Title :
TDOA based data association and multi-targets passive localization algorithm
Author :
Hongwei Li ; Chun Li
Author_Institution :
54th Res. Inst., CETC, Shijiazhuang, China
Abstract :
With the development of wireless frequency monitoring equipment, passive localization of multi-targets has become one of the most popular research hotspots. In this paper, by using the characteristic of multi-sensors passive location based on time difference of arrival (TDOA) and the continuity of TDOA at different times, a TDOA based Data Association and multi-targets passive localization algorithm is proposed, which realizes correct association of multi-targets measurements and utilizes Kalman filtering (KF), extended Kalman filtering (EKF) and particle filtering (PF) to locate the multi-radiant targets. The simulation shows that the proposed algorithm has a high correct probability for data association; moreover, the use of the filtering processing can improve the location precision and realize a fast convergence of localization error.
Keywords :
Kalman filters; filtering theory; nonlinear filters; particle filtering (numerical methods); probability; sensor fusion; time-of-arrival estimation; TDOA based data association; TDOA continuity; extended Kalman filtering; fast localization error convergence; high correct probability; location precision improvement; multisensors passive location characteristic; multitargets measurements; multitargets passive localization algorithm; particle filtering; time difference-of-arrival; wireless frequency monitoring equipment development; Atmospheric measurements; Filtering algorithms; Kalman filters; Mathematical model; Particle measurements; Time measurement; Data Association; TDOA; extended Kalman filtering; multi-targets localization; particle filter;
Conference_Titel :
Image and Signal Processing (CISP), 2014 7th International Congress on
Conference_Location :
Dalian
DOI :
10.1109/CISP.2014.7003948
