AUTHOR=Jia Xutao , Song Tianhong , Liu Guang 

TITLE=Fine grained analysis method for unmanned aerial vehicle measurement based on laser-based light scattering particle sensing

JOURNAL=Frontiers in Physics

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2024.1413037

DOI=10.3389/fphy.2024.1413037

ISSN=2296-424X

ABSTRACT=As an effective particle measurement method, laser-based particle sensors combined with unmanned aerial vehicles (UAVs) can be used for measuring air quality in near ground space. In order to assess the air quality between flight trajectories, a new fine-grained analysis method, Co-KNN-DNN is proposed to present the continuous distribution of air quality in more detail. First of all, the overall scheme was designed, M30T UAV was selected to carry the portable air quality monitoring equipment, with laser-based laser particulate matter sensor and Mini2, to collect AQI and related attributes of the near-ground layer in the selected research area, to do the necessary processing of the collected data, to build a data set suitable for model input, etc., to train and optimize the model, and to carry out practical application of the model. Based on the spatial dimension-based air quality finegrained analysis model Co-KNN-DNN, three experiments were conducted at different altitudes within the study area. 290 samples from each altitude data set were randomly selected to form the initial marker sample set, and 200 samples from each altitude were randomly selected as the test sample set. The remaining samples were unlabeled sample sets. The experimental results show that the average R-squared value can reach 0.99. The effectiveness and practicability of the Co-KNN-DNN model were verified by application research.