Radar Emitter Signal Recognition Based on Resemblance Coefficient Features

Zhang, Gexiang; Rong, Haina; Jin, Weidong; Hu, Laizhao

doi:10.1007/978-3-540-25929-9_83

Cited by 35 publications

(18 citation statements)

References 2 publications

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“…The performances of the two algorithms are evaluated by using computing efficiency and optimization result. Computing efficiency includes computing time and the decay performance given in (13). Optimization result is evaluated by using the correlation ratio of the original signal and approximation signal given in (12).…”

Section: Methodsmentioning

confidence: 99%

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Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition

Zhang

Rong

2007

Computational Science – ICCS 2007

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Abstract. The main problem of time-frequency atom decomposition (TFAD) lies in an extremely high computational load. This paper presents a fast implementation method based on quantum-inspired genetic algorithm (QGA). Instead of finding the optimal atom in greedy implementation algorithm, this method is to search a satisfactory atom in every iteration of TFAD. Making full use of QGA's advantages such as good global search capability, rapid convergence and short computing time, the method reduces greatly the computational load of TFAD. Experiments conducted on radar emitter signals verify the effectiveness and practicality of the introduced method.

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Section: Methodsmentioning

confidence: 99%

“…To compute the correlation between the original signal f and the restored signal f r with parts of decomposed time-frequency atoms, resemblance coefficient method [13] is used to the correlation ratio C r of f and f r .…”

Section: Algorithm 1 Algorithm Of Qga Based Tfadmentioning

confidence: 99%

Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition

Zhang

Rong

2007

Computational Science – ICCS 2007

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“…In our prior work, 16 features have been extracted from 10 radar emitter signals using different approaches [15][16][17]. The features are fractal dimensions, including information dimension, box dimension, and correlation dimension, two resemblance coefficient features, Lempel-Ziv complexity, approximate entropy, wavelet entropy, and eight energy distribution features based on wavelet packet decomposition.…”

Section: Application Examplementioning

confidence: 99%

Novel Quantum Genetic Algorithm and Its Applications

Zhang

Jin

et al. 2006

Front. Electr. Electron. Eng. China

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By introducing strong parallelism of quantum computing into evolutionary algorithm, a novel quantum genetic algorithm (NQGA) is proposed. In NQGA, a novel approach for updating the rotation angles of quantum logic gates and a strategy for enhancing search capability and avoiding premature convergence are adopted. Several typical complex continuous functions are chosen to test the performance of NQGA. Also, NQGA is applied in selecting the best feature subset from a large number of features in radar emitter signal recognition. The testing and experimental results of feature selection show that NQGA presents good search capability, rapid convergence, short computing time, and ability to avoid premature convergence effectively.

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“…, , , x x x respectively, are chosen to make the simulation experiment. Attribute set is made up of 8 features [11][12][13] that are represented with 1 2 8 , , , a a a . When signal-to-noise (SNR) varies from 5 dB to 20 dB, 8 features extracted from 10 RESs construct the attribute table shown in Table 1.…”

Section: Discretization Algorithmmentioning

confidence: 99%

Discretization of Continuous Attributes in Rough Set Theory and Its Application

Zhang¹,

Hu²,

Jin

2004

Computational and Information Science

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Abstract. Existing discretization methods cannot process continuous intervalvalued attributes in rough set theory. This paper extended the existing definition of discretization based on cut-splitting and gave the definition of generalized discretization using class-separability criterion function firstly. Then, a new approach was proposed to discretize continuous interval-valued attributes. The introduced approach emphasized on the class-separability in the process of discretization of continuous attributes, so the approach helped to simplify the classifier design and to enhance accurate recognition rate in pattern recognition and machine learning. In the simulation experiment, the decision table was composed of 8 features and 10 radar emitter signals, and the results obtained from discretization of continuous interval-valued attributes, reduction of attributes and automatic recognition of 10 radar emitter signals show that the reduced attribute set achieves higher accurate recognition rate than the original attribute set, which verifies that the introduced approach is valid and feasible.

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Radar Emitter Signal Recognition Based on Resemblance Coefficient Features

Cited by 35 publications

References 2 publications

Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition

Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition

Novel Quantum Genetic Algorithm and Its Applications

Discretization of Continuous Attributes in Rough Set Theory and Its Application

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