A New Intelligent Approach for Recognition of Digital Satellite Signals

Azarbad, Milad; Ebrahimzadeh, Ataollah; Addeh, Jalil

doi:10.1007/s11265-013-0829-0

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…There are many expert features which have been applied to the recognition of communication signals in the pattern recognition method. [10] classified MASK, MPSK, MFSK, and MQAM signals by using instantaneous features, higher order moments and higher order cumulants, which had high recognition probability at low signal to noise ratio (SNR). [11] extracted the wavelet transform features of MPSK, MFSK, and MQAM signals in Gaussian channel, flat fading channel and multipath fading channel to perform signal recognition.…”

Section: Automatic Modulation Classification (Amc) Is An Intermediate...mentioning

confidence: 99%

“…The third label sequence can be directly obtained by ReliefF algorithm. According to (10), we can calculate the total scores of each feature and select the top m features with the highest scores. Because the normalized variance shows the stability of features under different signal-to-noise ratios, it determines whether a feature is available.…”

Section: Stage1: Combined Scoringmentioning

confidence: 99%

“…It can be seen from (5,5) and (5,10) that when T increases, the average recognition probability of different modulation formats increases and the number of selected features decreases. For (10,10) and (20,20), when T increases, the average recognition probability and the number of selected features of different modulation formats remain unchanged. This may be because TSHFSA has selected the best feature subset at present.…”

Section: Analysis Of Average Recognition Probabilitymentioning

confidence: 99%

“…This may be because TSHFSA has selected the best feature subset at present. For (5,10) and (10,10), when the S increases, the average recognition probability of different modulation formats increases and the number of selected features decreases under low SNR. At the same time, both remain unchanged at high SNR.…”

Section: Analysis Of Average Recognition Probabilitymentioning

confidence: 99%

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A Two-Stage Hybrid Feature Selection Method of Communication Signals Based on Combined Scoring and Improved Binary Whale Optimization Algorithm

Liu

Zhao

et al. 2023

Preprint

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Communication signals have many modulation types in the current complex electromagnetic environment, so it is more and more difficult to recognize them accurately. Extracting features from different dimensions can describe the essence of the signals in different aspects and better distinguish various signal modulation formats. However, there may be features with poor anti-noise performance, irrelevant features and redundant features in the original high-dimensional feature set, which not only reduce the recognition probability of the signals but also make the feature selection space grow exponentially. Aiming at the problem of multi-dimensional feature selection of communication signals, this paper proposes a two-stage hybrid feature selection method based on combined scoring and improved binary whale optimization algorithm. In the first stage, the method uses the combined algorithm to initially filter the extracted signal features, and in the second stage, the improved binary whale optimization algorithm is used for further optimization. The results show that, compared with the original feature set, this method can increase the average recognition probability of the signals by up to 17%, and the feature reduction rate up to 81%. Besides, we prove that the proposed algorithm has certain generalization ability for different signal-to-noise ratio (SNR) and classifiers.

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Section: Automatic Modulation Classification (Amc) Is An Intermediate...mentioning

confidence: 99%

Section: Stage1: Combined Scoringmentioning

confidence: 99%

Section: Analysis Of Average Recognition Probabilitymentioning

confidence: 99%

Section: Analysis Of Average Recognition Probabilitymentioning

confidence: 99%

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A Two-Stage Hybrid Feature Selection Method of Communication Signals Based on Combined Scoring and Improved Binary Whale Optimization Algorithm

Liu

Zhao

et al. 2023

Preprint

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“…The RBF networks were used in many cases, such as problems from physics [11][12][13][14], solving differential equations [15][16][17], robotics [18,19], face recognition [20], digital communications [21,22], chemistry problems [23,24], economic problems [25][26][27], network security problems [28,29] etc. Also, recently a variety of papers have appeared proposing novel initialization techniques for the network parameters [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Adapt the Parameters of RBF Networks Using Grammatical Evolution

Tsoulos,

Tzallas,

Karvounis

2023

Preprint

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RBF networks are used in a variety of real-world applications such as medical data or signal processing problems. The success of these parametric models lies in the successful adaptation of their parameters using efficient computational techniques. In the current work, a method of adjusting the parameters of these networks using Grammatical Evolution is presented. Grammatical Evolution will be used to successfully discover the most promising range of parameter values and then the training of the parameter set will be achieved using a Genetic Algorithm. The new method was applied to a wide range of data fitting and classification problems, and the results were more than promising.

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