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Your search keyword '"Wang, Linghuan"' showing total 3 results
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3 results on '"Wang, Linghuan"'

1. Parameters estimation of linear FM signal based on matching Fourier transform

Author

Li Zhao, Ma Hong-guan, Wang Linghuan, Ai Ming-shun, and Zhang Xin-yu

Subjects
symbols.namesake, Signal processing, Signal-to-noise ratio, Fourier transform, Noise (signal processing), Estimation theory, symbols, Electronic engineering, Sine, Doppler effect, Signal, Algorithm, Mathematics
Abstract

The maximum amplitude of matching Fourier transform (MFT) spectrum based on re-sampling of sine (single pulse, SP) signal reflected the frequency of the signal. The analysis of the MFT spectrum of SP signal reflected the MFT theories, from which a method to estimate the frequency of SP signal was proposed; The similarity of the MFT spectrum of the SP and linear FM (LFM) signal was analyzed, then the parameter estimation of LFM signal was presented; The characteristic of promoting the MFT spectrum after adding windows was illustrated, so a method to improve the precision of estimation parameter by adding windows was proposed. The experiments and simulations show the efficiency of the parameters estimation of LFM signal.

Published
2008

2. A Method to Detect Multi-components Linear FM Signal

Author

Ma Hongguang, Zhang Jiawen, Ai Ming-shun, and Wang Linghuan

Subjects
Radon transform, Estimation theory, business.industry, Computation, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Signal, Time–frequency analysis, Support vector machine, Cluster labeling, Detection theory, Artificial intelligence, business, Mathematics
Abstract

The support vector clustering (SVC) algorithm was introduced to get the number of the pinnacles in the result of the time-frequency analysis and Radon transform of the multi-component linear FM (LFM) signal, and to finish the detection of the components of the LFM signal. Meanwhile, the preprocessing to reduce the points' number of the input data-set for SVC, was proposed to improve the computation efficiency. And a novel cluster labeling method was developed to improve the SVC algorithm. The simulation results depict that the SVC-Radon-time-frequency approach is efficient for the detection and parameter estimation of the multi-component LFM signal with low SNR

Published
2006

3. Multicomponent linear FM signal detection based on support vector clustering

Author

Wang Linghuan, Li Qi, Ma Hongguang, and Li Zheng

Subjects
Radon transform, business.industry, Estimation theory, Computation, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Signal, Time–frequency analysis, Support vector machine, Cluster labeling, Detection theory, Artificial intelligence, business, Mathematics
Abstract

The support vector clustering (SVC) algorithm was introduced to get the number of the pinnacles in the result of the time-frequency analysis and Radon transform of the multicomponent linear FM (LFM) signal, and to fulfil the detection of the components of the LFM signal. Meanwhile, an approach called near zero mean, for reducing the point number of the input data-set for SVC, was proposed to improve the computation efficiency. And a novel cluster labeling method was developed to improve the SVC algorithm. The simulation results depict that the SVC-radon-time-frequency approach is efficient for the detection and parameter estimation of the multi-components LFM signal

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