Data filtering based maximum likelihood forgetting factor stochastic gradient parameter estimation algorithm for feedback nonlinear systems with colored noise

2017 ◽  
Author(s):  
Junhong Li ◽  
Xiao Li ◽  
Jiali Zhang
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Nonlinear Systems ◽  
Colored Noise ◽  
Estimation Algorithm ◽  
Stochastic Gradient ◽  
Data Filtering ◽  
Forgetting Factor ◽  
Parameter Estimation Algorithm ◽  
Gradient Parameter

scholarly journals Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Weili Xiong ◽  
Wei Fan ◽  
Rui Ding
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Least Squares ◽  
Moving Average ◽  
Estimation Algorithm ◽  
Autoregressive Moving Average ◽  
Parameter Estimates ◽  
Persistent Excitation ◽  
Estimation Algorithms ◽  
Parameter Estimation Algorithm

This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive (IN-CAR) model and the input nonlinear controlled autoregressive autoregressive moving average (IN-CARARMA) model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algorithm to estimate the unknown parameter vectors. It is proved that the parameter estimates consistently converge to their true values under the persistent excitation condition. A simulation example is provided.

scholarly journals Fast Implementation of Approximated Maximum Likelihood Parameter Estimation for Frequency Agile Radar under Jamming Environment

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2022
Author(s):  
Yang Zhao ◽  
Jianxin Wu ◽  
Zhiyong Suo ◽  
Xiaoyu Liu
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Order Approximation ◽  
Estimation Algorithm ◽  
Adaptive Beamforming ◽  
Computationally Efficient ◽  
Fine Grid ◽  
Frequency Agile ◽  
Parameter Estimation Algorithm ◽  
Deceptive Jamming

A computationally efficient target parameter estimation algorithm for frequency agile radar (FAR) under jamming environment is developed. First, the barrage noise jamming and the deceptive jamming are suppressed by using adaptive beamforming and frequency agility. Second, the analytical solution of the parameter estimation is obtained by a low-order approximation to the multi-dimensional maximum likelihood (ML) function. Due to that, fine grid-search (FGS) is avoided and the computational complexity is greatly reduced.

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