In a Kalman filter, a 'fusing weight' refers to

• A. The gravitational weight in trajectory integration.
• B. The physical weight of sensors.
• C. The number of iterations performed.
• D. Observational and process noise weights that determine how much the filter trusts predictions vs measurements.

Answer: (D)

In a Kalman filter, the fusing weight is about how much the filter blends the predicted state with the new measurement. That blend is controlled by how reliable each source is, which comes from the process noise (uncertainty in the model) and the measurement noise (uncertainty in the sensors). The Kalman gain adjusts the correction based on these uncertainties: when measurements are trustworthy (low measurement noise) or the model is uncertain (high process noise, larger predicted error), more weight is given to the measurement; when measurements are noisy or the model is trusted more (low process noise), more weight stays with the prediction. So the fusing weight is essentially the observational and process noise weights that determine how much the filter trusts predictions versus measurements. This is not a physical weight or an iteration count.