Published：2017-08
- Accepted：
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Rui-rui LIU, Yun-long WANG, Jie-xin YIN, et al. Passive source localization using importance sampling based on TOA and FOA measurements[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(8): 1167-1179.
DOI：

Rui-rui LIU, Yun-long WANG, Jie-xin YIN, et al. Passive source localization using importance sampling based on TOA and FOA measurements[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(8): 1167-1179. DOI： 10.1631/FITEE.1601657.

摘要

最大似然类的无源定位方法可以达到较高的定位精度，但其计算量非常大。由于时频参数联合定位模型本身的非线性和非凸性非常大，繁重的计算量在TOA与FOA联合定位系统中表现尤为明显。本文针对这一问题，通过Pincus全局最优理论和蒙特卡洛重要性采样技术降低了最大似然类定位算法的计算复杂度，并且保证算法可以收敛到全局最优解。本文主要的贡献是构建了一个高斯分布的概率密度函数来近似原始的代价函数方便后续的采样，我们称之为重要性函数。该方法所带来性能上的提升是因为选择了最优的重要性函数并且Pincus保证算法收敛到全局最小值。这一处理大大降低了计算量，由于算法进行了泰勒级数展开，需要初始估计值。通过采样处理并且对样本进行加权，本文算法对初始估计值具有良好的鲁棒性。最后，实验证明本文所提算法可以达到克拉美罗限，且性能要优于现有算法。

Abstract

Passive source localization via a maximum likelihood (ML) estimator can achieve a high accuracy but involves high calculation burdens

especially when based on time-of-arrival and frequency-of-arrival measurements for its internal nonlinearity and nonconvex nature. In this paper

we use the Pincus theorem and Monte Carlo importance sampling (MCIS) to achieve an approximate global solution to the ML problem in a computationally efficient manner. The main contribution is that we construct a probability density function (PDF) of Gaussian distribution

which is called an important function for efficient sampling

to approximate the ML estimation related to complicated distributions. The improved performance of the proposed method is attributed to the optimal selection of the important function and also the guaranteed convergence to a global maximum. This process greatly reduces the amount of calculation

but an initial solution estimation is required resulting from Taylor series expansion. However

the MCIS method is robust to this prior knowledge for point sampling and correction of importance weights. Simulation results show that the proposed method can achieve the Cramér-Rao lower bound at a moderate Gaussian noise level and outperforms the existing methods.

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