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CLC number: TN91

On-line Access: 2017-09-08

Received: 2016-10-25

Revision Accepted: 2017-04-17

Crosschecked: 2017-08-18

Cited: 0

Clicked: 3692

Citations:  Bibtex RefMan EndNote GB/T7714


Rui-rui Liu


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.8 P.1167-1179


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

Author(s):  Rui-rui Liu, Yun-long Wang, Jie-xin Yin, Ding Wang, Ying Wu

Affiliation(s):  National Digital Switching System Engineering & Technology Research Center, Zhengzhou 450001, China

Corresponding email(s):   chriswayulo@sina.com

Key Words:  Passive source localization, Time of arrival (TOA), Frequency of arrival (FOA), Monte Carlo importance sampling (MCIS), Maximum likelihood (ML)

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.

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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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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