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Parametric spectral moments estimation for wind profiling radar

机译:风廓线雷达的参数谱矩估计

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The purpose of this work is the estimation of Doppler echoes spectral moments. In case of strong overlapping, Fourier-like techniques provide poor results because of the lack of resolution. We propose the use of stochastic maximum-likelihood (SML) and subspace-based methods (WPSF algorithm) for a joint estimation of spectral moments. The statistical performances (theoretical and empirical by Monte Carlo simulations) of estimators are compared with the Cramer-Rao lower bound. The results of tests performed on very high frequency (VHF) times series obtained during Thunderstorm, Arecibo, PR during September and October 1998 validate the model and algorithms and confirm the interest of both approaches.
机译:这项工作的目的是估计多普勒回波频谱矩。在强重叠的情况下,由于缺乏分辨率,类似傅立叶的技术效果不佳。我们建议使用随机最大似然(SML)和基于子空间的方法(WPSF算法)联合估计谱矩。将估计量的统计性能(通过蒙特卡洛模拟的理论和经验)与Cramer-Rao下界进行比较。 1998年9月至1998年10月在美国加利福尼亚州阿雷西博市雷暴期间对甚高频(VHF)时间序列进行的测试结果验证了该模型和算法,并确认了这两种方法的兴趣。

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