Placeholder chapter page. Sections are produced by the book-skills pipeline.
Sections
- 9.1 Hidden state and observation models
- 9.2 Recursive Bayesian estimation
- 9.3 The linear Kalman filter
- 9.4 Tuning process and measurement noise
- 9.5 Observability and divergence
- 9.6 Smoothing vs filtering
- 9.7 Failure modes and diagnostics
Lab 9
implement a Kalman filter for noisy position tracking; extend it to sensor dropout.