Suppression of False-terms in Wigner-Ville Distribution using Time and Frequency Windowing
- Resource Type
- Conference
- Authors
- Faisal, Kazi Newaj; Sharma, Rishi Raj
- Source
- 2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS) Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS), 2023 2nd International Conference on. :1-6 Apr, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Performance evaluation
Time-frequency analysis
Smoothing methods
Simulation
Machine learning
Electrocardiography
Kernel
Non-stationary signal analysis
Wigner-Ville distribution (WVD)
false-term reduction
Time and frequency windowing.
- Language
The Wigner-Ville distribution (WVD) is a widely used tool in the time-frequency analysis of non-stationary signals. However, the presence of false-terms in WVD for multicomponent signals can limit its applicability and interpretation. Various kernel and window-based smoothing methods have been used to remove false-terms from WVD, but they often come at the cost of reduced time-frequency resolution of autoterms. This paper proposes a novel sliding time and frequency windowing-based technique for removing false-terms from WVD, which aims to overcome the limitations of kernel-based methods. The proposed method segments a multi-component signal using overlapping windows in time and frequency domains successively and the WVD of each windowed signal is computed. The WVDs of all windowed signals are added together to obtain the falseterm free WVD. Energy scaling is also applied to minimize the effect of overlapping windows. Performance of the proposed method is evaluated for different multi-component synthetic signals and a natural ECG signal using various performance measures. The simulation results demonstrate that the proposed method can effectively remove false-terms from the WVD with improved auto-term enhancement and time-frequency resolution. Results from the proposed method are also compared with different kernel and window-based smoothing methods to show its superiority over these methods.