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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 6129120, 9 pages
https://doi.org/10.1155/2017/6129120
Research Article

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

Institute of Information and Navigation, Air Force Engineering University, Xi’an, Shanxi 710077, China

Correspondence should be addressed to Kun-feng Zhang; moc.oohay@gnahz_gnefnuk

Received 26 February 2017; Revised 1 June 2017; Accepted 28 June 2017; Published 6 September 2017

Academic Editor: Zhike Peng

Copyright © 2017 Kun-feng Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.