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525.745 - Applied Kalman Filtering Course Homepage

Instructor Information

John Samsundar

Adam Watkins

Email: adam.watkins@jhuapl.edu
Work Phone: (443) 778-9423

Course Information

Course Description

Theory, analysis, and practical design and implementation of Kalman filters are covered, along with example applications to real-world problems. Topics include a review of random processes and linear system theory; Kalman filter derivations; divergence analysis; numerically robust forms; suboptimal filters and error budget analysis; prediction and smoothing; cascaded, decentralized, and federated filters; linearized, extended, second order, and adaptive filters; and case studies in GPS, inertial navigation, and ballistic missile tracking.

Prerequisites

525.414 Probability and Stochastic Processes for Engineers and 525.466 Linear System Theory or equivalents. Knowledge of Matlab (or equivalent software package).

Course Goal

The Kalman filter is a computer algorithm for processing discrete measurements into optimal estimates.  The goal of this course is to present Kalman filtering theory with an emphasis on practical design and implementation for a wide variety of disciplines.

Course Objectives

  • Theoretical foundation of the Kalman filter.
  • Implementation and diagnostics of the Kalman filter.
  • Applications to target tracking and navigation.
  • Advanced filtering techniques.

When This Course is Typically Offered

Each spring semester at APL.

Syllabus

Topics Covered

  • Probability and Random Processes
  • Linear Stochastic Systems
  • Least Squares
  • Derivation of the Kalman Filter
  • Implementation of the Kalman Filter
  • The Extended Kalman Filter
  • The Information Filter and Extended Information Filter
  • Smoothing
  • The Unscented Kalman Filter
  • Covariance Analysis
  • Applications: Target Tracking
  • Applications: Navigation
  • Applications: Robotic Localization (and Mapping)

Student Assessment Criteria

Homework (10 assignments) 25%
Mid-term Exam (take-home/in-class) 25%
Final Exam (take-home/in-class) 25%
Project 25%

Computer and Technical Requirements

Working knowledge of Matlab (or equivalent software package)

Textbooks

Textbook information for this course is available online through the MBS Direct Virtual Bookstore.

Course Notes

There are notes for this course.

(Last Modified: 01-26-2009 at 4:03:42 PM)