The non-parametric amplitude estimation using MSL windows
نویسندگان
چکیده
The error reduction of the non-parametric amplitude estimation of the periodic signals with the three-point interpolated discrete Fourier transform (DFT) using cosine windows is presented. The paper analyzes and compares the systematic bias errors and the noise error behavior of the amplitude estimation changing the order of Rife-Vincent windows class I (RV1), which are designed for maximization of the window spectrum side-lobe fall-off, and minimum side-lobe level (MSL) windows, which are designed for minimization of the energy in the window spectrum main lobe. The lowest systematic bias errors can be found with the MSL windows and at the same time they better suppress the noise error contribution owed to smaller equivalent noise bandwidth (ENBW) than RV1 windows with the same order.
منابع مشابه
Power Spectrum Estimation for Band Pass Filter Using Non Parametric Methods
Power spectrum estimation is one of the important applications in DSP. Basically a spectrum is a relationship typically represented by a plot of the magnitude or relative value of some parameter against frequency. In this Paper Non Parametric Methods are used for power spectrum estimation namely Periodogram Method and Welch Method. Here A Band pass FIR Filter using two different Window Techniqu...
متن کاملSPECTRAL ESTIMATION via ADAPTIVE TUNABLE NON-PARAMETRIC METHOD
We devise a new approach for non-parametric adaptive spectral analysis method, which is called the Adaptive Tuning Amplitude and Phase Estimation (ATAPES) method. The main advantage of the ATAPES algorithm is its elimination of biased estimation results in APES method, which is biased peak location and corresponding biased amplitude estimation problem. Therefore, ATAPES method provides more acc...
متن کاملتخمین احتمال بزرگی زمینلغزشهای رخداده در حوزه آبخیز پیوهژن (استان خراسان رضوی)
Knowing the number, area, and frequency of landslides occurred in each area has a prominent role in the long-term evolution of area dominated by landslides and can be used for analyzing of susceptibility, hazard, and risk. In this regard, the current research is trying to consider identified landslides size probability in the Pivejan Watershed, Razavi Khorasan Province. In the first step, lands...
متن کاملNon-Local Manifold Parzen Windows
To escape from the curse of dimensionality, we claim that one can learn non-local functions, in the sense that the value and shape of the learned function at x must be inferred using examples that may be far from x. With this objective, we present a non-local non-parametric density estimator. It builds upon previously proposed Gaussian mixture models with regularized covariance matrices to take...
متن کاملManifold Parzen Windows
The similarity between objects is a fundamental element of many learning algorithms. Most non-parametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, in particular to exploit the local invariances in the data or to capture the possibly non-linear manifold on which most of the data lies. We propose a new non-parametric kernel density est...
متن کامل