Implementation of a Hebbian chemoreceptor model for diffusive source localization

While new approaches to chemical localization have been proposed, animals are still widely used for locating landmines and illegal substances. Existing electronic noses still do not have the necessary sensitivity and accuracy. By modeling a cell's chemical detection system, we can gain insight into the basic “olfactory” system. We use an inspiration from chemotaxis and Hebbian learning to enhance localization and tracking of gradient sources, which can be applied to both chemicals and heat. The eukaryotic receptor clustering model shows improvement over previous prokaryotic chemotaxis-inspired methods that do not take into account receptor clustering. Receptor clustering essentially adapts receptors spatio-temporally. For a mobile simulation, our method locates the source in less convergence time than the other chemotaxis algorithms and insignificantly less time compared to no spatio-temporal filtering (e.g. a single-sensor memoryless case). We then show that local regions of receptor cooperation have the best performance reflecting observations of receptor behavior in biology. To demonstrate the performance of this system in real-time, a stationary 4/8-sensor version of the array is implemented, and the algorithm improves the convergence time, mean, and variance of the Direction-of-Arrival calculation in diffusive, turbulent, and noisy environments.