Sensor Fusion

Sensor fusion benefits

    • One type of sensor cannot do the job alone
      • For a significant subset of applications, one positioning sensor is insufficient to meet requirements.
    • More is better or cheaper
      • When one sensor can meet requirements, a fusion of simpler sensors still might cost less
    • Robustness = availability
      • Sensor fusion is particularly attractive when requirements emphasize availability: multiple sensors have different failure modes and properly combined measurements will perform a ‘soft handover’ between sensors

Sensor fusion filter types

Each treats the probability distribution of possible solutions with a different level of compression:

    • MAP filter
      • Estimates full state probability distribution
      • Maximum aposteriori probability reported
    • Particle filter (PF)
      • Approximate probability distribution with discrete points
      • Hybrid PFs now being invented (ex: Rao-Blackwellized PF)
    • Unscented Kalman filter (UKF)
      • 3 points per eigenvector, good to 3rd moment of distribution. Susceptible to local minimum failure.
      • >>Numerical jacobian (NJ) simplification: eigenvectors replaced with state axes
    • Gaussian-sum Iterated Extended KF (IEKF)
      • Only 2nd moment stats, but deals with multimodal distributions
    • IEKF
      • iterated to linearize better locally, but still only 2nd moment. Susceptible to local minimum failure