Hybrid non-parametric belief propagation for localization in wireless networks

2011 ◽  
Author(s):  
P.-A. Oikonomou-Filandras ◽  
Kai-Kit Wong
Keyword(s):  
Wireless Networks ◽  
Belief Propagation ◽  
Non Parametric

scholarly journals Uniformly Reweighted Belief Propagation for Estimation and Detection in Wireless Networks

2012 ◽  
Vol 11 (4) ◽  
pp. 1587-1595 ◽  
Author(s):  
Henk Wymeersch ◽  
Federico Penna ◽  
Vladimir Savic
Keyword(s):  
Wireless Networks ◽  
Belief Propagation

scholarly journals Applications of Belief Propagation in CSMA Wireless Networks

2012 ◽  
Vol 20 (4) ◽  
pp. 1276-1289 ◽  
Author(s):  
Cai Hong Kai ◽  
Soung Chang Liew
Keyword(s):  
Wireless Networks ◽  
Belief Propagation

Sliding window non-parametric cumulative sum: a quick algorithm to detect selfish behaviour in wireless networks

2011 ◽  
Vol 5 (15) ◽  
pp. 2130-2140 ◽  
Author(s):  
C. Liu ◽  
O.W.W. Yang ◽  
M. Li ◽  
Y. Shu
Keyword(s):  
Wireless Networks ◽  
Sliding Window ◽  
Cumulative Sum ◽  
Non Parametric

scholarly journals INCORPORATION OF UNRELIABLE INFORMATION INTO PHOTOGRAMMETRIC RECONSTRUCTION FOR RECOVERY OF SCALE USING NON-PARAMETRIC BELIEF PROPAGATION

2016 ◽  
Vol XLI-B5 ◽  
pp. 653-658
Author(s):  
J. Hollick ◽  
P. Helmholz ◽  
D. Belton
Keyword(s):  
Error Bounds ◽  
Belief Propagation ◽  
Sensor Data ◽  
Control Points ◽  
Geometric Accuracy ◽  
Scale Models ◽  
The Creation ◽  
Scaled Models ◽  
Actual Size ◽  
Non Parametric

The creation of large photogrammetric models often encounter several difficulties in regards to geometric accuracy, scale and geolocation, especially when not using control points. Geometric accuracy can be a problem when encountering repetitive features, scale and geolocation can be challenging in GNSS denied or difficult to reach environments. Despite these challenges scale and location are often highly desirable even if only approximate, especially when the error bounds are known. Using non-parametric belief propagation we propose a method of fusing different sensor types to allow robust creation of scaled models without control points. Using this technique we scale models using only the sensor data sometimes to within 4% of their actual size even in the presence of poor GNSS coverage.

scholarly journals INCORPORATION OF UNRELIABLE INFORMATION INTO PHOTOGRAMMETRIC RECONSTRUCTION FOR RECOVERY OF SCALE USING NON-PARAMETRIC BELIEF PROPAGATION

2016 ◽  
Vol XLI-B5 ◽  
pp. 653-658
Author(s):  
J. Hollick ◽  
P. Helmholz ◽  
D. Belton
Keyword(s):  
Error Bounds ◽  
Belief Propagation ◽  
Sensor Data ◽  
Control Points ◽  
Geometric Accuracy ◽  
Scale Models ◽  
The Creation ◽  
Scaled Models ◽  
Actual Size ◽  
Non Parametric

The creation of large photogrammetric models often encounter several difficulties in regards to geometric accuracy, scale and geolocation, especially when not using control points. Geometric accuracy can be a problem when encountering repetitive features, scale and geolocation can be challenging in GNSS denied or difficult to reach environments. Despite these challenges scale and location are often highly desirable even if only approximate, especially when the error bounds are known. Using non-parametric belief propagation we propose a method of fusing different sensor types to allow robust creation of scaled models without control points. Using this technique we scale models using only the sensor data sometimes to within 4% of their actual size even in the presence of poor GNSS coverage.

scholarly journals Non-parametric belief propagation for mobile mapping sensor fusion

2016 ◽  
Vol 19 (3) ◽  
pp. 195-201 ◽  
Author(s):  
Joshua Hollick ◽  
Petra Helmholz ◽  
David Belton
Keyword(s):  
Sensor Fusion ◽  
Belief Propagation ◽  
Mobile Mapping ◽  
Non Parametric
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