Scholarlink

<p>LIKES (LIKelihood-based Estimation of Sparse parameters) is a novel method that is applied to the Indian MST radar data for the estimation of the Doppler profiles. The traditional spectral estimation methods fail to detect the signal as they are normally weaker due to contamination with clutter and interference. The semiparametric method, namely, LIKES which is based on the principle of maximum likelihood and is more accurate than SPICE (SParse Iterative Covariance-based Estimation), is applied to the simulated data. This paper gives the implementation of the LIKES algorithm for the Indian MST radar data along with the estimation of various wind parameters and is validated with global positioning system (GPS) radiosonde data. The results indicate the improved performance of LIKES method when compared to existing methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Likelihood-Based Estimation Algorithm for Processing Indian MST Radar Data

  • 期刊论文

  • 发表日期：

    2025年6月21日

查看全文
  • Raju C,
  • Sivasubramanyam Medasani,
  • Swetha Kumari Koduru,
  • Sreenivasulu Reddy Thatiparthi

摘要

LIKES (LIKelihood-based Estimation of Sparse parameters) is a novel method that is applied to the Indian MST radar data for the estimation of the Doppler profiles. The traditional spectral estimation methods fail to detect the signal as they are normally weaker due to contamination with clutter and interference. The semiparametric method, namely, LIKES which is based on the principle of maximum likelihood and is more accurate than SPICE (SParse Iterative Covariance-based Estimation), is applied to the simulated data. This paper gives the implementation of the LIKES algorithm for the Indian MST radar data along with the estimation of various wind parameters and is validated with global positioning system (GPS) radiosonde data. The results indicate the improved performance of LIKES method when compared to existing methods.