Signal-to-noise ratio estimation using higher-order moments

Sekhar, S. Chandra; Sreenivas, T.V.

doi:10.1016/j.sigpro.2005.06.003

Cited by 24 publications

(24 citation statements)

References 17 publications

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“…The method relies on the possibility of expressing moments from the observed random signal as functions of the parameters to be estimated. However, when in the observed signal model, one term is deterministic, as is our case, so the derivation of the moments might be cumbersome, as shown, for example, in [6]. For these cases, Kay suggests to assume a random nature for the "deterministic" sinusoid by assuming a random phase, and in this paper we follow such an approach [7] (p. 299).…”

Section: Moment-based Estimatorsmentioning

confidence: 99%

“…The same problem has been studied in [5], wherein Papic et al proposed an algorithm for SNR estimation based on autocorrelation and modified covariance methods for autoregressive spectral estimation and required the reliable estimate of the frequency of the sinusoid. The case of complex rather than real deterministic sinusoids in additive noise has been addressed in [6], wherein an SNR estimator derived with the method of moments [7] that makes use of second and fourth order moments was proposed (M 2 M 4 -estimator). It does not require the complex sinusoid frequency estimate, and for this reason, it belongs to the class of blind SNR estimators and may find applications in all those cases where the frequency estimate may not be available or frequency may be varying over the observation interval.…”

Section: Introductionmentioning

confidence: 99%

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Asymptotic Performances of a Signal-To-Noise Ratio Moment-Based Estimator for Real Sinusoids in Additive Noise

Romano

2019

Applied Sciences

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We considered the problem of the estimation of signal-to-noise ratio (SNR) with a real deterministic sinusoid with unknown frequency, phase and amplitude in additive Gaussian noise of unknown variance. A blind SNR estimator that does not require the knowledge of the instantaneous frequency of the sinusoid, through separate estimation of signal and noise power, was derived using the method of moments, a general method to derive estimators based on high-order moments. Statistical performances of the proposed estimators were studied theoretically through derivation of Cramer–Rao lower bounds (CRLBs) and asymptotic variances. Furthermore, results from Monte-Carlo simulations that confirm the validity of the theoretical analysis are presented along with some comments on the use of proposed estimators in practical applications.

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Section: Moment-based Estimatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Asymptotic Performances of a Signal-To-Noise Ratio Moment-Based Estimator for Real Sinusoids in Additive Noise

Romano

2019

Applied Sciences

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“…i. The maximum likelihood estimator of signal amplitude and noise standard deviation for FM signals is proposed in [15]:…”

Section: Comments On the Algorithm And Parameter Setupmentioning

confidence: 99%

Bias compensation in the instantaneous frequency estimators based on the time–frequency representations and ICI algorithm

Djurović

2017

IET signal process.

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In this study, the author has proposed a technique for a bias compensation in the intersection-of-the-confidenceintervals (ICI) algorithm for the instantaneous frequency (IF) estimation. Algorithms from the ICI class are based on selection of the signal adaptive window length in the time-frequency representation-based IF estimators giving a trade-off between a bias and variance. The main difficulty in these estimators is sensitivity to a high noise influence that is producing outliers in estimates used for the ICI algorithm initialisation. Several ICI algorithm modifications are proposed in order to achieve robustness to the high noise influence. However, these techniques can suffer from the emphatic bias. The proposed bias compensation algorithm is based on the estimation of high-order IF derivative. It gives improvement in the mean squared error for more than 5 dB with respect to the state-of-the-art variants of the ICI algorithm.

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“…The ZCR of the AR-filtered is used as the robust estimate of in (8). For a complex sinusoid in complex white Gaussian noise, the method-of-moments (MoM) technique provides a robust estimate of the amplitude of the sinusoid using second-and fourthorder sample moments [32]. Since we are dealing with realvalued-signals, we employ the moments-based variance estimator on the analytic counterpart of the signal and estimate as where is the analytic version of .…”

Section: Require Setmentioning

confidence: 99%

A Zero-Crossing Rate Property of Power Complementary Analysis Filterbank Outputs

Shenoy

Seelamantula

2015

IEEE Signal Process. Lett.

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We establish zero-crossing rate (ZCR) relations between the input and the subbands of a maximally decimated -channel power complementary analysis filterbank when the input is a stationary Gaussian process. The ZCR at lag is defined as the number of sign changes between the samples of a sequence and its -sample shifted version, normalized by the sequence length. We derive the relationship between the ZCR of the Gaussian process at lags that are integer multiples of and the subband ZCRs. Based on this result, we propose a robust iterative autocorrelation estimator for a signal consisting of a sum of sinusoids of fixed amplitudes and uniformly distributed random phases. Simulation results show that the performance of the proposed estimator is better than the sample autocorrelation over the SNR range of to 15 dB. Validation on a segment of a trumpet signal showed similar performance gains.

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Signal-to-noise ratio estimation using higher-order moments

Cited by 24 publications

References 17 publications

Asymptotic Performances of a Signal-To-Noise Ratio Moment-Based Estimator for Real Sinusoids in Additive Noise

Asymptotic Performances of a Signal-To-Noise Ratio Moment-Based Estimator for Real Sinusoids in Additive Noise

Bias compensation in the instantaneous frequency estimators based on the time–frequency representations and ICI algorithm

A Zero-Crossing Rate Property of Power Complementary Analysis Filterbank Outputs

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