Odor source distance is predictable from time-histories of odor statistics for large scale outdoor plumes

Odor plumes in turbulent environments are intermittent and sparse. Lab-scaled experiments suggest that information about the source distance may be encoded in odor signal statistics, yet it is unclear whether useful and continuous distance estimates can be made under real-world flow conditions. Here we analyze odor signals from outdoor experiments with a sensor moving across large spatial scales in desert and forest environments to show that odor signal statistics can yield useful estimates of distance. We show that achieving accurate estimates of distance requires integrating statistics from 5-10 seconds, with a high temporal encoding of the olfactory signal of at least 20 Hz. By combining distance estimates from a linear model with wind-relative motion dynamics, we achieved source distance estimates in a 60x60 m2 search area with median errors of 3-8 meters, a distance at which point odor sources are often within visual range for animals such as mosquitoes., The setup can be divided into two components, the first being the mobile sensor stack that was carried by a human for collecting odor signals, and the second component included the placement of 8 stationery wind sensors for ambient wind measurements around the odor source. The odor source was a propylene gas tank that was mounted with a stationary GPS antenna which sent Real Time Kinematics (RTK) correction data to the antenna mounted on the mobile sensor stack for a high-resolution position with an accuracy close to 1cm.The mobile sensor stack included an odor sensor, a GPS antenna to receive accurate location measurements, and an IMU that provided angular velocity measurements. The sensors stack was balanced on a gimbal for stability and ease of carrying. The odor sensor data was collected using a data acquisition (DAQ) unit, which was connected along with all the other sensors to a computer that was running ROS as middleware and recorded data in real-time. Due to the different sampli..., These are data frames with .hdf and .h5 extension, processed with python pandas. They can be opened in using python pandas https://pandas.pydata.org/pandas-docs/version/1.5/getting_started/install.html
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This repository consist of the data analysis done for Odor Tracking experiment.

## Abstract

Odor plumes in turbulent environments are intermittent and sparse. Lab-scaled experiments suggest that information about the source distance may be encoded in odor signal statistics, yet it is unclear whether useful and continuous distance estimates can be made under real-world flow conditions. Here we analyze odor signals from outdoor experiments with a sensor moving across large spatial scales in desert and forest environments to show that odor signal statistics can yield useful estimates of distance. We show that achieving accurate estimates of distance requires integrating statistics from 5-10 seconds, with a high temporal encoding of the olfactory signal of at least 20 Hz. By combining distance estimates from a linear model with wind-relative motion dynamics, we achieved source dist...