Discussion
A Kalman filter improves on a fixed complementary filter because it
Answer: C
Correct answer: Weights by estimated uncertainty
A Kalman filter carries a covariance matrix representing its current uncertainty in each state, and it weights each new measurement against that uncertainty. The blending is therefore adaptive: when the accelerometers are disturbed by manoeuvring their measurement noise is large and they are trusted less, whereas a fixed complementary filter applies the same crossover regardless.
It also estimates states that are not measured directly, such as the gyro biases themselves. It does require a model of the system - that is what propagates the states between measurements - and it needs neither more sensors nor a higher update rate than the filter it replaces.
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