Open AccessTrack fusion in the presence of sensor biases
Author(s) -
Zhu Hongyan,
Chen Shuo
Publication year - 2014
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2013.0393
Subject(s) - taylor series , series (stratigraphy) , monte carlo method , sensor fusion , computer science , algorithm , track (disk drive) , least squares function approximation , fusion , artificial intelligence , mathematics , statistics , mathematical analysis , paleontology , linguistics , philosophy , estimator , biology , operating system
A computationally effective approach is developed in this study to deal with the problem of track fusion in the presence of sensor biases. Aiming at the case that sensor biases are implicitly included in the local estimates, a pseudo‐measurement equation is derived based on the Taylor series expansion firstly, which reveals the relationship explicitly between local estimates and the sensor biases; and then, the bias estimates can be obtained in the rule of recursive least squares; finally, based on the derived pseudo‐measurement equation, the sensor biases can be removed from the original local estimates and track fusion can be carried out directly and easily. Monte Carlo simulations demonstrate the efficiency and effectiveness of the proposed approach compared with the competing algorithms.