Mimo Recursive Least Squares Control Algorithm for the AN/FPN-44A Loran- C Transmitter

Abstract

A multiple-input, multiple-output (MIMO) recursive least squares (RLS) algorithm is developed to shape and control the Loran-C RF pulse of the AN/FPN-44A tube type transmitter. The control algorithm is incorporated into a transmitter simulation program, where it seeks to produce an optimal transmitter drive waveform (TDW). An optimal TDW produces a near ideal RF pulse. The control algorithm uses a MIMO reference model of the transmitter; parameters of the model are obtained using recursive least squares multichannel time series techniques. The MIMO reference model has the ability to adapt to the non-LTI characteristics of the simulated transmitter. The MMO RLS control algorithm is implemented in both an ideal and a realistic noisy environment. In the ideal environment, when representing the RF pulse with parameters of its half-cycle peak amplitudes and zero-crossings, the MIMO RLS controller is able to shape the RF pulse and control its zero-crossings. Quantization and system noise in the non-ideal environment results in performance deterioration of the control algorithm. The performance of the MIMO RLS algorithm is compared against another method of control, the steepest descent algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1993
Accession Number
ADA274820

Entities

People

  • John D. Wood

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Algorithms
  • Coast Guard
  • Computational Complexity
  • Computers
  • Control Systems
  • Electrical Engineering
  • Electronic Equipment
  • Engineering
  • Least Squares Method
  • Loran
  • Multiple Input Multiple Output
  • Radio Navigation
  • Security
  • Simulations
  • Specifications
  • Standards
  • United States

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radio communications and signal processing.