Sparse Learning of Higher-Order Statistics for Communications and Sensing

Sun, Zhuo; Kong, Song; Wang, Wenbo

doi:10.1109/tetci.2018.2872404

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Bispectral analysis, as a higher-order spectrum analysis with the lowest order, shows superiority in addressing non-Gaussian and non-stationary signals. The bispectral analysis of a signal is essentially a two-dimensional Fourier transform of the third-order cumulative quantity of the signal that can be expressed as follows [28]:…”

Section: Signal Preprocessing a Bispectral Analysismentioning

confidence: 99%

A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

Xie¹,

Zhang²,

Zhong

2023

IEEE Access

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Obtaining larger category-label-containing training signal datasets in non-cooperative scenarios is difficult. Moreover, employing smaller labeled signal datasets for specific emitter identification is technically challenging. Therefore, we propose a novel method for few-shot SEI. We first design a bispectral analysis and Radon transformation-based signal preprocessing scheme to obtain feature vectors that effectively characterize the radio frequency fingerprints. The feature vectors are then fed to a network model for feature learning. Moreover, a meta-learning algorithm is applied to the network model to adapt to few-shot feature learning. The conventional meta-learning algorithm is improved to develop a novel algorithm involving latent embedding optimization for meta-learning. The proposed method extracts lowdimensional key features from high-dimensional input data and evaluates the distance and degree of feature dispersion. The resulting information is employed in sample point prediction. The algorithm effectively achieves few-shot SEI and offers stable and efficient recognition after training with a minimum of forty samples. This method identifies emitter individuals under multiple modulation types and exhibits scalability in identifying the emitter numbers. Moreover, it offers adaptability in identifying the emitter individuals under multiple propagation channel types.

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Section: Signal Preprocessing a Bispectral Analysismentioning

confidence: 99%

A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

Xie¹,

Zhang²,

Zhong

2023

IEEE Access

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“…To reduce the computational complexity, the generalization of co-prime sampling for HOS estimation is studied in [21]. In [22], sparse representation of HOS is explored. However, the sampler design, the HOS recovery guarantees and the achievable sampling rate reduction are not considered in [21] and [22].…”

Section: Introductionmentioning

confidence: 99%

“…In [22], sparse representation of HOS is explored. However, the sampler design, the HOS recovery guarantees and the achievable sampling rate reduction are not considered in [21] and [22].…”

Section: Introductionmentioning

confidence: 99%

Third-Order Statistics Reconstruction from Compressive Measurements

Wang,

Tian

2020

Preprint

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Estimation of third-order statistics relies on the availability of a huge amount of data records, which can pose severe challenges on the data collecting hardware in terms of considerable storage costs, overwhelming energy consumption, and unaffordably high sampling rate especially when dealing with high-dimensional data such as wideband signals. To overcome these challenges, this paper focuses on the reconstruction of the third-order cumulants under the compressive sensing framework. Specifically, this paper derives a transformed linear system that directly connects the cross-cumulants of compressive measurements to the desired third-order statistics. We provide sufficient conditions for lossless third-order statistics reconstruction via solving simple least-squares, along with the strongest achievable compression ratio. To reduce the computational burden, we also propose an approach to recover diagonal cumulant slices directly from compressive measurements, which is useful when the cumulant slices are sufficient for the inference task at hand. All the proposed techniques are tested via extensive simulations. The developed joint sampling and reconstruction approach to third-order statistics estimation is able to reduce the required sampling rates significantly by exploiting the cumulant structure resulting from signal stationarity, even in the absence of any sparsity constraints on the signal or cumulants.

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Third-Order Statistics Reconstruction From Compressive Measurements

Tian

2021

IEEE Trans. Signal Process.

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Sparse Learning of Higher-Order Statistics for Communications and Sensing

Cited by 3 publications

References 24 publications

A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

Third-Order Statistics Reconstruction from Compressive Measurements

Third-Order Statistics Reconstruction From Compressive Measurements

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