Blind Parameter Estimation of Frequency-Hopping Signals Based on Atomic Decomposition

2009 ◽  
Author(s):  
Haining Fan ◽  
Ying Guo ◽  
Qingwei Meng
Keyword(s):  
Parameter Estimation ◽  
Atomic Decomposition ◽  
Frequency Hopping ◽  
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.

scholarly journals Terahertz-Band MIMO Systems: Adaptive Transmission and Blind Parameter Estimation

2020 ◽  
pp. 1-1
Author(s):  
Mohamed Habib Loukil ◽  
Hadi Sarieddeen ◽  
Mohamed-Slim Alouini ◽  
Tareq Y. Al-Naffouri
Keyword(s):  
Parameter Estimation ◽  
Mimo Systems ◽  
Adaptive Transmission ◽  
Terahertz Band ◽  
Blind Parameter Estimation
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