Compressive sensing based adaptive synchronous estimation for Frequency Hopping signal

Li, Binwu; Li, Yonggui; Zhu, Yunmin; Niu, Yingtao; Gao, Zezhong

doi:10.1109/mec.2013.6885202

Cited by 1 publication

(1 citation statement)

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“…In [20], Yidong Zhang focused on studying of the digital domain compressive sampling and reconstruction algorithm of FHSS signals by using the basic principles and methods of compressed sensing. In [21], Binwu Li proposed a novel adaptive synchronous estimation method of hopping frequency for FHSS signals without reconstructing the original signal. Several methods for compressive detection were proposed, which assume that the signal can be sparsely represented in the known dictionaries [22,23].…”

Section: Introductionmentioning

confidence: 99%

Detection and Frequency Estimation of Frequency Hopping Spread Spectrum Signals Based on Channelized Modulated Wideband Converters

2018

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It is challenging to detect and track frequency hopping spread spectrum (FHSS) signals due to their wideband frequencies and the limitations of current hardware. In the implementation, there has been a trend of conducting compressive sensing for blind signal processing of FHSS signals. The modulated wideband converter (MWC) is a type of sub-Nyquist sampling system, which accomplishes the recovery of highly accurate broadband sparse signals by multichannel sub-Nyquist sampling sequences. However, it is difficult to adjust MWC to FHSS signals, because the support set and sparsity change with the hop. In this paper, we propose a channelized MWC scheme in order to solve these problems. First, the proposed method distributes the sub-bands to different channels. We can derive and refresh the frequency support set rapidly without recovery. Secondly, by reconstructing the low-pass filter and decimation, we reduced the computational cost to 1/m as the traditional m-channel MWC scheme, where m is the number of channels. Moreover, we propose a series of strategies to estimate carrier frequency. The numerical simulations show that our method can detect the channel, which contains FHSS signals in the case of a low signal-to-noise ratio. Furthermore, the estimation method leads to the successful estimation of the FHSS carrier frequency. This indicates that our method is also effective in the broadband non-cooperative spectrum sensing.

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