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

• Published in: Frontiers of information technology & electronic engineering Vol. 18; no. 8; pp. 1167 - 1179
• Main Authors: Liu, Rui-rui, Wang, Yun-long, Yin, Jie-xin, Wang, Ding, Wu, Ying
• Format: Journal Article
• Language: English
• Published: Hangzhou Zhejiang University Press 01.08.2017; Springer Nature B.V
• Subjects: Communications Engineering; Computer Hardware; Computer Science; Computer Systems Organization and Communication Networks; Electrical Engineering; Electronics and Microelectronics; Importance sampling; Instrumentation; Localization; Lower bounds; Mathematical analysis; Maximum likelihood estimators; Networks; Noise levels; Normal distribution; Probability density functions; Random noise; Series expansion; Statistical analysis; Taylor series; Monte Carlo importance sampling (MCIS); TN91; Frequency of arrival (FOA); Time of arrival (TOA); Passive source localization; Maximum likelihood (ML)
• ISSN: 2095-9184; 2095-9230
• DOI: 10.1631/FITEE.1601657

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 at- tributed 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 Cram6r-Rao lower bound at a moderate Gaussian noise level and outper- forms the existing methods.