scholarly journals A new method of hybrid frequency hopping signals selection and blind parameter estimation

2018 ◽  
Author(s):  
Xiaoyu Zeng ◽  
Wencheng Jiao ◽  
Huixian Sun
Keyword(s):  
Parameter Estimation ◽  
Frequency Hopping ◽  
New Method ◽  
Hybrid Frequency ◽  
Blind Parameter Estimation

scholarly journals A Blind Parameter Estimation Method of Frequency Hopping Signal with Low SNR

2021 ◽  
Vol 15 ◽  
pp. 248-253
Author(s):  
Zengke Wang ◽  
Yi Li ◽  
Wei Xu
Keyword(s):  
Parameter Estimation ◽  
Simulation Experiment ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Frequency Hopping ◽  
Cross Term ◽  
Low Snr ◽  
Parameter Estimation Method ◽  
Estimate Frequency ◽  
Blind Parameter Estimation

In order to effectively estimate the parameters of the frequency hopping signals under low signal-to-noise ratio (SNR), a blind parameter estimation method based on the modified discrete time Wigner-Ville distribution (MDTWVD) is proposed. We choose a low order Chebyshev polynomial as the kernel function for reducing the cross-term. Then, the parameters of the frequency hopping signals are finally obtained from the MDTWVD. The simulation experiment results show that the method used in this paper can effectively and accurately estimate frequency hopping signals parameters, especially under low SNR condition compared with other estimating methods.

Time-Frequency Analysis of Frequency Hopping Signals Based on Particle Swarm Optimization

2012 ◽  
Vol 195-196 ◽  
pp. 265-269
Author(s):  
Jian Tao Guo
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Frequency Hopping ◽  
Parameter Estimation Problem ◽  
Swarm Optimization ◽  
Time Frequency ◽  
Simulation Results ◽  
Blind Parameter Estimation

A new method is proposed for blind parameter estimation of frequency hopping signals. According to the relation between peaks location on the time frequency plane and component centers of frequency hopping signals, parameter estimation problem is solved using multi-species particle swarm optimization algorithm. Each particle moves around the time and frequency plane and will converge to different species, which species seed represents the center of frequency hopping component. Using this method, the parameters of frequency hopping signals can be estimated. Simulation results demonstrate that the method is effective and feasible.

Estimation of Failure Probability and its Applications in Reliability Data Analysis

2010 ◽  
Vol 118-120 ◽  
pp. 601-605
Author(s):  
Han Ming
Keyword(s):  
Parameter Estimation ◽  
Data Analysis ◽  
Bayesian Estimation ◽  
Failure Probability ◽  
Evaluation Method ◽  
Estimation Method ◽  
New Method ◽  
Reliability Parameter ◽  
Hierarchical Bayesian Estimation ◽  
Definition Of

Evaluation method of reliability parameter estimation needs to be improved effectively with the advance of science and technology. This paper develops a new method of parameter estimation, which is named E-Bayesian estimation method. In the case one hyper-parameter, the definition of E-Bayesian estimation of the failure probability is provided, moreover, the formulas of E-Bayesian estimation and hierarchical Bayesian estimation, and the property of E-Bayesian estimation of the failure probability are also provided. Finally, calculation on practical problems shows that the provided method is feasible and easy to perform.

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonia Goel ◽  
Meena Tushir
Keyword(s):  
Parameter Estimation ◽  
Missing Data ◽  
Incomplete Data ◽  
Model Identification ◽  
Fuzzy Model ◽  
New Method ◽  
Content Type ◽  
Ubiquitous Environment ◽  
Sugeno Model ◽  
Takagi Sugeno

Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

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