Chemical plume source localization.
This paper addresses the problem of estimating a likelihood map for the location of the source of a chemical plume using an autonomous vehicle as a sensor probe in a fluid flow. The fluid flow is assumed to have a high Reynolds number. Therefore, the dispersion of the chemical is dominated by turbulence, resulting in an intermittent chemical signal. The vehicle is capable of detecting above-threshold chemical concentration and sensing the fluid flow velocity at the vehicle location. This paper reviews instances of biological plume tracing and reviews previous strategies for a vehicle-based plume tracing. The main contribution is a new source-likelihood mapping approach based on Bayesian inference methods. Using this Bayesian methodology, the source-likelihood map is propagated through time and updated in response to both detection and nondetection events. Examples are included that use data from in-water testing to compare the mapping approach derived herein with the map derived using a previously existing technique.
Pang
Shuo
S
Department of Electrical Engineering, University of California, Riverside, CA 92521, USA. shuo.pang@erau.edu
Farrell
Jay A
JA
D000397:Air Pollution / Q000032:analysis*; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D004058:Diffusion; D004784:Environmental Monitoring / Q000379:methods*; D044085:Microfluidics / Q000379:methods; D008956:Models, Chemical; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012371:Robotics / Q000379:methods; D012903:Smell; D014874:Water Pollutants, Chemical / Q000032:analysis*
D014874:Water Pollutants, Chemical