Abstract
The advanced network applications enable software driven spectral analysis of non-stationary signal or processes which precisely involves domain analysis with the purpose of decomposing a complex signal coefficients into simpler forms. However, the proper estimation of power coefficients over frequency components of a random signal leads to provide very useful information required in various fields of study. The complex design constraints associated with conventional parametric models such as Dynamic Average Model, Autoregressive MA, etc. for multidimensional spectral estimation using adaptive filters leads to a situation where higher computational complexities generate significant overhead on the systems. Therefore, the proposed study aims to formulate an efficient framework intended to derive a fast algorithm for processing Adaptive Capon and Phase Estimator (APES). The proposed method is applied to a non-stationary signal which is random. Further, the adaptive estimation of power spectra along with more accurate spectral efficiency has been identified in case of APES. An extensive performance evaluation followed by a comparative analysis has been performed by obtaining the values from different spectral estimation techniques, such as APES, PSC, ASC, and CAPON. Moreover, the framework ensures that unlike others, APES is subjected to attain superior signal quality regarding Power Spectral Density (PSD) and Signal to Noise Ratio (SNR) while achieving very less amount of Mean Square Error (MSE). It also exhibits comparatively low convergence speed and computational complexity as compared to its legacy versions.
Highlights
Spectral analysis of signals is the measurement of power spectral components further analyzed to investigate the frequency coefficients of a random signal
The proposed system aims to design a novel framework is integrated with different significant spectral estimation frameworks, e.g., Capon, PSC, APES, and ASC to perform spectral estimation on non-stationary signals by reducing the (MSE) of all linear regression models
The computational efficiency offered by the proposed method is illustrated in Fig. 4, where the process involves www.ijacsa.thesai.org effective spectral correlation (SC) of APES method is illustrated as well as the spectral estimation of the above algorithm is shown as a function of the sliding window data size N. here the number of observed samples N=100, filter length M=8, sampling frequency fs=1, Signal to Noise Ratio (SNR)=35 is assumed for the implementation purposes
Summary
Spectral analysis of signals is the measurement of power spectral components further analyzed to investigate the frequency coefficients of a random signal. The algorithm is claimed to have a limited scope of applications due to various factors such as poor resolution and high side lobe problems This situation further leads to a scenario, where retrieval of significant information by analyzing signal coefficients becomes entirely unfeasible. An in-depth investigational study gives an insight into the fact that the conventional data-dependent (adaptive) methods for both non-parametric and parametric approaches attain superior performance efficiency in comparison with the conventional data independent methods like Periodogram. The applicability of dataadaptive approaches further leads to improve the spectrum quality of a signal significantly and helps to retrieve more information under study. It has gained the interest among more researchers to explore its applicability towards mitigating issues of spectral estimation.
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