Plug-and-play measurement fusion method for integrated navigation system using low-cost nonlinear optimization

This paper addresses the problem of plug and play measurement fusion for integrated navigation system using nonlinear optimization method. We refer to multi-sensor fusion estimation as a sequential, weighted least squares problem. Using fluid re-linearization and partial state updates strategies to...

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Bibliographic Details
Published in2017 Forum on Cooperative Positioning and Service (CPGPS pp. 60 - 65
Main Authors Lingxiao Zheng, Xingqun Zhan, Xin Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
Data integration
Fluids
Global Positioning System
integrated navigation
multi-sensor data fusion
nonlinear optimization
Optimization
plug and play filter
Q measurement
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Summary:This paper addresses the problem of plug and play measurement fusion for integrated navigation system using nonlinear optimization method. We refer to multi-sensor fusion estimation as a sequential, weighted least squares problem. Using fluid re-linearization and partial state updates strategies to reduce computational burden, we propose a low cost nonlinear optimization algorithm. This algorithm not only processes multirate and asynchronous sensor data, but also provides a natural way to incorporate a new sensor or an ad hoc signal into the system. The proposed multi-sensor fusion estimation algorithm has been successfully implemented in integrated navigation with inertial measurement unit, global position system and star sensor.
DOI:10.1109/CPGPS.2017.8075098