Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 4Citation - Scopus: 4Phase Dependence Mitigation for Autocorrelation-Based Frequency Estimation(Elsevier Ltd., 2008) Altınkaya, Mustafa Aziz; Anarım, Emin; Sankur, BülentThe sinusoidal frequency estimation from short data records based on Toeplitz autocorrelation (AC) matrix estimates suffer from the dependence on the initial phases of the sinusoid(s). This effect becomes prominent when the impact of additive noise vanishes, that is at high signal-to-noise ratios (SNR). Based on both analytic derivation of the AC lag terms and simulation experiments we show that data windowing can mitigate the limitations caused by the phase dependence. Thus with proper windowing, the variance of the frequency estimate is no more eclipsed by phase dependence, but it continues to decrease linearly with increasing SNR. The study covers both the cases of a single sinusoid and two sinusoids closely spaced in the frequency with the Pisarenko frequency estimator, MUSIC and principal component autoregressive frequency estimators. The trade-offs between the spectral broadening and the achieved minimum variance level due to the data window are analyzed in detail.Conference Object Phase Noise Mitigation in the Autocorrelation Estimates With Data Windowing: the Case of Two Close Sinusoids(Institute of Electrical and Electronics Engineers Inc., 2006) Altınkaya, Mustafa Aziz; Anarım, Emin; Sankur, BülentWe address the phase noise and the superresolution problem in Toeplitz matrix-based spectral estimates. The Toeplitz autocorrelation (AC) matrix approach in spectral estimation brings in an order of magnitude computational advantage while the price paid is the phase noise that becomes effective at high signal-to-noise ratios (SNR). This noise can be mitigated with windowing the data though some concomitant loss in resolution occurs. The trade-offs between additive noise SNR, resolvability of sinusoids closer than the resolution limit, and behavior of the estimated AC lags and tone frequencies are investigated.Conference Object Citation - Scopus: 1Removal of the Phase Noise in the Autocorrelation Estimates With Data Windowing(Institute of Electrical and Electronics Engineers Inc., 2005) Altınkaya, Mustafa Aziz; Anarım, Emin; Sankur, BülentThe sinusoidal frequency estimation from short data records based on Toeplitz autocorrelation (AC) matrix estimates suffer from phase noise. This effect becomes prominent especially when additive noise vanishes becoming a nuisance, that is at high signal-to-noise ratios (SNR). Based on both analytic derivation of the AC lag terms and simulation experiments, we show that data windowing can mitigate the limitations caused by the phase noise. Thus with proper windowing, the variance of the frequency estimate is no more limited by phase noise, but it continues to decrease linearly with the SNR. The cases of the Pisarenko frequency estimator and of MUSIC, both for the single sinusoid case, are analyzed in detail.Article Citation - WoS: 9Citation - Scopus: 15Subspace-Based Frequency Estimation of Sinusoidal Signals in Alpha-Stable Noise(Elsevier Ltd., 2002) Altınkaya, Mustafa Aziz; Deliç, Hakan; Sankur, Bülent; Anarım, EminIn the frequency estimation of sinusoidal signals observed in impulsive noise environments, techniques based on Gaussian noise assumption are unsuccessful. One possible way to find better estimates is to model the noise as an alpha-stable process and to use the fractional lower order statistics (FLOS) of the data to estimate the signal parameters. In this work, we propose a FLOS-based statistical average, the generalized covariation coefficient (GCC). The GCCs of multiple sinusoids for unity moment order in SαS noise attain the same form as the covariance expressions of multiple sinusoids in white Gaussian noise. The subspace-based frequency estimators FLOS-multiple signal classification (MUSIC) and FLOS-Bartlett are applied to the GCC matrix of the data. On the other hand, we show that the multiple sinusoids in SαS noise can also be modeled as a stable autoregressive moving average process approximated by a higher order stable autoregressive (AR) process. Using the GCCs of the data, we obtain FLOS versions of Tufts-Kumaresan (TK) and minimum norm (MN) estimators, which are based on the AR model. The simulation results show that techniques employing lower order statistics are superior to their second-order statistics (SOS)-based counterparts, especially when the noise exhibits a strong impulsive attitude. Among the estimators, FLOS-MUSIC shows a robust performance. It behaves comparably to MUSIC in non-impulsive noise environments, and both in impulsive and non-impulsive high-resolution scenarios. Furthermore, it offers a significant advantage at relatively high levels of impulsive noise contamination for distantly located sinusoidal frequencies.