Sensor Fusion

Spring 2008

News

• Errata
• FAQ
• The folder \\site\edu\rt\Courses\sensorfusion\user has been created for user contributions.

General information

• The course will be given for the second time april-june, 2008. This version will be more compressed due to external visitors, and will be scheduled for about 6 days with three hour lectures, and the possibility to take part in a exercise classes. This page, the course plan and relevant links will be updated in due time. The old ones are kept as reference.
• 070612: The latest version of the compendium, compiled after course feedback.
• An updated version of the compendium will be prepared for the course, distributed electronically before each lecture, and distributed in hardware at the lecture to those who do not want to print themselves.
• Slides from a mini course in Siena, summer 2007.
• During spring 2007, some 40 students registered for the course, and 20 checked out the examination and passed.
• There is now a Student course TS1012 described in studiehandboken

Background knowledge

Course content

• The static case (x is constant)
  • General fusion theory.
    • Estimation theory in the linear case (y=Hx+e). Extensions to the non-linear case (y=H(x)+e, or H(y,x,e)=0).
    • Bayesian fusion perspective, the sensor fusion formula.
    • Computational estimation issues: Centralized versus decentralized fusion (information propagation and double-counting, covariance intersection techniques). Batch computations versus sensor iterations.
    • Detection theory T(x)>h: likelihood ratio concepts, Neyman-Pearson, GLRT and other tests.
    • Computational detection issues: Centralized and distributed detection. Censoring sensors.
    • Diagnosis H(i): x=x(i): The vector model. Error approximations.
  • Localization of a target based on one snap-shot from available sensors.
    • Sensor networks: Range and range-difference measurements. Triangulation from bearings. Information limitations and censoring sensors.
    • Measurement to target association, and extended target models (the fusion before detection principle).
• The dynamic case (x is time-varying)
  • Filter theory.
    • General non-linear filter theory for dx/dt=f(x,u,v), y=h(x,u,e): Numeric evaluation using the point mass filter. Two special cases (KF and HMM) and fundamental limitations (CRLB). Structured models and marginalization.
    • Kalman filtering: Basic theory, implementation aspects, practical issues, information filter, smoothing).
    • Approximative algorithms for non-linear models (Extended KF and HMM, UKF and sigma-point filters, KF banks).
    • The particle filter: theory, implementation, proposal methods, sampling principles, smoothing, practical aspects.
    • Time synchronization and coordinate system calibration in filtering.
  • Dynamic state dimension problems.
    • Multi-target tracking: association, track handling
    • Simultaneous localization and tracking (SLAM).
  Practice
  • Sensors, sensor models H(y,x,e)=0 and sensor-near signal processing.
    • Wheel speed sensors and odometry.
    • IMU and dead-reckoning.
    • GPS.
    • Camera
    • Radar, laser, sonar
    • Networked sensors: radio measurements, microphones, seismometers, magnetic field sensors
  • Motion models dx/dt=f(x,u,v)
    • Multi-purpose motion models
    • Standard models for different platforms (wheeled vehicles, surface and underwater vessels, aircraft,...).
    • Extended target models (track before detect, grid based methods for fusion)

Organizer

Fredrik Gustafsson , tel 282706 e-mail: fredrik_at_isy.liu.se.

Lectures

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