| 1. |
- Blanco, Jose Luis, et al.
(författare)
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A Kalman Filter Based Approach To Probabilistic Gas Distribution Mapping
- 2013
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Konferensbidrag (refereegranskat)abstract
- Building a model of gas concentrations has important indus-trial and environmental applications, and mobile robots ontheir own or in cooperation with stationary sensors play animportant role in this task. Since an exact analytical de-scription of turbulent flow remains an intractable problem,we propose an approximate approach which not only esti-mates the concentrations but also their variances for eachlocation. Our point of view is that of sequential Bayesianestimation given a lattice of 2D cells treated as hidden vari-ables. We first discuss how a simple Kalman filter pro-vides a solution to the estimation problem. To overcomethe quadratic computational complexity with the mappedarea exhibited by a straighforward application of Kalmanfiltering, we introduce a sparse implementation which runsin constant time. Experimental results for a real robot vali-date the proposed method.
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| 2. |
- Gonzàlez Monroy, Javier, et al.
(författare)
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Probabilistic gas quantification with MOX sensors in open sampling systems : a gaussian process approach
- 2013
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Ingår i: Sensors and actuators. B, Chemical. - : Elsevier. - 0925-4005 .- 1873-3077. ; 188, s. 298-312
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Tidskriftsartikel (refereegranskat)abstract
- Gas quantification based on the response of an array of metal oxide (MOX) gas sensors in an Open Sampling System is a complex problem due to the highly dynamic characteristic of turbulent airflow and the slow dynamics of the MOX sensors. However, many gas related applications require to determine the gas concentration the sensors are being exposed to. Due to the chaotic nature that dominates gas dispersal, in most cases it is desirable to provide, together with an estimate of the mean concentration, an estimate of the uncertainty of the prediction. This work presents a probabilistic approach for gas quantification with an array of MOX gas sensors based on Gaussian Processes, estimating for every measurement of the sensors a posterior distribution of the concentration, from which confidence intervals can be obtained. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID), used to obtain ground truth concentration, are placed downwind with respect to the gas source. Our approach has been implemented and compared with standard gas quantification methods, demonstrating the advantages when estimating gas concentrations.
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| 3. |
- Monroy, Javier G., et al.
(författare)
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Calibration of mox gas sensors in open sampling systems based on gaussian processes
- 2012
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Ingår i: Proceedings of the IEEE Sensors Conference, 2012. - : IEEE conference proceedings. - 9781457717659 ; , s. 1-4
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Konferensbidrag (refereegranskat)abstract
- Calibration of metal oxide (MOX) gas sensor for continuous monitoring is a complex problem due to the highly dynamic characteristics of the gas sensor signal when exposed to natural environment (Open Sampling System - OSS). This work presents a probabilistic approach to the calibration of a MOX gas sensor based on Gaussian Processes (GP). The proposed approach estimates for every sensor measurement a probability distribution of the gas concentration. This enables the calculation of confidence intervals for the predicted concentrations. This is particularly important since exact calibration is hard to obtain due to the chaotic nature that dominates gas dispersal. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID) are placed downwind w.r.t. the gas source. The PID is used to obtain ground truth concentration. Comparison with standard calibration methods demonstrates the advantage of the proposed approach.
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