A Parameter Estimation Algorithm for Multiple Frequency‐Hopping Signals Based on Sparse Bayesian Method

Abstract

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.

Parameter estimation of multiple frequency‐hopping signals based on Bayesian compressive sensing and atomic norm soft thresholding with missing observations

“…We notice that the key to the signal segmentation method mentioned above lies in accurately estimating the hopping time. Although the frequency of the received signals can be roughly estimated by the phase difference of the measured data [28], this approach is challenging to apply to the situation where observations are randomly missing. In this study, we determine the hopping time by estimating the change in the spatial frequency.…”

A Low-Signal-to-Noise Ratio Estimation Algorithm for Multipath Channels

“…Based on M2M4 algorithm, Yang et al [19] proposed a deep learning algorithm for SNR estimation, and verified the performance and robustness through experiments; the proposed algorithm is suitable for baseband signals and incoherent signals; compared with the M2M4, the proposed algorithm has a large application range of modulation method, and estimates the SNR in the middle layer in an accurate manner. Zhang et al [20] introduced the derivation and details of the FDE: the noise energy is obtained through spectral segmentation, and used to calculate the SNR; the algorithm performs poorly if the SNR is very low. Han et al [21] presents an SNR algorithm that estimates noise power based on the variance of the phase change in received signal, but the algorithm only applies to phase modulated signals.…”