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Automatic Whistler Detector and Analyzer system: Automatic Whistler Detector

机译:自动吹口哨检测仪和分析仪系统:自动吹口哨检测仪

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A new, unique system has been developed for the automatic detection and analysis of whistlers. The Automatic Whistler Detector and Analyzer (AWDA) system has two purposes: (1) to automatically provide plasmaspheric electron densities extracted from whistlers and (2) to collect statistical data for the investigation of whistler generation and propagation. This paper presents the details of and the first results obtained by the automatic detector segment. The detector algorithm is based on image correlation where the target image is a preprocessed spectrogram of raw VLF signals and the pattern is a model whistler. The first AWDA system has been working in Tihany, Hungary (L = 1.8), and has collected 100,000 whistler traces per year. The overall detection efficiency using a parameter set optimized for purpose 2 is 90% for misdetection and 50–80% for false detection. The statistical analysis over the period February 2002 to February 2008 including 600,000 whistler traces shows high diurnal variations; whistler were mainly, but not only, detected when both the source and receiver regions were unlit. The seasonal occurrence is high during austral summer and low during austral winter. Comparison with Tarcsai et al.'s (1988) statistical study on Tihany whistlers shows differences in both diurnal and seasonal variations, but the latter study was made on 1388 manually identified whistlers only. The L value distributions of both data sets are similar. A global network of AWDA systems (AWDAnet) has been set up to overcome the time and space limitations of a single station; the network consists of 13 nodes, and another 6 are envisaged for the near future.
机译:已经开发了一种新的独特系统,用于自动检测和分析啸叫者。自动吹口哨检测仪和分析仪(AWDA)系统有两个目的:(1)自动提供从吹口哨中提取的等离子层电子密度;(2)收集统计数据以研究吹口哨的产生和传播。本文介绍了自动检测器部分的详细信息和首次获得的结果。检测器算法基于图像相关性,其中目标图像是原始VLF信号的预处理频谱图,而图案是模型吹口哨。第一个AWDA系统已在匈牙利的蒂豪尼(L = 1.8)运行,并且每年收集了100,000条口哨痕迹。使用针对目的2优化的参数集的总体检测效率对于误检测为90%,对于误检测为50-80%。在2002年2月至2008年2月的统计分析中,包括60万条口哨痕迹,显示出很大的昼夜变化。当源和接收器区域均未点亮时,主要但不仅是检测到了吹口哨。季节发生在南方夏季高,南方冬季低。与Tarcsai等人(1988)对蒂豪尼(Tihany)吹口哨的统计研究的比较表明,昼夜变化和季节变化均存在差异,但后者的研究仅针对1388个手动识别的吹口哨。两个数据集的L值分布相似。已经建立了一个全球性的AWDA系统网络(AWDAnet),以克服单个站点的时间和空间限制;该网络由13个节点组成,并计划在不久的将来再增加6个。

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