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Multisensor Integration and Data Fusion for Positioning
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Author(s): Yong Li (Navextech Technology Ltd, Australia), Wei Jiang (Beijing Jiaotong University, China), Ling Yang (Tongji University, China)and Chris Rizos (University of New South Wales, Australia)
Copyright: 2018
Pages: 42
Source title:
Positioning and Navigation in Complex Environments
Source Author(s)/Editor(s): Kegen Yu (Wuhan University, China)
DOI: 10.4018/978-1-5225-3528-7.ch011
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Abstract
This chapter presents an alternative framework to the traditional centralised Kalman filtering (CKF) approach for implementing the optimal state estimation algorithm in support of multisensor integration. The data fusion algorithm is implemented via a series of transformations of vectors in the so-called information space (iSpace). This chapter describes how the conventional decentralised Kalman filtering (DKF) algorithm can be derived using a unified approach. A new global optimal fusion (GOF) algorithm is derived using the iSpace approach. Two case studies are presented to illustrate applications of the multisensor algorithms for GNSS/Locata/INS and GNSS/WiFi/INS integration.
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