Real-time Gaussian Markov random-field-based ground tracking for ground penetrating radar data

2009 ◽  
Author(s):  
Kyle Bradbury ◽  
Peter A. Torrione ◽  
Leslie Collins
Keyword(s):  
Random Field ◽  
Real Time ◽  
Markov Random Field ◽  
Ground Penetrating Radar ◽  
Radar Data ◽  
Gaussian Markov Random Field ◽  
Markov Random ◽  
Ground Penetrating

scholarly journals Traffic Updates: Saying a Lot While Revealing a Little

2019 ◽  
Vol 33 ◽  
pp. 986-995
Author(s):  
John Krumm ◽  
Eric Horvitz
Keyword(s):  
Random Field ◽  
Real Time ◽  
Markov Random Field ◽  
Road Network ◽  
Probabilistic Method ◽  
Vehicle Occupant ◽  
Traffic Conditions ◽  
Potential Value ◽  
Markov Random ◽  
Traffic Speed

Taking speed reports from vehicles is a proven, inexpensive way to infer traffic conditions. However, due to concerns about privacy and bandwidth, not every vehicle occupant may want to transmit data about their location and speed in real time. We show how to drastically reduce the number of transmissions in two ways, both based on a Markov random field for modeling traffic speed and flow. First, we show that a only a small number of vehicles need to report from each location. We give a simple, probabilistic method that lets a group of vehicles decide on which subset will transmit a report, preserving privacy by coordinating without any communication. The second approach computes the potential value of any location’s speed report, emphasizing those reports that will most affect the overall speed inferences, and omitting those that contribute little value. Both methods significantly reduce the amount of communication necessary for accurate speed inferences on a road network.

scholarly journals Mobile Robotic Sensors for Environmental Monitoring using Gaussian Markov Random Field

Robotica ◽  
2020 ◽  
pp. 1-23
Author(s):  
Linh Nguyen ◽  
Sarath Kodagoda ◽  
Ravindra Ranasinghe ◽  
Gamini Dissanayake
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Adaptive Sampling ◽  
Published Data ◽  
Data Sets ◽  
Independence Property ◽  
Gaussian Markov Random Field ◽  
Markov Random ◽  
Sampling Optimization ◽  
Robotic Sensors

SUMMARY This paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.

scholarly journals On the Introduction of Canny Operator in an Advanced Imaging Algorithm for Real-Time Detection of Hyperbolas in Ground-Penetrating Radar Data

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 541 ◽  
Author(s):  
Željko Bugarinović ◽  
Lara Pajewski ◽  
Aleksandar Ristić ◽  
Milan Vrtunski ◽  
Miro Govedarica ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Penetrating Radar ◽  
Urban Areas ◽  
Radar Data ◽  
Detection Algorithm ◽  
Advanced Imaging ◽  
Canny Operator ◽  
Imaging Algorithm ◽  
Canny Edge ◽  
Ground Penetrating

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB.

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