A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm

Ye, Jiaquan; Zou, Jie; Gao, Jing; Zhang, Guomin; Kong, Mingming; Pei, Zheng; Cui, Kaitao

doi:10.1109/access.2021.3070491

Cited by 19 publications

(14 citation statements)

References 28 publications

(33 reference statements)

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“…The points with signal strength greater than 𝜃 are set to be +1 and other points are -1. In work [12] , noise points and signal points are supposed to be convex distribution. So equidistant binning statistics is carried out and signal strength corresponding to the first minimum point after the maximum point is considered as the noise threshold 𝜃 of current frame.…”

Section: Noise Threshold Calculation and Binarizationmentioning

confidence: 99%

“…Now, the points need to be clustered to get the center frequency, bandwidth, starting and ending time of each signal. In work [12] , the 4-connected-component labeling technique is applied to cluster signal points. However, due to signal strength, modulation mode and other factors, connectivity is not necessary for two signal points to belong to the same signal.…”

Section: Signal Points Clusteringmentioning

confidence: 99%

“…The parameters in frequency and time domain can be obtained simultaneously through spectrogram based method. For example, the work [12] conducts a process to detect UAV (unmanned aerial vehicles) frequency hopping signals in spectrograms.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Noise Threshold Calculation and Binarizationmentioning

confidence: 99%

Section: Signal Points Clusteringmentioning

confidence: 99%

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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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“…Frequency hopping (FH) signals have been increasingly utilized in civilian and military communications. Since their invention, the robust anti-interference capability, low interception probability, and excellent anti-fading effect have made it possible to diversify the human use of electromagnetic waves [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. For instance, Code Division Multiple Accessbased 3G cellular communications significantly enhance the channel capacity; however, the multipath effect may distort the frequency identification of FH carriers, leading to an elevated Bit Error Rate.…”

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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“…There are also researchers who improve the performance in terms of the speed of the K-Means method by parallelizing iterations [23]. The research was also carried out with improvements to the k-means algorithm, for example, selecting the number of clusters based on the density characterization of objects [24], determining the initial centroid using the maximin method [25], determining the number of clusters using the Maximum Stable Set Problem and Continuous Hopfield Network [26]. Research with the K-Means algorithm is also carried out for special data, for example, those with outliers [27], associated with ensemble learning [3], [28].…”

Section: Introductionmentioning

confidence: 99%

Proposed Modification of K-Means Clustering Algorithm with Distance Calculation Based on Correlation

Rachmatullah

2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Clustering is a technique in data mining that groups a set of data into groups (clusters) of similar data. In general, there are two methods of clustering, namely the hierarchical method and the partition method. One of the most commonly used partition clustering methods in clustering is K-Means. The use of K-means method has been widely used in various fields with various purposes. Many research has been carried out to improve the performance of the K-Means method, for example, by modifying the method of determining the initial centroid or determining the appropriate number of clusters. In this research, the modification of the K-Means algorithm was carried out in calculating the distance by considering the correlation value between attributes. Attributes that have a high correlation value are assumed to have similar characteristics so that they determine the location of data in a particular cluster. The steps of the proposed method are: calculating the correlation value between attributes, determining the cluster centroid, calculating the distance by considering the value of correlation, and determining the data into certain clusters. The first contribution of this research is to propose a new distance calculation technique in the K-Means algorithm by considering correlation and the second contribution is to apply the proposed algorithm to a specific dataset, namely Iris dataset. In this research, the performance calculation of the modified algorithm was also carried out. From the experimental results using the Iris dataset, the proposed modification of the K-Means algorithm has fewer iterations than the original K-Means method, so that it requires less processing time. The original K-Means method requires 8 iterations, while the proposed method requires only 6 iterations. The proposed method also produces a higher accuracy rate of 89.33% than the original K-Means method, which is 82.67%.

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A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm

Cited by 19 publications

References 28 publications

Radio signal detection based on spectrogram

Radio signal detection based on spectrogram

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

Proposed Modification of K-Means Clustering Algorithm with Distance Calculation Based on Correlation

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