Programmable Blind Adaptive Multivariate Filtering

Abstract

We are developing a new framework, called Programmable Canonical Correlation Analysis (PCCA), for the design of blind adaptive spatial filtering algorithms that attempt to separate one or more signals of interest from co-channel interference and noise. Unlike many alternatives, PCCA does not require knowledge of the calibration data for the array, directions of arrival, training signals, or spatial autocorrelation matrices of the noise or interference. A novel aspect of PCCA is the ease with which new algorithms, targeted at capturing all signals from particular classes of interest, can be developed within this framework. The performance of the new method is being investigated analytically and by computer simulations to quantify its capabilities of signal separation, multipath mitigation, and interference rejection. Preliminary results suggest that the new method can converge very quickly to yield estimates that are comparable to those obtained by the MMSE method that uses perfectly known training signals.

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Document Details

Document Type
Technical Report
Publication Date
Jul 15, 1998
Accession Number
ADA358093

Entities

People

  • William A. Gardner

Organizations

  • University of California

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Co-Channel Interference
  • Communication Systems
  • Computer Simulations
  • Computers
  • Correlation Analysis
  • Data Science
  • Filtration
  • Information Science
  • Mobile Communications
  • Signal Processing
  • Simulations
  • Spatial Filtering
  • Statistics
  • Students
  • Training
  • Wireless Communications

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Phased Array Antenna Design.
  • Statistical inference.