Multilayer detection and classification of specular and nonspecular meteor trails

Siming Zhao, Julio Urbina, Lars Dyrud, Ryan Seal

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish (US)
    Article numberRS6009
    JournalRadio Science
    Volume46
    Issue number6
    DOIs
    StatePublished - Dec 19 2011

    Fingerprint

    meteor trails
    meteor
    echoes
    Multilayers
    Radar
    meteoroids
    radar
    Testing
    Data visualization
    Radar systems
    radar detection
    Puerto Rico
    scientific visualization
    Support vector machines
    radar echoes
    Feature extraction
    Signal to noise ratio
    Classifiers
    warning systems
    radar data

    All Science Journal Classification (ASJC) codes

    • Condensed Matter Physics
    • Earth and Planetary Sciences(all)
    • Electrical and Electronic Engineering

    Cite this

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    abstract = "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.",
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    Multilayer detection and classification of specular and nonspecular meteor trails. / Zhao, Siming; Urbina, Julio; Dyrud, Lars; Seal, Ryan.

    In: Radio Science, Vol. 46, No. 6, RS6009, 19.12.2011.

    Research output: Contribution to journalArticle

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