Optimization of a Constrained-Feed Rotman Lens Beamformer

Abstract

A previous mathematical analysis has demonstrated the utility of a partially overlapped, constrained-feed network for time-delay control of large linear arrays (R.J. Mailloux, IEEE Trans. 49, February 2001, pp.280-291). In particular, this novel method allows for approximately -30 dB sidelobe suppression over a 20% bandwidth. An array with time-delayed contiguous subarrays with the same separation would have quantization lobes at the -10 dB level; thus, this technique appears to offer significant advantages. We recently developed an experiment to demonstrate this concept. We collected data for the broadside case (array phase shifters set to zero) from -45 degrees to 45 degrees in 0.25 degrees increments and from 9.0 to 11.0 GHz in 0.05 GHz increments (center frequency of 10 GHz). We allowed weighting of both the Rotman lens outputs (constituent beams) and the Butler matrix outputs (subarray patterns). We used a genetic algorithm to optimize these complex weightings. We realized that our data set didn't represent the best measure of system performance, since there is no beam squinting at broadside; therefore, we performed a limited field of view (LFOV) test. Using this LFOV test, we were able to demonstrate at least -28 dB sidelobes over an angular field of view corresponding to a 20% bandwidth.

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Document Details

Document Type
Technical Report
Publication Date
Oct 20, 2005
Accession Number
ADA486592

Entities

People

  • James P. Kenney
  • Michelle H. Champion
  • Robert J. Mailloux
  • Scott G. Santarelli

Organizations

  • Air Force Research Laboratory

Tags

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Arrays
  • Bandwidth
  • Data Sets
  • Far Field
  • Frequency
  • Genetic Algorithms
  • Linear Arrays
  • Mathematical Analysis
  • Optimization
  • Power Dividers
  • Radiation Patterns
  • Sidelobes
  • Transfer Functions

Fields of Study

  • Physics

Readers

  • Phased Array Antenna Design.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Biotechnology