Frequency hopping signal parameter estimation algorithm based on time-frequency point correlation

Jiang, Zhiye; Wang, Shangyue; Chen, Yang; Wang, Peng

doi:10.1109/itaic54216.2022.9836485

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…The authors showed the importance of the parametric approach in their research field [27]. Jiang et al proposed a special signal parameter estimation algorithm that depends on a correlation [28]. The authors proposed this model for highly sensitive signal processing events such as military communications [28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Jiang et al proposed a special signal parameter estimation algorithm that depends on a correlation [28]. The authors proposed this model for highly sensitive signal processing events such as military communications [28]. In another study, authors compared methodologies to find the best method to improve parameter estimations in lines [29].…”

Section: Literature Reviewmentioning

confidence: 99%

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A Method to Improve Parameter Estimation Success

Unozkan

2023

IEEE Access

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This paper introduces a method for improving parameter estimation in statistical models. Parameter estimation is a popular area of study in statistics, and recent years have seen the introduction of new distributions with more parameters to enhance modelling success. While finding a suitable model for a dataset is crucial, accurately estimating parameters is equally important. In some cases, classical parameter estimation methods fail to provide a closed form of estimation for parameters. As a result, researchers commonly resort to numerical methods and software programs for parameter estimation in models. The success rates of models have gained significance with the rising popularity of novel techniques like machine learning algorithms and artificial neural networks. Robust and reliable models are built on the strong theoretical foundations of statistical distributions. Specific distributions are used in various research fields to model datasets, and the assumptions associated with these distributions provide valuable insights into observations. Additionally, parameter estimation results sometimes lead researchers to direct conclusions. This paper presents an improvement method that relies on the estimation of parameters from other statistical distributions. This novel approach aims to make parameter estimation easier and more successful in certain situations. In the applications in this paper, the proposed methodology improves the success rate by up to 10% which provides an additional 6% success in the models.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Method to Improve Parameter Estimation Success

Unozkan

2023

IEEE Access

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“…For example, in 2019, Wan et al [12] proposed a blind parameter estimation algorithm of FHSS signals based on space-time frequency analysis and matrix joint diagonalization. More recently, Jiang et al [13] designed a kernel function of the time-frequency transform to obtain the time-frequency distribution of the FHSS signal, as well as a model to extract the frequency-hopping ridge, which was used to estimate the signal parameters. In addition to the transform-based methods, energy-or statistical property-based methods, such as the channelized energy thresholding-based [14][15][16], subband occupation likelihood analysis-based [17,18] and spectrum analyzer-based [19][20][21] methods, are also implemented in FHSS signal parameter estimations, especially in carrier estimations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning

Jiang,

Liu

2024

Entropy

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As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature.

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“…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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Frequency hopping signal parameter estimation algorithm based on time-frequency point correlation

Cited by 4 publications

References 2 publications

A Method to Improve Parameter Estimation Success

A Method to Improve Parameter Estimation Success

Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning

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

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