CLC number: TN91
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-08-18
Cited: 0
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Rui-rui Liu, Yun-long Wang, Jie-xin Yin, Ding Wang, Ying Wu. Passive source localization using importance sampling based on TOA and FOA measurements[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(8): 1167-1179.
@article{title="Passive source localization using importance sampling based on TOA and FOA measurements",
author="Rui-rui Liu, Yun-long Wang, Jie-xin Yin, Ding Wang, Ying Wu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="8",
pages="1167-1179",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601657"
}
%0 Journal Article
%T Passive source localization using importance sampling based on TOA and FOA measurements
%A Rui-rui Liu
%A Yun-long Wang
%A Jie-xin Yin
%A Ding Wang
%A Ying Wu
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 8
%P 1167-1179
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601657
TY - JOUR
T1 - Passive source localization using importance sampling based on TOA and FOA measurements
A1 - Rui-rui Liu
A1 - Yun-long Wang
A1 - Jie-xin Yin
A1 - Ding Wang
A1 - Ying Wu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 8
SP - 1167
EP - 1179
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601657
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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