Sensor Fusion
Sensor fusion benefits
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
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