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Course 557 Details

 
Course 557: Inertial Systems, Kalman Filtering and GPS / INS Integration (3.0 CEUs; EXPANDED TO 5 FULL DAYS) (Public and On-Site)  
Instructors: Dr. Alan Pue, Johns Hopkins University, APL and  Mr. Michael Vaujin, Consultant

Description

Course 557 (formerly called Course 556) has been expanded to a full five days based on attendee requests. This course on GNSS-aided navigation will thoroughly immerse the student in the fundamental concepts and practical implementations of the various types of Kalman filters that optimally fuse GPS receiver measurements with a strapdown inertial navigation solution. The course includes the fundamentals of inertial navigation, inertial instrument technologies, technology surveys and trends, integration architectures, practical Kalman filter design techniques, case studies, and illustrative demonstrations using MATLAB.
 
The extra half-day will allow for a fuller and more detailed development of the design of an aided navigation system, combined with a more detailed discussion of the use of lower quality IMUs, and advanced filtering techniques. 

Prerequisites

  • Familiarity with principles of engineering analysis, including matrix algebra and linear systems.
  • A basic understanding of probability, random variables, and stochastic processes.
  • An understanding of the GPS operational principles in Course 356, or equivalent experience.

Who Should Attend?

  • GPS/GNSS professionals who are engineers, scientists, systems analysts, program specialists and others concerned with the integration of inertial sensors and systems.
  • Those needing a working knowledge of Kalman filtering, or those who work in the fields of either navigation or target tracking.

Equipment You Should Bring

  • A laptop (PC or Mac) with full version of MATLAB 5.0 (or later) installed. This will allow you to work the problems in class and do the practice "homework" problems each evening. All of the problems will also be worked in class by the instructor, so this equipment is not required, but is recommended.
  • These course notes are searchable and you can take electronic notes with the Adobe Acrobat 9 Reader we will provide you.

Materials You Will Keep

  • A color electronic copy of all course notes will be provided on a USB Drive or CD-ROM. Bringing a laptop to this class is highly recommended; power access will be provided. 
  • A black and white hard copy of the course notes, printed 3 slides to a page. 
  • Public Venue Attendees: Introduction to Random Signals and Applied Kalman Filtering, 3rd edition, by R. Grover Brown and Patrick Hwang, John Wiley & Sons, Inc., 1996. (Note: This arrangement does not apply to on-site contracts. Any books for on-site group contracts are negotiated on a case by case basis.)

Day 1, Morning

Introduction to INS/GPS Integration
  • Inertial navigation
  • Integration architectures
  • Example applications
Vectors, Matrices, and State Space
  • Vectors and matrices
  • State-space description
  • Examples
Random Processes
  • Random variables
  • Covariance matrices
  • Random process descriptions

Day 1, Afternoon

Kalman Filter
  • Filtering principles
  • Least squares estimation
  • Kalman filter derivation
Filter Implementation
  • Filter processing example
  • Off-line analysis
  • Filter tuning
Navigation Coordinate Systems
  • Earth model
  • Navigation coordinates
  • Earth relative kinematics

Day 2, Morning

Inertial Navigation Mechanization
  • Gravity model
  • Navigation equations
  • Implementation options
Inertial Sensor Technologies
  • Accelerometer technologies
  • Optical gyros
  • MEMS technologies
  • Technology survey
Strapdown Systems
  • Quaternions
  • Orientation vector
  • Coning and sculling compensation

Day 2, Afternoon

Navigation System Errors
  • Tilt angle definitions
  • Navigation error dynamics
  • Simplified error characteristics
System Initialization
  • INS static alignment
  • Transfer alignment
  • Simplified error analysis
Loosely-Coupled INS/GPS Design
  • Measurement processing
  • Filter design and tuning
  • Navigation systems update

Day 3, Morning

INS Aiding of Receiver Tracking
  • Code and carrier tracking
  • Track look deign trades
  • Interference suppression
  • Autocorrelation, power spectral density
  • Deep integration
Tightly coupled INS/GPS Design
  • Measurement processing
  • Filter parameter selection
  • Pseudo-range and delta pseudo-range measurement models
Multi-Sensor Integration
  • Terrain aiding and relative GPS
  • Carrier phase differential integration
  • GPS interferometer/INS integration

Day 3, Afternoon

Building Extended Kalman Filters
  • Linearized and extended Kalman filters
  • Radar tracking of a vertical body motion with non-linear dynamics
  • Radar tracking of an accelerating body with non-linear measurements
Implementation Considerations
  • Keeping a covariance matrix well-conditioned, symmetric, and positive definite
  • Sequential vs batch measurement processing
  • Methods of measurement correlation and de-correlation
Discreet Implementation of the Strapdown Equations
  • Attitude updates and TOV of the acceleration
  • Propagating the position DCM
  • High rate vs low rate routines
  • Effects of errors in initialization & IMU data

Day 4, Morning

Aided Psi-Angle Navitator
  • Description and demonstration of a loosely coupled 15 state Psi-angle wander azimuth navigator flying an aircraft type trajectory
  • Homework problems
Partials of Measurement Equations
  • Techniques for taking partials
  • Psi-angle and Phi-angle feedback to strapdown
  • Homework solutions
Initialization and Process Noise
  • Strapdown and covariance matrix initialization
  • Process noise for vertical deflections/anomaly & velocity/attitude random walk
  • Typical sensor error models: random constant, random walk & first order Gauss Markov

Day 4, Afternoon

Aided Phi-Angle Navigator
  • Description and demonstration of a Phi-angle north-slaved navigator
  • Modeling position error as latitude/longitude error
  • Modeling position error as navigation frame tilt error
  • Comparison of popular state dynamics matrix elements
  • Navigation in ECEF coordinates
Ground Alignment & Integrated Velocity Matching
  • Gyro-Compassing, zero velocity & zero earth rate observations
  • Small azimuth static alignment & leveling
  • Integrated true velocity error, mapping into delta-range
  • Large azimuth static alignment, advanced methods
  • Aiding with a 3 axis magnetometer
Attitude Matching & Use of Inexpensive IMUs
  • Attitude matching & boresight error states
  • Considerations for use of very inexpensive IMUs
  • Non-holonomic motion constraints
  • Process noise for un-modeled states
  • Matrix partitioning for computational efficiency
  • Using consider states

Day 5, Morning

Measurement Editing & Adaptive Filters
  • Online and offline residual analysis
  • Advanced methods of outlier detection & rejection
  • Multiple Model Adaptive Estimation
  • Application to carrier phase integer ambiguity resolution
Methods of Smoothing
  • Optimal prediction & Fixed Interval smoothing
  • Fixed Point & Fixed Lag smoothing
  • Application to navigation testing
Square Root Filtering

Day 5, Afternoon

Suboptimal Covariance Analysis
  • Effects of mis-modeling errors
  • Optimal & sub-optimal (two pass) covariance analysis
  • Error budget & reduced state analysis
Unscented Kalman Filters
  • Sigma points & the Unscented Transform
  • Performance against the EKF
  • Augmentation & application to navigation
  • Spherical Simplex Sigma Points
  • Square Root UKFs
Particle Filtering & 2nd Order EKFs
  • Bootstrap particle filter (PF)
  • Multi-modal position solutions
  • 2nd Order EKFs (2EKF)
  • Cage Match! PF vs 2EKF vs UKF vs EKF
 

 
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