A robust complex local mean decomposition method with self‐adaptive sifting stopping

Abstract

AbstractTargets 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 show that compared with the complex local mean decomposition method, the complex empirical mode decomposition method, and its improved method, the RCLMD method can achieve the highest decomposition effect of 96.57%, and the separation time consumed has a significant advantage over the above methods, performance is less fluctuating by the change of signal‐to‐noise ratio with good robustness. The measured data in real scenarios also verify the effectiveness of the proposed method.