Name
Title | ||
|---|---|---|
A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss-Hermite Approximation. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/s17102222 | SENSORS |
Keywords | Field | DocType |
nonlinear system,weighted measurement fusion,Gauss-Hermite approximation,particle filter | Least squares,Gauss,Nonlinear system,Control theory,Particle filter,Hermite polynomials,Algorithm,Fusion,Electronic engineering,Engineering | Journal |
Volume | Issue | ISSN |
17 | 10.0 | 1424-8220 |
Citations | PageRank | References |
2 | 0.40 | 20 |
Authors | ||
3 | ||