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

作     者：Rui-rui LIU Yun-long WANG Jie-xin YIN Ding WANG Ying WU 

作者机构：National Digital Switching System Engineering & Technology Research Center Zhengzhou 450001 China 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2017年第18卷第8期

页      面：1167-1179页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081001[工学-通信与信息系统] 

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

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

摘      要：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.