- Tytuł:
- Instantaneous Frequency Estimation of Multi-Component Non- Stationary Signals using Fourier Bessel series and Time-Varying Auto Regressive Model
- Autorzy:
-
Ravi Shankar Reddy, G.
Rao, R. - Powiązania:
- https://bibliotekanauki.pl/articles/226382.pdf
- Data publikacji:
- 2015
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
basis functions
Fourier-Bessel expansion
instantaneous frequency
multi component non stationary signal
Time-varying Auto Regressive model - Opis:
- In this paper, we propose a novel technique for Instantaneous frequency (IF) estimation of multi component non stationary signals using Fourier Bessel Series and Time-Varying Auto Regressive (FB-TVAR) model. In the proposed technique, the Fourier-Bessel (FB) expansion decomposes the multicomponent non stationary signal into a number of monocomponent signals and TVAR model is used to model each monocomponent signal. In TVAR modeling approach the time varying parameters are expanded as a linear combination of basis functions. In this paper, the TVAR parameters are expanded by a discrete cosine basis functions. The maximum likelihood estimation algorithm for model order selection in TVAR models is also discussed. The Instantaneous frequency (IF) is extracted from the time-varying parameters by calculating the angles of the estimation error filter polynomial roots. The estimation of the TVAR parameters of a multicomponent signal requires the inversion of a large covariance matrix, while the projected technique (FB-TVAR) requires the inversion of a number of comparatively small covariance matrices with better numerical stability properties. Simulation results are presented for three component discrete Amplitude and Frequency modulated (AM-FM)signal.
- Źródło:
-
International Journal of Electronics and Telecommunications; 2015, 61, 4; 365-376
2300-1933 - Pojawia się w:
- International Journal of Electronics and Telecommunications
- Dostawca treści:
- Biblioteka Nauki
Artykuł