scholarly journals A Novel Parameter Estimation Method Based on a Tuneable Sigmoid in Alpha-Stable Distribution Noise Environments

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3012 ◽  
Author(s):  
Li Li ◽  
Nicolas Younan ◽  
Xiaofei Shi
Keyword(s):  
Parameter Estimation ◽  
Time Delay ◽  
Stable Distribution ◽  
Fractional Fourier Transform ◽  
Impulsive Noise ◽  
A Priori ◽  
Estimation Method ◽  
Fractional Power ◽  
Estimation Accuracy ◽  
Alpha Stable Distribution

In this paper, a novel method, that employs a fractional Fourier transform and a tuneable Sigmoid transform, is proposed, in order to estimate the Doppler stretch and time delay of wideband echoes for a linear frequency modulation (LFM) pulse radar in an alpha-stable distribution noise environment. Two novel functions, a tuneable Sigmoid fractional correlation function (TS-FC) and a tuneable Sigmoid fractional power spectrum density (TS-FPSD), are presented in this paper. The novel algorithm based on the TS-FPSD is then proposed to estimate the Doppler stretch and the time delay. Then, the derivation of unbiasedness and consistency is presented. Furthermore, the boundness of the TS-FPSD to the symmetric alpha stable ( S α S ) noise, the parameter selection of the TS-FPSD, and the feasibility analysis of the TS-FPSD, are presented to evaluate the performance of the proposed method. In addition, the Cramér–Rao bound for parameter estimation is derived and computed in closed form, which shows that better performance has been achieved. Simulation results and theoretical analysis are presented, to demonstrate the applicability of the forgoing method. It is shown that the proposed method can not only effectively suppress impulsive noise interference, but it also does not need a priori knowledge of the noise with higher estimation accuracy in alpha-stable distribution noise environments.

scholarly journals Joint Estimation of Doppler Stretch and Time Delay of Wideband Echoes for LFM Pulse Radar Based on Sigmoid-FRFT Transform under the Impulsive Noise Environment

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 121 ◽  
Author(s):  
Li Li ◽  
Nicolas Younan ◽  
Xiaofei Shi
Keyword(s):  
Fourier Transform ◽  
Time Delay ◽  
Fractional Fourier Transform ◽  
Impulsive Noise ◽  
A Priori ◽  
Joint Estimation ◽  
Estimation Accuracy ◽  
A Priori Knowledge ◽  
Noise Interference ◽  
Priori Knowledge

To overcome the limitation of performance degradation of existing methods based on fractional Fourier transform in impulsive noise, and fractional lower-order statistics based method dependence on a priori knowledge of the noise, a novel Sigmoid fractional Fourier transform (Sigmoid-FRFT) is presented in this paper. This novel approach is then used to estimate the Doppler stretch and time delay. Furthermore, the properties of the Sigmoid transform, robustness and boundedness of the Sigmoid-FRFT to the S α S noise, and the computation complexity of the Sigmoid-FRFT method are presented to evaluate the performance of the proposed method. Simulation results and theoretical analysis are presented to demonstrate the applicability of the forgoing method. It is shown that the proposed method not only can effectively suppress impulsive noise interference but also does not need a priori knowledge of the noise, with higher estimation accuracy and lower computational complexity in impulsive noise environments.

scholarly journals A model-free hull deformation measurement method with time delay compensation

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881069 ◽  
Author(s):  
Ying He ◽  
Xiafu Peng ◽  
Xiaoli Zhang ◽  
Xiaoqiang Hu
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Deformation Measurement ◽  
Estimation Accuracy ◽  
Deformation Model ◽  
Delay Compensation ◽  
The Neural Network ◽  
Time Delay Compensation

Estimation and compensation for hull deformation is an indispensable step for the ship to establish a unified space attitude. The existing hull deformation measurement methods are dependent on the pre-established deformation model, and an inaccurate deformation model will reduce the deformation estimation accuracy. To solve this problem, a hull deformation estimation method without deformation model is proposed in this article, which utilizes the neural network to fit the hull deformation. To train the neural network online, connection weights of the neural network are regarded as system state variables which can be estimated by the Unscented Kalman Filter. Simultaneously, considering the time delay problem of inertial data, a time delay compensation method based on the quaternion attitude matrix is proposed. The simulation results show that the proposed method can obtain high estimation accuracy without any deformation model even when the inertial data are asynchronous.

scholarly journals A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Shaolong Chen ◽  
Renyu Yang ◽  
Renhuan Yang ◽  
Liu Yang ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Fitness Function ◽  
System Modeling ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Function Parameter ◽  
Swarm Optimization ◽  
Modeling And Control ◽  
Chicken Swarm Optimization

Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO) has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO) is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.

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