发表机构
Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对短驻留天气雷达多普勒矩反演，提出基于FFT的快速Whittle最大似然参数谱估计，在保持精度优势的同时显著降低计算成本。
AI 中文摘要
本文针对短驻留天气雷达多普勒矩反演中Whittle最大似然参数谱估计（PSE）的计算成本问题进行了研究。PSE能够减少有限样本偏差，特别是在短相干处理间隔和宽谱的情况下，但重复的似然评估使得直接使用成本高昂。本文提出了一种有限样本高斯多普勒模型的快速实现方法，通过以闭式自相关滞后形式写出似然，并利用快速傅里叶变换（FFT）在与周期图相同的N点网格上合成模型谱及解析导数。该估计器被扩展为联合估计总功率、归一化平均多普勒频率和归一化谱宽。基于1000次噪声实现的蒙特卡洛模拟表明，快速PSE保持了原始PSE的精度优势，而目标级计时确认了从密集滞后到频率合成到基于FFT评估的缩减。
英文摘要
The computational cost of Whittle maximum-likelihood parametric spectrum estimation (PSE) for short-dwell weather-radar Doppler moment retrieval is addressed. PSE can reduce finite-sample bias, especially for short coherent processing intervals and broad spectra, but repeated likelihood evaluations make direct use expensive. A fast implementation of the finite-sample Gaussian Doppler model is introduced by writing the likelihood in terms of closed-form autocorrelation lags and synthesizing the model spectrum and analytic derivatives by fast Fourier transforms (FFTs) on the same N-point grid as the periodogram. The estimator is extended to jointly estimate total power, normalized mean Doppler frequency, and normalized spectral width. Monte Carlo simulations with 1000 noise realizations show that the fast PSE keeps the accuracy advantage of the original PSE, while objective-level timing confirms the reduction from dense lag-to-frequency synthesis to FFT-based evaluation.
Comments4 figures, 1 table
