Open AccessTDOA and FDOA based source localisation via importance sampling
Author(s) -
Wang Yunlong,
Wu Ying,
Wang Ding,
Shen Yuan
Publication year - 2018
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2017.0242
Subject(s) - fdoa , multilateration , estimator , fisher information , computer science , algorithm , mathematical optimization , upper and lower bounds , sampling (signal processing) , mathematics , consistency (knowledge bases) , statistics , artificial intelligence , filter (signal processing) , mathematical analysis , geometry , azimuth , computer vision
Source localisation withtime‐difference‐of‐arrival (TDOA) and frequency‐difference‐of‐arrival (FDOA)measurements is of great interest since it can provide the location information with highaccuracy.Although the maximumlikelihood (ML) estimator exhibits excellent asymptotic properties, the non‐linearity and non‐convexity of ML estimator requiremuch computation resources.In this study, source localisation with TDOA and FDOA measurementsis developed viaMonteCarlo importance sampling (IS).In particular, the optimalperformance can be guaranteed by constructing an optimalimportance function whosecovariance is equivalent to the inverse of Fisher information matrix.The derived variance of the proposed estimator showsgood consistency with the theoretical lowerbound. The improved performance of the proposed method is due to the optimal selection ofimportance function and it canconverge to the global optimum with a large number of samples. Although an initial estimate of source localisation information isrequired, the proposedmethod is robust to this a priori knowledge via IS. Moreover, the scenario ofconsidering sensor location uncertainties is analysed and the corresponding IS based solution is derived. Simulation results show that the proposed methods can achieve the Cramér–Rao lower bound at moderate level noises and is superior to several existing methods.