A Spiking Bout Algorithm Based on Gas Sensor Signals for the Distance Estimation from Gas Source

Estimating the distance to the odor source is crucial for olfactory navigation as it influences olfactory signals and navigation behavior. The derivative of a MOX gas sensors signal is considered to be associated with gas fluctuations, thereby enabling the estimation of the distance to the source. Derivatively-based methods require the calculation of differentials and can generate large amounts of data. In this study, a distance estimation method for odor sources is proposed based on spike encoding and the number of spike clusters with the cubic polynomial model. Results show that the proposed method reduces the data volume while retaining effective information. For the distance estimation of a sensor in a turbulent environment, the proposed method performs well in estimating source distance. The RMSE is 0.109 m. In conclusion, the proposed spike-based source estimation method demonstrates effective distance estimation with a single sensor for gas sources. This lays a foundation for research in gas source estimation and olfactory navigation.