Time-frequency decomposition of band-limited signals with BMFLC and Kalman filter

Time-Frequency analysis of band-limited signals received significant attention in biomedical research. As most bio-signals are non-stationary, time-frequency analysis is essential to analyze the characteristics of the signal. To accurately model the band-limited bio-signal, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter provides accurate time-frequency decomposition of the bandlimited signal. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for synthesized data is performed in this paper. The results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT.