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Stochastic Maximum Likelihood (SML) parametric estimation of overlapped Doppler echoes

机译:重叠多普勒回波的随机最大似然(SML)参数估计

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摘要

This paper investigates the area of overlapped echo data processing. In such cases, classical methods, such as Fourier-like techniques or pulse pair methods, fail to estimate the first three spectral moments of the echoes because of their lack of resolution. A promising method, based on a modelization of the covariance matrix of the time series and on a Stochastic Maximum Likelihood (SML) estimation of the parameters of interest, has been recently introduced in literature. This method has been tested on simulations and on few spectra from actual data but no exhaustive investigation of the SML algorithm has been conducted on actual data: this paper fills this gap. The radar data came from the thunderstorm campaign that took place at the National Astronomy and Ionospheric Center (NAIC) in Arecibo, Puerto Rico, in 1998.
机译:本文研究了重叠回波数据处理的领域。在这种情况下,由于缺乏分辨率,经典方法(如傅立叶式技术或脉冲对方法)无法估计回波的前三个频谱矩。最近在文献中引入了一种有前途的方法,该方法基于时间序列的协方差矩阵的模型化以及感兴趣参数的随机最大似然(SML)估计。该方法已在模拟中进行了测试,并在实际数据中仅在少数光谱上进行了测试,但尚未对实际数据进行SML算法的详尽研究:本文填补了这一空白。雷达数据来自1998年在波多黎各阿雷西博的国家天文学和电离层中心(NAIC)进行的雷暴活动。

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