A Novel Algorithm of Blind Detection of Frequency Hopping Signal Based on Second-Order Cyclostationarity

Fan, Haining; Guo, Ying; Xu, Yaohua

doi:10.1109/cisp.2008.266

Cited by 17 publications

(7 citation statements)

References 9 publications

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“…As described in Figure 2, the architecture of the proposed DNNs is fully connected. The nodes in the input layer represent posterior probabilities of the subband usage given the designed coefficients in the measurement matrix and the measured results in the signal present case, i.e., P r (15). The width of the input layer is equal to the number of subbands divided in the moG model of the FHSS signals.…”

Section: The Structure and The Training Of The Deep Neural Networkmentioning

confidence: 99%

“…The first measurement result is obtained using the initialized measurement kernel. Then the posterior probabilities of the subband usages are calculated according to Equation (15), and passed to the trained neural network to design the measurement kernel for the next measurement. In this manner, the measurements, the subband usage posterior probability updates and the measurement kernel design steps with the DNN are done sequentially and iteratively until the entire measurement procedure is finished.…”

Section: Combination Of Knowledge-enhanced Compressed Detection Archi...mentioning

confidence: 99%

“…The non-cooperative detection of the FHSS signal is the first step of the entire signal interception procedure [2]. Although various methods has been rendered since the 1990s (e.g., methods based on time-frequency analysis [3][4][5][6][7][8], wavelet analysis [4,[9][10][11][12][13], auto-correlation analysis [9,[14][15][16], likelihood analysis [17][18][19][20][21], etc. ), energy thresholding is the most commonly used in FHSS signal detection [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Knowledge-Enhanced Compressed Measurements for Detection of Frequency-Hopping Spread Spectrum Signals Based on Task-Specific Information and Deep Neural Networks

Liu

Jiang

2022

Entropy

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The frequency-hopping spread spectrum (FHSS) technique is widely used in secure communications. In this technique, the signal carrier frequency hops over a large band. The conventional non-compressed receiver must sample the signal at high rates to catch the entire frequency-hopping range, which is unfeasible for wide frequency-hopping ranges. In this paper, we propose an efficient adaptive compressed method to measure and detect the FHSS signals non-cooperatively. In contrast to the literature, the FHSS signal-detection method proposed in this paper is achieved directly with compressed sampling rates. The measurement kernels (the non-zero coefficients in the measurement matrix) are designed adaptively, using continuously updated knowledge from the compressed measurement. More importantly, in contrast to the iterative optimizations of the measurement matrices in the literature, the deep neural networks are trained once using task-specific information optimization and repeatedly implemented for measurement kernel design, enabling efficient adaptive detection of the FHSS signals. Simulations show that the proposed method provides stably low missing detection rates, compared to the compressed detection with random measurement kernels and the recently proposed method. Meanwhile, the measurement design in the proposed method is shown to provide improved efficiency, compared to the commonly used recursive method.

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Section: The Structure and The Training Of The Deep Neural Networkmentioning

confidence: 99%

Section: Combination Of Knowledge-enhanced Compressed Detection Archi...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Knowledge-Enhanced Compressed Measurements for Detection of Frequency-Hopping Spread Spectrum Signals Based on Task-Specific Information and Deep Neural Networks

Liu

Jiang

2022

Entropy

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“…It has the advantages of strong interpretability, fast calculation and no need for large-scale labeled data. It mainly includes channelized radiometer method [1][2] [3] , autocorrelation method [4] [5] , match filter method [6][7] [8] and sweeping frequency analysis method [9][10] [11] , etc. The channelized radiometer method is first proposed by Urkowitz [1] and it is a kind of energy based detection algorithm.…”

Section: Introductionmentioning

confidence: 99%

Radio signal detection based on spectrogram

Han

Zhou

2023

2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022)

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In this paper, a radio signal detection algorithm is proposed. This algorithm detect the radio signal from complex electromagnetic environment to obtain the center frequency, bandwidth and starting and ending time. First, to detect the time and frequency information simultaneously, the I/Q data in time domain is transformed into spectrogram. Then, the noise threshold is calculated by equidistant binning statistics, and the spectrogram is binarized based on the threshold. A kind of filter operator is designed for binary image denoising. Then we use DBSCAN clustering algorithm to cluster signal points in binary spectrogram. Finally, after separating the overlapping signals and filtering the false signals, the center frequency, bandwidth, starting and stopping time of each signal can be extracted. For experimental evaluation, average precision (AP) and average Distance-DIOU (ADIOU) are used as evaluation indexes. Radio signal data with center frequency near 2.4GHz and bandwidth 40 MHz is collected. And 420 segments of test data are extracted for manual labeling. Detection experiments on the 420 test samples reveal that the algorithm we proposed can achieve a AP value of 0.839, and the ADIOU value reaches 0.674. The result shows that the algorithm can detect radio signals accurately.

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“…The detection and interception of FH signals can be addressed in several methods of which wide band or channelized receiver, time-frequency distribution, and cyclostationary processing are typical ones [1][2][3][4]. For all the methods above, the extremely large requirement of measurements is one of the most serious disadvantages, which can be a bottleneck in the application of identification of high speed wide band FH signals.…”

Section: Introductionmentioning

confidence: 99%

The Identification of Frequency Hopping Signal Using Compressive Sensing

Jia¹,

Tian²,

Yu³

2009

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Abstract:Compressive sensing (CS) creates a new framework of signal reconstruction or approximation from a smaller set of incoherent projection compared with the traditional Nyquist-rate sampling theory. Recently, it has been shown that CS can solve some signal processing problems given incoherent measurements without ever reconstructing the signals. Moreover, the number of measurements necessary for most compressive signal processing application such as detection, estimation and classification is lower than that necessary for signal reconstruction. Based on CS, this paper presents a novel identification algorithm of frequency hopping (FH) signals. Given the hop interval, the FH signals can be identified and the hopping frequencies can be estimated with a tiny number of measurements. Simulation results demonstrate that the method is effective and efficient.

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A Novel Algorithm of Blind Detection of Frequency Hopping Signal Based on Second-Order Cyclostationarity

Cited by 17 publications

References 9 publications

Knowledge-Enhanced Compressed Measurements for Detection of Frequency-Hopping Spread Spectrum Signals Based on Task-Specific Information and Deep Neural Networks

Knowledge-Enhanced Compressed Measurements for Detection of Frequency-Hopping Spread Spectrum Signals Based on Task-Specific Information and Deep Neural Networks

Radio signal detection based on spectrogram

The Identification of Frequency Hopping Signal Using Compressive Sensing

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