CRMD: Complex Robust Modal Decomposition

TL;DR

This paper introduces a robust modal decomposition method in the complex domain, enhancing analysis of complex signals across diverse scientific and engineering applications with improved performance.

Contribution

It extends the real-valued robust modal decomposition to the complex domain, providing a mathematically grounded and algorithmically tailored approach.

Findings

Significant performance improvements in synthetic and real-world datasets

Effective in diverse applications like radar, bearing fault detection, UAV identification, and WiFi sensing

Demonstrates robustness and versatility of the complex-domain RMD

Abstract

Compared to real-valued signals, complex-valued signals provide a unique and intuitive representation of the phase of real physical systems and processes, which holds fundamental significance and is widely applied across many fields of science and engineering. In this paper, we propose a robust modal decomposition (RMD) in the complex domain as a natural and general extension of the original real-valued RMD. We revisit and derive the mathematical principles of RMD in the complex domain, and develop an algorithmic version tailored for this domain. Extensive experiments are conducted on synthetic simulation datasets and real-world datasets from diverse fields, including a millimeter-wave radar physiological signal detection dataset, a faulty bearing dataset, a radio-frequency unmanned aerial vehicle identification dataset, and a WiFi CSI-based respiration detection dataset. The results demonstrate that the proposed complex-domain robust modal decomposition significantly improves performance across these various applications.