Characterization and Implementation of a Real-World Target Tracking Algorithm on Field Programmable Gate Arrays with Kalman Filter Test Case

Abstract

A one dimensional Kalman Filter algorithm provided in Matlab is used as the basis for a Very High Speed Integrated Circuit Hardware Description Language (VHDL) model. The JAVA programming language is used to create the VHDL code that describes the Kalman filter in hardware which allows for maximum flexibility. A one-dimensional behavioral model of the Kalman Filter is described, as well as a one dimensional and synthesizable register transfer level (RTL) model with optimizations for speed, area, and power. These optimizations are achieved by a focus on parallelization as well as careful Kalman filter sub-module algorithm selection. Newton-Raphson reciprocal is the chosen algorithm for a fundamental aspect of the Kalman filter, which allows efficient high-speed computation of reciprocals within the overall system. The Newton-Raphson method is also expanded for use in calculating square-roots in an optimized and synthesizable twodimensional VHDL implementation of the Kalman filter. The two-dimensional Kalman filter expands on the one-dimensional implementation allowing for the tracking of targets on a real-world Cartesian coordinate system.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2008
Accession Number
ADA487116

Entities

People

  • Benjamin Hancey

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Application-Specific Integrated Circuits
  • Circuits
  • Computer Programming
  • Computers
  • Coordinate Systems
  • Estimators
  • Field Programmable Gate Arrays
  • Integrated Circuits
  • Java Programming Language
  • Kalman Filters
  • Language
  • Spreadsheet Software
  • Square Roots
  • Target Tracking
  • Two Dimensional

Readers

  • Approximation Theory.
  • Parallel and Distributed Computing.