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Simulation of mid-infrared clutter rejection. 1: One-dimensional LMS spatial filter and adaptive threshold

M S Longmire1, A F Milton, E H Takken

  • 1Western Kentucky University, Department of Physics & Astronomy, Bowling Green, Kentucky 42101, USA.

Applied Optics
|April 17, 2010
PubMed
Summary

Signal processing techniques effectively filtered cloud clutter using a 1-D least-mean-square (LMS) spatial filter and adaptive threshold sensor. This method ensures reliable signal detection in both clear and cluttered skies for advanced sensor systems.

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Area of Science:

  • * Remote sensing
  • * Signal processing
  • * Atmospheric optics

Background:

  • * Cloud clutter presents a significant challenge for remote sensing systems.
  • * High-spatial-resolution data is crucial for detecting faint signals.
  • * The 4.0-4.8 micrometer spectral band is utilized for atmospheric observation.

Purpose of the Study:

  • * To evaluate 1-D signal processing techniques for cloud clutter rejection.
  • * To compare the effectiveness of different spatial filters and threshold algorithms.
  • * To determine optimal sensor parameters for reliable signal detection.

Main Methods:

  • * Digital computer simulations were performed on high-spatial-resolution noise data.
  • * Data was collected from back-lit clouds and uniform sky using a scanning system.

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  • * Evaluated techniques included bandpass filters, a least-mean-square (LMS) spatial filter, and adaptive threshold algorithms.

    • Main Results:

    • * A 1-D LMS filter combined with a 1-D adaptive threshold sensor effectively rejected cloud clutter.
    • * This combination provided nearly equal signal detection in clear and cluttered skies.
    • * Performance was validated for systems with noise equivalent irradiance (NEI) > 1.5 x 10(-13) W/cm(2) and spatial resolution < 0.15 x 0.36 mrad.

    Conclusions:

    • * The 1-D LMS filter and adaptive threshold sensor offer a robust solution for cloud clutter mitigation.
    • * These techniques enhance the reliability of remote sensing systems in challenging atmospheric conditions.
    • * The findings are applicable to systems requiring high spatial resolution and sensitive detection.