Authors:
Yifan Xie
;
Seung Hyo Park
and
Taek Lyul Song
Affiliation:
Department of Electronic Systems Engineering, Hanyang University, Ansan and Republic of Korea
Keyword(s):
TDOA, 3D, Corrleated Measurement Noise, Cholesky, Target Tracking.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Nonlinear Signals and Systems
;
Sensors Fusion
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
Target tracking with time difference of arrival measurements usually encounters the problem of correlated measurement noises. When the sensor network utilizes the common reference sensor, the covariance matrix of the correlated measurement noises becomes off-diagonal such that the computational complexity of the inverse of the covariance matrix as well as the subsequent matrix operations increases proportionally to the cube of the sensor number. This makes target tracking algorithms inconvenient for practical applications, and an appropriate measurement noise decorrelation method is required. In multi-sensor environments, the parallel update and the serial update are applied for exploiting the measurements from different sensors. Although the two methods deliver the equivalent tracking performances in linear systems, this equivalence does not hold in nonlinear systems as linearizing the nonlinear functions leads to approximation error. Additionally, the requirements of the two method
s for storage structure and computational resource allocations are different. This paper presents a target tracking algorithm which integrates the Cholesky decomposition to decorrelate the measurement noises for the serial update which shows computational efficiency. The tracking performance is evaluated by estimation accuracy, execution time.
(More)