Fast Kalman Filtering for ARMA (Autoregressive Moving Average) Processes: Fixed Point Implementation.

Abstract

Kalman predictors and filters are implemented in fixed point arithmetic on a 16-bit INTEL 8086 microprocessor. Results from this implementation are compared with corresponding results for a 16-bit floating point implementation on an 8-bit 8080 microprocessor. Both implementations are carried out using an INTEL MDS 230 development system. Floating point code is written in FORTRAN and fixed point code is written in Assembly language. The Kalman filters and predictors are realized in a fast form that uses the so-called fast Kalman gain algorithm. This algorithm for the gain is inherently fixed point. Scaling rules for Kalman filters and predictors are derived, and expressions are derived for rounding error variances. The numerical results show that low order fixed point realizations perform very close to the floating point realizations. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 15, 1983
Accession Number
ADA126994

Entities

People

  • Sigurdur Sigurdsson

Organizations

  • University of Rhode Island

Tags

Communities of Interest

  • Advanced Electronics
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Assembly Languages
  • Computational Complexity
  • Electrical Engineering
  • Equations
  • Filters
  • Filtration
  • Floating Point Operations
  • Indexes
  • Information Science
  • Kalman Filtering
  • Military Research
  • Probability
  • Signal Processing
  • Statistics
  • Test And Evaluation
  • White Noise

Fields of Study

  • Engineering

Readers

  • Parallel and Distributed Computing.
  • Phased Array Antenna Design.
  • Statistical inference.