Accession Number:

ADA339150

Title:

Maximum Likelihood Adaptive Neural Systems (MLANS) Application to High Frequency (HF) Propagation

Descriptive Note:

Final technical rept. Apr-Dec 94

Corporate Author:

NICHOLS RESEARCH CORP WAKEFIELD MA

Report Date:

1997-09-01

Pagination or Media Count:

60.0

Abstract:

The feasibility of applying a model-based neural network technique to investigate the properties of ionospheric clutter observed in the operation of high frequency HF propagation systems was examined. Individual ionospheric clutter structures found in the amplitude-range-Doppler ARD spectra of over-the-horizon OTH radar data were successfully segmented and characterized. A multi-mode Gaussian clutter model was formulated using the Maximum Likelihood Adaptive Neural System MLANS to fit the observations. The results indicate that either a three or a four mode Gaussian model is sufficient for MLANS to segment and characterize the observed clutter. High Fidelity simulations of time slices of the raw data were achieved by combining time-varying Gaussian together with a time-varying uniform distribution to represent the noise floor. Each Gaussian mode or model is characterized by a time-varying set of three parameters amplitude, Doppler spread, and Doppler shift.

Subject Categories:

  • Cybernetics
  • Active and Passive Radar Detection and Equipment

Distribution Statement:

APPROVED FOR PUBLIC RELEASE