Nonlinear chirp mode extraction: A new efficient method to decompose nonstationary signals

Abstract

Current signal decomposition methods face difficulties such as mode mixing, low efficiency, and the need for prior knowledge, etc. In view of that, this paper proposes a new method, called Nonlinear Chirp Mode Extraction (NCME), for adaptively extracting nonlinear chirp modes from nonstationary signals. This method can decompose a signal into desired mode and residual mode adaptively without any prior knowledge. A functional filter is used here to tackle the mode mixing problem and therefore improves the constraint optimization to help extract the desired mode accurately. Prior knowledge for initializing the number of modes in the signal is then no longer required and the desired mode can be extracted directly from the signal. Both computational efficiency and accuracy are greatly improved. The effectiveness and advantages of NCME are verified with simulated and measured signals. The results show that NCME can extract nonlinear chirp modes with higher precision, noise robustness, and computational efficiency than the comparative methods.