Hopping Time Estimation of Frequency-Hopping Signals Based on HMM-Enhanced Bayesian Compressive Sensing With Missing Observations

Wang, Hongbin; Zhang, Bangning; Wang, Heng; Wu, Binbin

doi:10.1109/lcomm.2022.3184173

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Підсумовуючи, адаптивне стрибкоподібне перемикання частоти дозволяє БпЛА уникати перевантажених або шумних каналів, забезпечуючи надійну передачу даних [20]. Адаптивність, можливості співіснування та енергоефективність АРЧ роблять його важливою особливістю сучасних реалізацій завадозахищеного зв'язку.…”

Section: програмна частина 221 компоненти радіозв'язкуunclassified

Analysis of the Possibility of Development and Implementation of a Basic Unmanned Aircraft System

Los,

Rozhenkov,

Zhyrna

et al. 2023

dndivsovt

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The term “Basic Unmanned Aircraft System” refers to a comprehensive system encompassing both hardware and software components, forming an integral configuration aimed at unmanned aviation operations. The Unmanned Aircraft System, in its fundamental assembly, comprises an intricate interplay of technological components. These components, ranging from cutting-edge hardware to sophisticated software, collaborate harmoniously to execute tasks that were traditionally accomplished by manned aircraft. Collaboration between specialized enterprises and research institutions approach holds promise in terms of synergizing the respective expertise of these entities. This symbiotic partnership not only expedites the development process but also introduces the prospect of novel breakthroughs through the fusion of practical experience and theoretical knowledge. A paramount consideration is the host of advantages that could potentially be reaped from the realization of the Basic System. One such advantage is the projected increase in production volumes. This, in turn, could significantly bolster the capacity to meet defense orders in a shorter timeframe, thereby enhancing national security endeavors. Furthermore, the article delves into the catalytic effect this innovation could have on scientific research, particularly in prioritized domains. The development of the Basic System could potentially serve as a stimulus for intensified scientific inquiry. The complexity of its design necessitates advances in a multitude of scientific fronts, such as materials science, artificial intelligence, and robotics.

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Section: програмна частина 221 компоненти радіозв'язкуunclassified

Analysis of the Possibility of Development and Implementation of a Basic Unmanned Aircraft System

Los,

Rozhenkov,

Zhyrna

et al. 2023

dndivsovt

View full text Add to dashboard Cite

show abstract

“…Furthermore, the FH signal is time-varying, making it difficult to describe its changing patterns visually and to identify FH signals from interferences using traditional time-domain or frequencydomain analysis, especially in complex EMI environment. Fortunately, approaches based on time-frequency analysis (TFA) enable the visualization of signal characteristics in the time-frequency (TF) domain, providing an effective solution for this problem [18], [19], [20], [21], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet

et al. 2023

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Accurate detection and parameter estimation of frequency hopping (FH) signals remain challenging in FH signal-based transmission systems. This study proposes a scheme combining time-frequency analysis (TFA) and deep learning (DL)-based image processing algorithms to alleviate the degradation of detection accuracy and estimation performance caused by complex electromagnetic interference (EMI). A short-time Fourier transform (STFT) was used to obtain the signal spectrogram, which reflects the signal energy in a concentration-dependent manner. Then, a CenterNet-based deep network was employed to identify each FH hop's shape and position, reducing the computational burden via a lightweight neural network while maintaining high recognition accuracy. Inverse mapping from the coordinates to the spectrogram was used to perform parameter estimation in the time-frequency (TF) domain. The estimation error was reduced by precisely locating the centroid of the signal energy using CenterNet. The simulation results demonstrate that the proposed scheme can accurately estimate the FH signal at a low signal-to-noise ratio (SNR) with complex EMI. Furthermore, appropriately determining the optimal parameters of CenterNet to ensure the estimator performance provides a novel approach for integrating DL into signal detection and estimation in complex EMI environments.

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“…Sparse reconstruction is a traditional processing way in the theory of compressed sensing [10], the algorithm of reconstruction not only increases the amount of computation but is also sensitive to noise. To overcome the shortcomings of sparse reconstruction, some scholars have proposed algorithms for estimating FH signal parameters directly in the compressed domain using the sparse characteristics of FH signal [11][12][13][14][15][16][17][18][19][20][21][22]. Zhang et al established a redundant atomic dictionary to estimate hopping time accurately [23], which reduces the computational complexity compared to the study in [9].…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation Algorithm of Frequency-Hopping Signal in Compressed Domain Based on Improved Atomic Dictionary

Zhu

Wang

et al. 2023

Sensors

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This paper considers the problem of estimating the parameters of a frequency-hopping signal under non-cooperative conditions. To make the estimation of different parameters independently of each other, a compressed domain frequency-hopping signal parameter estimation algorithm based on the improved atomic dictionary is proposed. By segmenting and compressive sampling the received signal, the center frequency of each signal segment is estimated using the maximum dot product. The signal segments are processed with central frequency variation using the improved atomic dictionary to accurately estimate the hopping time. We highlight that one superiority of the proposed algorithm is that high-resolution center frequency estimation can be directly obtained without reconstructing the frequency-hopping signal. Additionally, another superiority of the proposed algorithm is that hopping time estimation has nothing to do with center frequency estimation. Numerical results show that the proposed algorithm can achieve superior performance compared with the competing method.

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Hopping Time Estimation of Frequency-Hopping Signals Based on HMM-Enhanced Bayesian Compressive Sensing With Missing Observations

Cited by 7 publications

References 15 publications

Analysis of the Possibility of Development and Implementation of a Basic Unmanned Aircraft System

Analysis of the Possibility of Development and Implementation of a Basic Unmanned Aircraft System

Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet

Parameter Estimation Algorithm of Frequency-Hopping Signal in Compressed Domain Based on Improved Atomic Dictionary

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