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

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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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Authors and Affiliations

  1. National Digital Switching System Engineering & Technology Research Center, Zhengzhou, 450001, China

    Rui-rui Liu, Yun-long Wang, Jie-xin Yin, Ding Wang & Ying Wu

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  1. Rui-rui Liu
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  2. Yun-long Wang
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  3. Jie-xin Yin
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  4. Ding Wang
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  5. Ying Wu
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Corresponding author

Correspondence to Yun-long Wang.

Additional information

Project supported by the National Natural Science Foundation of China (No. 61201381) and the China Postdoctoral Science Foundation (No. 2016M592989)

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Liu, Rr., Wang, Yl., Yin, Jx. et al. Passive source localization using importance sampling based on TOA and FOA measurements. Frontiers Inf Technol Electronic Eng 18, 1167–1179 (2017). https://doi.org/10.1631/FITEE.1601657

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  • DOI: https://doi.org/10.1631/FITEE.1601657

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