Doppler Feature Based Classification of Wind Profiler Data

2016 International Conference on Communication, Image and Signal Processing

物理学;无线电电子学;计算机科学

Sinha, Swati^1 ; Chandrasekhar Sarma, T.V.^2 ; Lourde, Mary R.^1

BITS Pilani Dubai Campus Dubai, United Arab Emirates^1

National Atmospheric Research Laboratory Gadanki, India^2

关键词: Classical techniques;    Feature-based classification;    Mean squared error;    Operating parameters;    Radar target identification;    Target Classification;    Weather prediction;    Wind velocity and direction;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/787/1/012028/pdf
DOI  :  10.1088/1742-6596/787/1/012028

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Wind Profilers (WP) are coherent pulsed Doppler radars in UHF and VHF bands. They are used for vertical profiling of wind velocity and direction. This information is very useful for weather modeling, study of climatic patterns and weather prediction. Observations at different height and different wind velocities are possible by changing the operating parameters of WP. A set of Doppler power spectra is the standard form of WP data. Wind velocity, direction and wind velocity turbulence at different heights can be derived from it. Modern wind profilers operate for long duration and generate approximately 4 megabytes of data per hour. The radar data stream contains Doppler power spectra from different radar configurations with echoes from different atmospheric targets. In order to facilitate systematic study, this data needs to be segregated according the type of target. A reliable automated target classification technique is required to do this job. Classical techniques of radar target identification use pattern matching and minimization of mean squared error, Euclidean distance etc. These techniques are not effective for the classification of WP echoes, as these targets do not have well-defined signature in Doppler power spectra. This paper presents an effective target classification technique based on range-Doppler features.