A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation
1
School of Electronic Engineering, Heilongjiang University, Harbin 150080, China
2
School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(10), 2222; https://doi.org/10.3390/s17102222
Received: 8 August 2017 / Revised: 24 September 2017 / Accepted: 26 September 2017 / Published: 28 September 2017
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications 2017)
We addressed the fusion estimation problem for nonlinear multisensory systems. Based on the Gauss–Hermite approximation and weighted least square criterion, an augmented high-dimension measurement from all sensors was compressed into a lower dimension. By combining the low-dimension measurement function with the particle filter (PF), a weighted measurement fusion PF (WMF-PF) is presented. The accuracy of WMF-PF appears good and has a lower computational cost when compared to centralized fusion PF (CF-PF). An example is given to show the effectiveness of the proposed algorithms.
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Keywords:
nonlinear system; weighted measurement fusion; Gauss–Hermite approximation; particle filter
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MDPI and ACS Style
Li, Y.; Sun, S.L.; Hao, G. A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation. Sensors 2017, 17, 2222. https://doi.org/10.3390/s17102222
AMA Style
Li Y, Sun SL, Hao G. A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation. Sensors. 2017; 17(10):2222. https://doi.org/10.3390/s17102222
Chicago/Turabian StyleLi, Yun; Sun, Shu L.; Hao, Gang. 2017. "A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation" Sensors 17, no. 10: 2222. https://doi.org/10.3390/s17102222
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