Pseudo Cohen Time-Frequency Distributions in Infinite Variance Noise Environment

2013 ◽  
Vol 475-476 ◽  
pp. 253-258
Author(s):  
Hai Bin Wang ◽  
Jun Bo Long ◽  
Dai Feng Zha
Keyword(s):  
Order Statistic ◽  
Stable Distribution ◽  
Impulsive Noise ◽  
Infinite Variance ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Noise Environment ◽  
Class Distribution ◽  
Cohen Class ◽  
Low Order

stable distribution has been suggested as a more appropriate model in impulsive noise environment.The performance of conventional time-frequency distributions (TFDs) degenerate in stable distribution noise environment. Hence, three improved methods are proposed based on Fractional Low Order statistics, Fractional Low Order Wigner-Ville Distribution (FLO-WVD), Fractional Low Order Statistic pseudo Wigner-Ville Distribution (FLO-PWVD), Fractional Low Order Statistic Cohen class distribution (FLO-Cohen). In order for real-time, on-line operation and fairly long signals processing, a new smoothed pseudo Fractional Low Order Cohen class distribution (PFLO-Cohen) is proposed.Simulations show that the methods demonstrate the advantages in this paper, are robust.

Parameter Estimation Based on Fractional Correlation under Alpha-Stable Distribution Noise Environment in Bistatic MIMO Radar System

2014 ◽  
Vol 989-994 ◽  
pp. 3710-3713
Author(s):  
Li Li
Keyword(s):  
Parameter Estimation ◽  
Stable Distribution ◽  
Impulsive Noise ◽  
Mimo Radar ◽  
Radar System ◽  
Noise Model ◽  
Distribution Model ◽  
Music Algorithm ◽  
Bistatic Mimo Radar ◽  
Noise Environment

This paper takes the-stable distribution as the noise model and works on the parameter estimation problem of bistatic Multiple-Input Multiple-Output (MIMO) radar system in the impulsive noise environment.This paper presents a signal model and a novel method for parameter estimation in bistatic MIMO radar system in the impulsive noise environment. Firstly, a signal array model is constructed based on the-stable distribution model. Secondly, Doppler parameters are jointly estimated by searching the optimal rotation angle to meet concentrated-energy of the FLOS-FC. Furthermore, two algorithms are presented for the estimation of DODs and DOAs, including based on FLOS-MUSIC algorithm and FLOS-ESPRIT algorithm. Simulation results are presented to verity the effectiveness of the proposed method.

scholarly journals Single-Trial Evoked Potential Estimating Based on Sparse Coding under Impulsive Noise Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Nannan Yu ◽  
Ying Chen ◽  
Lingling Wu ◽  
Hanbing Lu
Keyword(s):  
Evoked Potentials ◽  
Sparse Coding ◽  
Stable Distribution ◽  
Impulsive Noise ◽  
Event Related Potentials ◽  
Signal Denoising ◽  
Single Trial ◽  
Gaussian Random Process ◽  
Noise Environment ◽  
Related Potentials

Estimating single-trial evoked potentials (EPs) corrupted by the spontaneous electroencephalogram (EEG) can be regarded as signal denoising problem. Sparse coding has significant success in signal denoising and EPs have been proven to have strong sparsity over an appropriate dictionary. In sparse coding, the noise generally is considered to be a Gaussian random process. However, some studies have shown that the background noise in EPs may present an impulsive characteristic which is far from Gaussian but suitable to be modeled by the α-stable distribution 1<α≤2. Consequently, the performances of general sparse coding will degrade or even fail. In view of this, we present a new sparse coding algorithm using p-norm optimization in single-trial EPs estimating. The algorithm can track the underlying EPs corrupted by α-stable distribution noise, trial-by-trial, without the need to estimate the α value. Simulations and experiments on human visual evoked potentials and event-related potentials are carried out to examine the performance of the proposed approach. Experimental results show that the proposed method is effective in estimating single-trial EPs under impulsive noise environment.

scholarly journals Applications of Fractional Lower Order Synchrosqueezing Transform Time Frequency Technology to Machine Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Haibin Wang ◽  
Junbo Long
Keyword(s):  
Lower Order ◽  
Signal Analysis ◽  
Stable Distribution ◽  
Signal Reconstruction ◽  
Outer Race ◽  
Time Frequency ◽  
S Transform ◽  
Synchrosqueezing Transform ◽  
Better Than ◽  
Low Order

Synchrosqueezing transform (SST) is a high resolution time frequency representation technology for nonstationary signal analysis. The short time Fourier transform-based synchrosqueezing transform (FSST) and the S transform-based synchrosqueezing transform (SSST) time frequency methods are effective tools for bearing fault signal analysis. The fault signals belong to a non-Gaussian and nonstationary alpha (α) stable distribution with 1<α<2 and even the noises being also α stable distribution. The conventional FSST and SSST methods degenerate and even fail under α stable distribution noisy environment. Motivated by the fact that fractional low order STFT and fractional low order S-transform work better than the traditional STFT and S-transform methods under α stable distribution noise environment, we propose in this paper the fractional lower order FSST (FLOFSST) and the fractional lower order SSST (FLOSSST). In addition, we derive the corresponding inverse FLOSST and inverse FLOSSST. The simulation results show that both FLOFSST and FLOSSST perform better than the conventional FSSST and SSST under α stable distribution noise in instantaneous frequency estimation and signal reconstruction. Finally, FLOFSST and FLOSSST are applied to analyze the time frequency distribution of the outer race fault signal. Our results show that FLOFSST and FLOSSST extract the fault features well under symmetric stable (SαS) distribution noise.

scholarly journals Applications of an Improved Time-Frequency Filtering Algorithm to Signal Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Junbo Long ◽  
Haibin Wang ◽  
Daifeng Zha ◽  
Hongshe Fan ◽  
Zefeng Lao ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Stable Distribution ◽  
Signal Reconstruction ◽  
Order Moment ◽  
Frequency Filtering ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Noise Environment ◽  
Frequency Representation ◽  
Short Time

The short time Fourier transform time-frequency representation (STFT-TFR) method degenerates, and the corresponding short time Fourier transform time-frequency filtering (STFT-TFF) method fails underαstable distribution noise environment. A fractional low order short time Fourier transform (FLOSTFT) which takes advantage of fractionalporder moment is proposed forαstable distribution noise environment, and the corresponding FLOSTFT time-frequency representation (FLOSTFT-TFR) algorithm is presented in this paper. We study vector formulation of the FLOSTFT and inverse FLOSTFT (IFLOSTFT) methods and propose a FLOSTFT time-frequency filtering (FLOSTFT-TFF) method which takes advantage of time-frequency localized spectra of the signal in time-frequency domain. The simulation results show that, employing the FLOSTFT-TFR method and the FLOSTFT-TFF method with an adaptive weight function, time-frequency distribution of the signals can be better gotten and time-frequency localized region of the signal can be effectively extracted fromαstable distribution noise, and also the original signal can be restored employing the IFLOSTFT method. Their performances are better than the STFT-TFR and STFT-TFF methods, and MSEs are smaller in differentαand GSNR cases. Finally, we apply the FLOSTFT-TFR and FLOSTFT-TFF methods to extract fault features of the bearing outer race fault signal and restore the original fault signal fromαstable distribution noise; the experimental results illustrate their performances.

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