Plume source position estimation using sensor networks

This paper proposes the use of a sensor network for estimating the location of a source that releases certain substance in the environment which is then propagated over a large area. More specifically, we use nonlinear least squares optimization to estimate the source position based on the concentration readings at the sensor nodes. Such a network can be of tremendous help to emergency personnel trying to protect people from terrorist attacks or responding to an accident. Our results indicate that in high uncertainty environments it pays off to use a large number of sensors in the estimation whereas in low uncertainty scenarios a few sensors achieve satisfactory results. In addition, our results point out the importance of choosing the appropriate parameters for the least squares optimization especially the start position for our algorithm. We compare our results to the Closest Point Approach (CPA) where the source location is assumed to be the sensor node with the highest measurement. ©2005 IEEE.