scholarly journals Retraction Notice: Parameter Estimation of Fractional Low Order Time-frequency Autoregressive Based on Infinite Variance Analysis

2016 ◽  
Vol 8 (1) ◽  
pp. 103-103
Author(s):  
Cao Ying ◽  
Yuan Qingshan ◽  
Zeng Lili
Keyword(s):  
Parameter Estimation ◽  
Variance Analysis ◽  
Infinite Variance ◽  
Time Frequency ◽  
Retraction Notice ◽  
Low Order

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.

scholarly journals Wavelet multi-resolution approximation of time-varying frame structure

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879559 ◽  
Author(s):  
Min Xiang ◽  
Feng Xiong ◽  
Yuanfeng Shi ◽  
Kaoshan Dai ◽  
Zhibin Ding
Keyword(s):  
Parameter Estimation ◽  
Degrees Of Freedom ◽  
Structural Parameters ◽  
Identification Problem ◽  
Frame Structure ◽  
Structural Parameter ◽  
Time Varying ◽  
Engineering Structures ◽  
Time Frequency ◽  
Non Parametric

Engineering structures usually exhibit time-varying behavior when subjected to strong excitation or due to material deterioration. This behavior is one of the key properties affecting the structural performance. Hence, reasonable description and timely tracking of time-varying characteristics of engineering structures are necessary for their safety assessment and life-cycle management. Due to its powerful ability of approximating functions in the time–frequency domain, wavelet multi-resolution approximation has been widely applied in the field of parameter estimation. Considering that the damage levels of beams and columns are usually different, identification of time-varying structural parameters of frame structure under seismic excitation using wavelet multi-resolution approximation is studied in this article. A time-varying dynamical model including both the translational and rotational degrees of freedom is established so as to estimate the stiffness coefficients of beams and columns separately. By decomposing each time-varying structural parameter using one wavelet multi-resolution approximation, the time-varying parametric identification problem is transformed into a time-invariant non-parametric one. In solving the high number of regressors in the non-parametric regression program, the modified orthogonal forward regression algorithm is proposed for significant term selection and parameter estimation. This work is demonstrated through numerical examples which consider both gradual variation and abrupt changes in the structural parameters.

scholarly journals Rotating Parabolic-Reflector Antenna Target in SAR Data: Model, Characteristics, and Parameter Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Bin Deng ◽  
Hong-Qiang Wang ◽  
Yu-Liang Qin ◽  
Sha Zhu ◽  
Xiang Li
Keyword(s):  
Parameter Estimation ◽  
Data Model ◽  
Target Recognition ◽  
Image Interpretation ◽  
Likelihood Estimator ◽  
Parabolic Reflector ◽  
Time Frequency ◽  
Aspect Angle ◽  
Multiple Parameters ◽  
Scattering Center

Parabolic-reflector antennas (PRAs), usually possessing rotation, are a particular type of targets of potential interest to the synthetic aperture radar (SAR) community. This paper is aimed to investigate PRA’s scattering characteristics and then to extract PRA’s parameters from SAR returns, for supporting image interpretation and target recognition. We at first obtain both closed-form and numeric solutions to PRA’s backscattering by geometrical optics (GO), physical optics, and graphical electromagnetic computation, respectively. Based on the GO solution, a migratory scattering center model is at first presented for representing the movement of the specular point with aspect angle, and then a hybrid model, named the migratory/micromotion scattering center (MMSC) model, is proposed for characterizing a rotating PRA in the SAR geometry, which incorporates PRA’s rotation into its migratory scattering center model. Additionally, we in detail analyze PRA’s radar characteristics on radar cross-section, high-resolution range profiles, time-frequency distribution, and 2D images, which also confirm the models proposed. A maximal likelihood estimator is developed for jointly solving the MMSC model for PRA’s multiple parameters by optimization. By exploiting the aforementioned characteristics, the coarse parameter estimation guarantees convergency upon global minima. The signatures recovered can be favorably utilized for SAR image interpretation and target recognition.

Radar Echo Parameter Estimation Using Sparse Time-Frequency Analysis Method

2014 ◽  
Vol 543-547 ◽  
pp. 2229-2233
Author(s):  
Hao Chen ◽  
Jun Hai Guo
Keyword(s):  
Parameter Estimation ◽  
Frequency Analysis ◽  
Computational Cost ◽  
Velocity Estimation ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Adaptive Data Analysis

The echoes of pulse radar from maneuvering targets are amplitude modulation and frequency modulation (AM-FM) signal. At present, the methods of estimating parameters of AM-FM signal are time-frequency analysis method, empirical mode decomposition and empirical wavelet transform based adaptive data analysis methods. This paper takes the idea of intrinsic mode function in guessing the initial phase, and applies the newly developed sparse time-frequency analysis method in AM-FM signal parameter estimation. Simulation results show that the estimating performance of this method in AM-FM signal is good under different SNR and it has low computational cost, and this method is applicable in target acceleration and velocity estimation.

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