Multilayer detection and classification of specular and nonspecular meteor trails

• Published in: Radio science Vol. 46; no. 6; pp. np - n/a
• Main Authors: Zhao, Siming, Urbina, Julio, Dyrud, Lars, Seal, Ryan
• Format: Journal Article
• Language: English
• Published: Washington Blackwell Publishing Ltd 01.12.2011
• Subjects: Atmospheric sciences; Digital signal processors; meteor trail classification; radar signal processing; Radio; ASW, Caribbean Sea, Greater Antilles, Puerto Rico
• ISSN: 0048-6604; 1944-799X
• DOI: 10.1029/2010RS004548

Meteor radar data are continuously collected by different radar systems that operate throughout the year. Analyzing this fast growing, large data set requires efficient and reliable detection routines. Currently most meteor echo routines search for underdense meteor trails, often discarding overdense and nonspecular meteor trails. This is because their main purpose is the study of mesospheric winds. But the study of meteor flux requires the unique identification of each type of meteor reflections. In this paper, a multilayer radar detection and classification algorithm is proposed to correctly identify multiple types of meteor trail reflections. The process consists of two steps. The first step is based on the time‐frequency waveform detector. In this step, we start by selecting low signal‐to‐noise ratio (SNR) values in order to detect all types of radar echoes; however, a high probability offalse alarm is often produced. In the second step, several features from the detected echoes in step one are extracted and a support vector machine (SVM) classifier is constructed to further classify these echoes. The algorithm was tested using data collected from a 50‐MHz radar stationed near Salinas, Puerto Rico, on April 5, 1998. A total of 270 detected echoes were labeled as underdense, overdense, nonspecular, other ionospheric echoes, and noise. We used 50% of the labeled echoes as training samples and divided the rest 50% testing samples as 10 subsets for testing. This technique successfully classified about 85% of the testing samples. Details concerning implementation, feature extraction, and data visualization are presented and discussed. Key Points Time‐frequency waveform detector is used to detect meteor trail reflections Support vector machine classifier is built to classify meteor trail reflections The algorithm was tested using data collected from a 50‐MHz radar