• DocumentCode
    1246305
  • Title

    Quadratic detectors for energy estimation

  • Author

    Fang, Jing ; Atlas, Les E.

  • Author_Institution
    Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
  • Volume
    43
  • Issue
    11
  • fYear
    1995
  • fDate
    11/1/1995 12:00:00 AM
  • Firstpage
    2582
  • Lastpage
    2594
  • Abstract
    The estimation of signal energy is an important part of physics and signal processing. A commonly used energy estimate in signal processing is instantaneous energy that is defined by the square of the signal magnitude at time t, i.e., |x(t)|2. For a noisy signal, a standard energy detector, which consists of a linear time-invariant (LTI) filter followed by a magnitude-squared operator, is commonly used to reduce noise and extract signal energy in a certain frequency band. However, due to the temporal response of the LTI filtering, this energy estimate is smeared in time. In addition, it is unclear how this estimate relates to the physical energy in the system that produced the signal. e propose simple quadratic systems producing frequency-selective energy estimates and effective noise reduction with little or no smearing in time. We introduce the new concept of quadratic detectors, discuss desirable time and frequency resolution properties of a general quadratic detector, and study five different applications to demonstrate the simplicity of quadratic detector design and implementation
  • Keywords
    filtering theory; parameter estimation; signal detection; signal resolution; LTI filtering; energy detector; frequency band; frequency resolution; frequency-selective energy estimates; instantaneous energy; linear time-invariant filter; magnitude-squared operator; noise reduction; noisy signal; physics; quadratic detectors; quadratic systems; signal energy estimation; signal magnitude; signal processing; temporal response; time resolution; Band pass filters; Bandwidth; Biomedical signal processing; Detectors; Energy resolution; Filtering; Frequency estimation; Noise reduction; Nonlinear filters; Signal processing;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.482109
  • Filename
    482109