A parameter estimation algorithm for multiple frequency-hopping signals based on compressed sensing

2019 ◽  
Vol 37 ◽  
pp. 100892
Author(s):  
Weihong Fu ◽  
Ting Jiang
Keyword(s):  
Parameter Estimation ◽  
Compressed Sensing ◽  
Estimation Algorithm ◽  
Frequency Hopping ◽  
Multiple Frequency ◽  
Parameter Estimation Algorithm

scholarly journals A Parameter Estimation Algorithm for Multiple Frequency-Hopping Signals Based on Sparse Bayesian Method

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Kun-feng Zhang ◽  
Ying Guo ◽  
Zisen Qi
Keyword(s):  
Parameter Estimation ◽  
Spatial Frequency ◽  
Prior Information ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Frequency Hopping ◽  
Blind Estimation ◽  
Filter Methods ◽  
Challenging Tasks ◽  
Parameter Estimation Algorithm

Parameter estimation and network sorting for noncooperative wideband frequency-hopping (FH) signals have been essential and challenging tasks, especially in the case with little or even no prior information at all. In this paper, we propose a nearly blind estimation approach to estimate signal parameters based on sparse Bayesian reconstruction. Taking the sparsity in the spatial frequency domain of multiple FH signals into account, we propose a sparse Bayesian algorithm to estimate the spatial frequency parameters. As a result, the frequency and direction of arrival (DOA) parameters can be obtained. In order to improve the accuracy of the estimation parameters, we employ morphological filter methods to further clean the data poisoned by the noise. Moreover, our method is applicable to the wideband signal models with little prior information. We also conduct extensive numerical simulations to verify the performance of our method. Notably, the proposed method works well even in low signal-to-noise ratio (SNR) environment.

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