• Akanksha Singh
• Smita Sharma
• Suman Avdhesh Yadav

Spectral estimation is very useful in the estimation of the power and frequency of the signal. The goal of spectral estimation is to describe the distribution of the power contained in a signal, based on a finite set of data. Estimation of power spectra is useful in a variety of applications, including the detection of signals buried in wide-band noise. The power spectrum of a stationary random process is mathematically related to the correlation sequence by the discrete-time Fourier transform. Frequency estimation of a sinusoid in white noise using the maximum entropy power spectral method has been shown to be very sensitive to the initial sinusoid. Previously work has been done which showed the dependence of the frequency estimate of the sinusoid in white noise on the phase of the sinusoid, and the Hilbert transform has been used to obtain the analytic signal. This paper used the wavelet method to obtain the analytic signal and then employed the maximum entropy power spectral method for the frequency estimation. The use of Morlet wavelet for the generation of the analytic signal is reducing the percentage change in frequency deviation. The analytic signal is generated using the complex Morlet wavelet.The dependence of the frequency estimate of a sinusoid in white noise on the phase of the sinusoid can be significantly reduced by employing an analytic signal approach.

"Spectral Estimation using Morlet Transform", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.135-138, June-2018, Available :http://www.jetir.org/papers/JETIR1806521.pdf

"Spectral Estimation using Morlet Transform", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp135-138, June-2018, Available at : http://www.jetir.org/papers/JETIR1806521.pdf