What does the Kalman filter do?
The Kalman filter produces an optimal (H2) estimate of the system state based on the different sensor inputs. It uses a user-defined noise estimate matrix that allows tuning for different sensor process noise levels, as well as a “trust factor” that defines how much faith the estimate should place in each of the sensors. It also builds a run-time covariance matrix that does something (help!). • 3.2: How does it compare to the complimentary filter? Aaron wrote something about this in the autopilot-devel archives that should be included here. • 3.3: What is the computational complexity of the filter? It depends on the size of the system being estimated. For an N state system with M inputs, the filter requires: NxM transpose MxN * NxN * NxM multiplication MxM addition MxM inverse NxN * NxM * M*M M additions NxM * MxN * NxN multiplication NxN addition The covariance update portion requires: Two NxN * NxN multiplications NxN transpose NxN addition This is in addition to the system specific
