Compressive Sampling for Detection of Frequency-Hopping Spread Spectrum Signals

Liu, Feng; Marcellin, Michael W.; Goodman, Nathan A.; Bilgin, Ali

doi:10.1109/tsp.2016.2597122

Cited by 39 publications

(21 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is observed that the dispread operation is the same as the spread operation. different carrier signals which are chosen randomly using PN sequence [11], [12]. FHSS is a wireless technology of CDMA scheme that distributes its signal over frequencies that are continuously varying.…”

Section: Spread Spectrum Sensing Techniques For Transformer Frequencymentioning

confidence: 99%

Spread Spectrum Sensing Techniques for Transformer Frequency Response Data

Reddy*¹,

Goel²

2019

IJITEE

View full text Add to dashboard Cite

With the advent of wireless communication, the problem of data security is of greatest interest. In this paper, spread spectrum techniques have been employed due to its advantages in providing data security. Further, the mechanical integrity of a transformer is investigated using an off-line diagnosis test called as Frequency Response Analysis (FRA). The FRA data of a transformer has been taken as the input signal which is being transmitted from the field where transformer is kept and received at the control room. Furthermore, Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping Spread Spectrum (FHSS) are employed on FRA data and analyzed the signals by transmitting over Additive white Gaussian noise (AWGN) and Rayleigh faded Channels.

show abstract

Section: Spread Spectrum Sensing Techniques For Transformer Frequencymentioning

confidence: 99%

Spread Spectrum Sensing Techniques for Transformer Frequency Response Data

Reddy*¹,

Goel²

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…If the channel condition is good and the SNR on the equivalent narrow band is relatively high, we prefer a low weighting factor to reduce power consumption. In addition to the channelized MWC, a compressive detection with random measurement kernels, which was proposed in previous study [28], was also implemented as a reference. Although the compressive detection method can only detect the presence of the FHSS signals and cannot find the specific channel, it serves as a benchmark.…”

Section: Channel Detectionmentioning

confidence: 99%

“…In [28], compressive detection strategies that sample the full FHSS spectrum in a compressive manner are proposed. This focuses on the use of random measurement kernels and specifically designed measurement kernels in the proposed architecture.…”

mentioning

confidence: 99%

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

2018

View full text Add to dashboard Cite

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.

show abstract

“…Many studies and research works on this topic have been performed since the introduction of CS in 2004 by Donoho, Candès, Romberg, and Tao [29], [30], [31]. Thus, a method for detecting wideband FH signals with a tiny number of incoherent measurements is presented in [32], whereas different methods for detecting FHSS signals from compressive measurements are proposed in [33] and [34]. Finally, a closely related research work [35], which is considered a reference throughout this paper, relies on a Viterbi sequence detector (VD) to recover the transmitted bits from compressively sampled FHSS-GFSK signals.…”

Section: Introductionmentioning

confidence: 99%

Non-Cooperative Low-Complexity Detection Approach for FHSS-GFSK Drone Control Signals

Mototolea

Youssef

Rădoi

et al. 2020

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

The commercial drone market has substantially grown over the past few years. While providing numerous advantages in various fields and applications, drones also provide ample opportunities for misuse by irresponsible hobbyists or malevolent actors. The increasing number of safety/security incidents in which drones are involved has motivated researchers to find new and ingenious ways to detect, locate and counter this type of vehicles. In this paper, we propose a new method to detect frequency hopping spread spectrum-Gaussian frequency-shift keying (FHSS-GFSK) drone communication signals, in a non-cooperative scenario, where no prior information about the signals of interest is available. The system is designed to detect and retrieve data bit sequences through a compressive sampling approach, which includes the extraction of the reduced spectral information and a soft detection algorithm. The performance of the proposed approach is assessed in terms of bit error rate and compared with that of a Viterbi detector and a neural network-based detector. The effectiveness of the method described in the paper highlights the fact that current UAV communications are not infallible and present real security issues. INDEX TERMS Counter-drone measures, FHSS-GFSK communication signals, non-cooperative data detection, compressive sampling, security of UAV communications.

show abstract

Compressive Sampling for Detection of Frequency-Hopping Spread Spectrum Signals

Cited by 39 publications

References 42 publications

Spread Spectrum Sensing Techniques for Transformer Frequency Response Data

Spread Spectrum Sensing Techniques for Transformer Frequency Response Data

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

Non-Cooperative Low-Complexity Detection Approach for FHSS-GFSK Drone Control Signals

Contact Info

Product

Resources

About