Signal representation using adaptive normalized Gaussian functions

Qian, Shie; Chen, Dapang

doi:10.1016/0165-1684(94)90174-0

Cited by 228 publications

(112 citation statements)

References 9 publications

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“…In this section, the adaptive signal decomposition scheme independently developed in [34][35][36] will be briefly reviewed. That is, for a given signal s(t), first select a function g 0 (t ) from a set of predefined atoms such that the distance between s(t) and its orthogonal projection on g 0 (t ) is minimum in the sense of…”

Section: Adaptive Gaussian Chirplets Decompositionmentioning

confidence: 99%

A method for condition monitoring and fault diagnosis in electromechanical system

Guo

Huang

et al. 2007

Neural Comput & Applic

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Condition monitoring of electrical machines has received considerable attention in recent years. Many monitoring techniques have been proposed for electrical machine fault detection and localization. In this paper, the feasibility of using a nonlinear feature extraction method noted as Kernel independent component analysis (KICA) is studied and it is applied in self-organizing map to classify the faults of induction motor. In nonlinear feature extraction, we employed independent component analysis (ICA) procedure and adopted the kernel trick to nonlinearly map the Gaussian chirplet distributions into a feature space. First, the adaptive Gaussian chirplet distributions are mapped into an implicit feature space by the kernel trick, and then ICA is performed to extract nonlinear independent components of the Gaussian chirplet distributions. A thorough laboratory study shows that the diagnostic methods provide accurate diagnosis, high sensitivity with respect to faults, and good diagnostic resolution.

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Section: Adaptive Gaussian Chirplets Decompositionmentioning

confidence: 99%

A method for condition monitoring and fault diagnosis in electromechanical system

Guo

Huang

et al. 2007

Neural Comput & Applic

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“…The adaptive joint time frequency (AJTF) method [22], in the sense of a modified ML method, is first presented in the adaptive Gaussian representation (AGR) [23] and matching pursuit (MP) [24] methods. It is widely applied in an ISAR imaging method [25,26], which can estimate the PPS phase parameters by searching the solution space based on the maximum projection principle, and, parameterized, represent the signal by extracting the signal components piece by piece.…”

Section: Introductionmentioning

confidence: 99%

A Frequency Domain Extraction Based Adaptive Joint Time Frequency Decomposition Method of the Maneuvering Target Radar Echo

Lao

Chao

et al. 2018

Remote Sensing

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Abstract:The maneuvering target echo of high-resolution radar can be expressed as a multicomponent polynomial phase signal (mc-PPS). However, with improvements in radar resolution and increases in the synthetic period, classical time frequency analysis methods cannot satisfy the requirements of maneuvering target radar echo processing. In this paper, a novel frequency domain extraction-based adaptive joint time frequency (FDE-AJTF) decomposition method was proposed with three improvements. First, the maximum frequency spectrum of the phase compensation signal was taken as the fitness function, while the fitness comparison, component extraction, and residual updating were operated in the frequency domain; second, the time window was adopted on the basis function to fit the uncertain signal component time; and third, constant false alarm ratio (CFAR) detection was applied in the component extraction to reduce the ineffective components. Through these means, the stability and speed of phase parameters estimation increased with one domination ignored in the phase parameter estimation, and the accuracy and effectiveness of the signal component extraction performed better with less influence from the estimation errors, clutters, and noises. Finally, these advantages of the FDE-AJTF decomposition method were verified through a comparison with the classical method in simulation and experimental tests.

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“…Different from Fourier and Wavelet orthogonal transforms, the information in TFAD is not diluted across the whole basis [3]. Hence, TFAD has become an attractive analysis technique in signal processing and harmonic analysis [1,[3][4][5]. However, the the necessary dictionary of time-frequency atoms being very large, the computational load turns out to be the main problem of TFAD [2].…”

Section: Introductionmentioning

confidence: 99%

“…Time-frequency atom decomposition (TFAD), also known as matching pursuit or adaptive Gabor representation [1,2], was introduced independently in [3] and [4]. TFAD is an approach that decomposes any signal into a linear expansion of waveforms selected from a redundant dictionary of time-frequency atoms that well localized both in time and frequency [3].…”

Section: Introductionmentioning

confidence: 99%

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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Signal representation using adaptive normalized Gaussian functions

Cited by 228 publications

References 9 publications

A method for condition monitoring and fault diagnosis in electromechanical system

A method for condition monitoring and fault diagnosis in electromechanical system

A Frequency Domain Extraction Based Adaptive Joint Time Frequency Decomposition Method of the Maneuvering Target Radar Echo

Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition

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