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Autocorrelation Method

The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

Xiang Li – 1st expert on this subject based on the ideXlab platform

  • sfm signal detection and parameter estimation based on pulse repetition interval transform
    European Signal Processing Conference, 2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li

    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

  • EUSIPCO – SFM signal detection and parameter estimation based on pulse-repetition-interval transform
    , 2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li

    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

Bin Deng – 2nd expert on this subject based on the ideXlab platform

  • sfm signal detection and parameter estimation based on pulse repetition interval transform
    European Signal Processing Conference, 2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li

    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

  • EUSIPCO – SFM signal detection and parameter estimation based on pulse-repetition-interval transform
    , 2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li

    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

Hongqiang Wang – 3rd expert on this subject based on the ideXlab platform

  • sfm signal detection and parameter estimation based on pulse repetition interval transform
    European Signal Processing Conference, 2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li

    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

  • EUSIPCO – SFM signal detection and parameter estimation based on pulse-repetition-interval transform
    , 2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li

    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.