Power spectrum is the considerable aspect in the atmospheric radar data processing to estimate wind parameters. Due to the poor resolution and high sidelobe level problems of the existing algorithms, there is a requisite for the novel data-dependent approaches. A non-parametric and hyperparameter-free iterative adaptive approach (IAA) is presented for the power spectral density estimation. This approach is able to work with single snapshot and is obtained by minimizing the weighted least square fitting criterion. The IAA method provides the accurate amplitude and frequency estimation for the simulated data. The data for the above study is collected from Indian MST (mesosphere, stratosphere, and troposphere) radar. The power spectrum and Doppler frequency are estimated using IAA. In this paper, zonal (U), meridional (V), windspeed (W) are also calculated and validated using Global Positioning System Sonde data. The effectiveness of the spectral estimation performance showed by IAA is demonstrated and assessed.

中文翻译：

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使用迭代自适应方法的MST雷达信号处理

功率谱是大气雷达数据处理中估计风参数的重要方面。由于现有算法的较差的分辨率和较高的旁瓣级问题，因此需要新颖的数据相关方法。提出了一种非参数且无超参数的迭代自适应方法（IAA），用于功率谱密度估计。该方法能够使用单个快照，并且可以通过最小化加权最小二乘拟合准则来获得。IAA方法可为仿真数据提供准确的幅度和频率估计。以上研究的数据是从印度MST（平流层，平流层和对流层）雷达收集的。使用IAA估算功率谱和多普勒频率。在本文中，纬向（U），子午（V），风速（W）也会使用全球定位系统Sonde数据进行计算和验证。证明并评估了IAA显示的光谱估计性能的有效性。