Parameter estimation and blind channel identification in impulsive signal environments

Ma, Xinyu; Nikias, C.L.

doi:10.1109/78.476432

Cited by 165 publications

(8 citation statements)

References 9 publications

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“…Since there has no finite variance of an α-stable process X n ( ) , its frequency characteristics cannot be represented by the power spectrum used in the Gaussian model. Dealing with the problem of spectrum estimation for an alpha-stable process, we adopt α-spectrum estimation method, 20 due to its equivalent position to the power spectrum density estimation method based on autocorrelation function in Gaussian distribution.…”

Section: Sncc-based Yule-walker Equation Of Arma Modelmentioning

confidence: 99%

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α-spectrum estimation for 1/f processes in noisy environments

Wang

Shi

Zhu

et al. 2019

Noise & Vibration Worldwide

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In the past, 1/f noise was regarded as a stochastic process that accords with Gaussian distribution. According to our studies, the output transistor 1/f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear SαS processes and propose a samples normalized cross-correlations-based α-spectrum method effective in noisy environments. Simulation results and diodes noise spectrum estimation results exhibit good performance.

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Section: Sncc-based Yule-walker Equation Of Arma Modelmentioning

confidence: 99%

“…Dealing with the problem of spectrum estimation for an alpha-stable process, we adopt α -spectrum estimation method, 20 due to its equivalent position to the power spectrum density estimation method based on autocorrelation function in Gaussian distribution.…”

Section: Sncc and Sncc-based Spectrum Estimation In Sαs Processmentioning

confidence: 99%

α-spectrum estimation for 1/f processes in noisy environments

Wang

Shi

Zhu

et al. 2019

Noise & Vibration Worldwide

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“…Dealing with the problem of spectrum estimation for an α-stable process, we adopt α-spectrum estimation method, 27 due to its equivalent position to the power spectrum density estimation method based on the autocorrelation function in Gaussian distribution.…”

Section: Development Of An Sncc-based Parameter Estimation Algorithm mentioning

confidence: 99%

1/f spectrum estimation based on α-stable distribution in colored Gaussian noise environments

Shi

Wang

Zhu

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

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The main goal of this paper is to suppress the effect of unavoidable colored Gaussian noise on declining accuracy of transistor 1/f spectrum estimation. Transistor noises are measured by a nondestructive cross-spectrum measurement method, which is first to amplify the voltage signals through ultra-low noise amplifiers, then input the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output 1/f noise can be characterized more accurately as non-Gaussian a-stable distribution rather than Gaussian distribution. Therefore, by utilizing the properties of a-stable distribution, we propose a cross-spectrum method effective in noisy environments based on samples normalized cross-correlation function. Simulation results and diodes output noise spectrum estimation results confirm the effectiveness of our method.

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“…The blind Cauchy detector, which is a special case of the GLRT, only performs well for a few characteristic exponent values α . The fractional lower order moment (FLOM) is an effective tool for signal processing in S S α noise and is often utilized for parameter estimation [13,14,15]. Recently, the FLOM-based detector was presented, and its performance was investigated in [16].…”

Section: Introductionmentioning

confidence: 99%

Signal Detection Based on a Decreasing Exponential Function in Alpha-Stable Distributed Noise

2018

KSII TIIS

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Signal detection in symmetric alpha-stable (S S α) distributed noise is a challenging problem. This paper proposes a detector based on a decreasing exponential function (DEF). The DEF detector can effectively suppress the impulsive noise and achieve good performance in the presence of S S α noise. The analytical expressions of the detection and false alarm probabilities of the DEF detector are derived, and the parameter optimization for the detector is discussed. A performance analysis shows that the DEF detector has much lower computational complexity than the Gaussian kernelized energy detector (GKED), and it performs better than the latter in S S α noise with small characteristic exponent values. In addition, the DEF detector outperforms the fractional lower order moment (FLOM)-based detector in S S α noise for most characteristic exponent values with the same order of magnitude of computational complexity.

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Parameter estimation and blind channel identification in impulsive signal environments

Cited by 165 publications

References 9 publications

α-spectrum estimation for 1/f processes in noisy environments

α-spectrum estimation for 1/f processes in noisy environments

1/f spectrum estimation based on α-stable distribution in colored Gaussian noise environments

Signal Detection Based on a Decreasing Exponential Function in Alpha-Stable Distributed Noise

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