Multi-scale chirp sparse representation for  autonomous system non-stationary signal with Fast Varying Instantaneous Frequency

Abstract

Autonomous system is an emerging AI technology based on latest advances in intelligence, cognition, computing and systems science without human intervention, which is widely applied in the field of Internet of things, mechanical condition monitoring system and so on. With the rapid development of industry 4.0, autonomous system’s natural characteristic and application field determine that its corresponding signal presents multi-component, modulation coupling, non-stationary feature with fast time-varying instantaneous frequency (IF). Considering non-stationary signal contains system state information, how to effectively compute IF and realize signal decoupling separation plays an important role in autonomous system perception and making decision. In this paper, a sparse representation method called multi-scale chirp sparse representation (MSCSR) is introduced to to identify, extract and trend IF for achieving a highly concentrated time-frequency energy comparable to the standard IF estimation methods, simulation demonstrates that the proposed method performs better than traditional Hilbert-Huang transform and variational mode decomposition(VMD) combination with synchrosqueezing wavelet transform. Furthermore, an adaptive time-varying filter is constructed using the extracted instantaneous frequency to decouple non-stationary fast signal. Ultimately, by rapid instantaneous frequency fluctuation experiment, the effectiveness of proposed method for fast strong time-varying signal is validated, it can effectively extract rapid oscillation instantaneous frequency, and the error is less than 5%.