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 To open or produce the figures inskcape is needed., # Odor source distance is predictable from time-histories of odor statistics for large scale outdoor plumes 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...湍流环境中的气味羽流具有间歇性且稀疏分布的特征。实验室尺度实验表明，气味源的距离信息可通过气味信号的统计特征进行编码，但目前尚不明确在真实流动环境中能否得到可靠且连续的源距估算结果。本研究针对在沙漠与森林环境中以大空间尺度移动的传感器所采集的户外实验气味信号展开分析，证明气味信号的统计特征可生成可靠的源距估算结果。研究表明，要实现高精度的源距估算，需整合5至10秒时长的统计特征，并对嗅觉信号采用至少20Hz的高时间分辨率编码。通过将线性模型得到的源距估算结果与相对风的运动动力学相结合，我们在60×60平方米的搜索区域内实现了源距估算，中位误差为3至8米——在此距离下，蚊子等动物通常可通过视觉定位气味源。 本实验装置可分为两个组成部分：其一为人类携带的移动传感器组，用于采集气味信号；其二为在气味源周边布置的8台固定式风速传感器，用于测量环境风速。气味源为丙烯气瓶，其上安装有固定式GPS天线，该天线可向移动传感器组上搭载的天线发送实时运动学（Real Time Kinematics）校正数据，以实现精度接近1厘米的高精度位置定位。移动传感器组包含气味传感器、用于接收高精度位置信息的GPS天线，以及用于测量角速度的惯性测量单元（IMU）。传感器组搭载于云台上以保证稳定性与便携性。气味传感器数据通过数据采集（DAQ）单元进行采集，该单元与所有其他传感器均连接至运行机器人操作系统（ROS）作为中间件的计算机，实现数据的实时记录。由于采样参数存在差异…… 数据文件为扩展名为.hdf与.h5的数据帧，可通过Python Pandas库进行处理，安装指南可参考：https://pandas.pydata.org/pandas-docs/version/1.5/getting_started/install.html。若要打开或生成图片，需使用Inkscape软件。 # 基于大尺度户外羽流气味统计特征的时间序列可实现气味源距预测 本仓库包含气味追踪实验的相关数据分析内容。 ## 摘要 湍流环境中的气味羽流具有间歇性且稀疏分布的特征。实验室尺度实验表明，气味源的距离信息可通过气味信号的统计特征进行编码，但目前尚不明确在真实流动环境中能否得到可靠且连续的源距估算结果。本研究针对在沙漠与森林环境中以大空间尺度移动的传感器所采集的户外实验气味信号展开分析，证明气味信号的统计特征可生成可靠的源距估算结果。研究表明，要实现高精度的源距估算，需整合5至10秒时长的统计特征，并对嗅觉信号采用至少20Hz的高时间分辨率编码。通过将线性模型得到的源距估算结果与相对风的运动动力学相结合，我们实现了源距……