Title :
Source Localization and Tracking Using Distributed Asynchronous Sensors
Author :
Li, Teng ; Ekpenyong, Anthony ; Huang, Yih-Fang
Author_Institution :
Marvell Semicond., Inc, Santa Clara, CA
Abstract :
This paper presents a source localization algorithm based on the source signal´s time-of-arrival (TOA) at sensors that are not synchronized with one another or the source. The proposed algorithm estimates source positions using a window of TOA measurements which, in effect, creates a virtual sensor array. Based on a Gaussian noise model, maximum likelihood estimates (MLE) for the source position and displacement are obtained. Performance issues are addressed by evaluating the Cramer-Rao lower bound and considering the virtual sensor array´s geometric properties. To track the source trajectory from the TOA measurement, which is a nonlinear function of source position and displacement, this localization algorithm is combined with the extended Kalman filter (EKF) and the unscented Kalman filter, resulting in good tracking performance
Keywords :
Gaussian noise; Kalman filters; array signal processing; distributed sensors; maximum likelihood estimation; nonlinear filters; time-of-arrival estimation; Cramer-Rao lower bound; Gaussian noise model; distributed asynchronous sensors; extended Kalman filter; maximum likelihood estimates; source localization algorithm; source signal position; source tracking algorithm; time-of-arrival measurement; unscented Kalman filter; virtual sensor array; Clocks; Displacement measurement; Global Positioning System; Large-scale systems; Maximum likelihood estimation; Position measurement; Sensor arrays; Signal processing algorithms; Synchronization; Timing; Asynchronous sensors; Kalman filter; localization; sensor network; synchronization; time-difference-of-arrival (TDOA); time-of-arrival (TOA); tracking;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2006.880213
