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\begin{document}

\title{A robust complex local mean decomposition method with self-adaptive
sifting stopping}



\author[1]{CanYu Mo}%
\author[1]{QianQiang Lin}%
\author[1]{YuanDuo Niu}%
\author[1]{HaoRan Du}%
\affil[1]{National University of Defense Technology}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
Targets with rotating components generate micro-motion (MM) modulation
effect in addition to the main body. Extracting MM parameters is
challenging due to interference from the target's main body,
necessitating the separation of modulation signals. This letter proposes
a robust complex local mean decomposition (RCLMD) method with
self-adaptive sifting stopping, aiming at the problem of component
redundancy due to multiple iterations during break and the loss of
modulation components during the separation process. The proposed method
sets the objective function and self-adaptive stopping criterion,
combined with the modulation signal characteristics, enhancing the
accuracy and efficiency of MM component extraction. Simulation
experiments indicate that at a low signal-to-noise ratio (SNR) of 3 dB,
the separation effect of RCLMD is still 14.72\textbackslash{}\% higher
than that of the conventional complex local mean decomposition (CLMD)
method, and the separation efficiency is improved by
54.92\textbackslash{}\%. Furthermore, the measured radar signals verify
the effectiveness of the proposed method in real scenarios.%
\end{abstract}%



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