The way I understand it, in a linear system with discrete measurements where you move and measure your position, averaging last 3 measurements, you are effectively lagging 1 step behind your actual position.
What Kalman filter does is that it estimates your position and then averages measurement with that, in essence bringing the value closer to where you are at the moment.
Having a delay in a feedback loop may cause oscillations. If you react way slower than you measure, you might not need Kalman filter. Proposed GPS example is relevant here, because position updates come in slowly.
What Kalman filter does is that it estimates your position and then averages measurement with that, in essence bringing the value closer to where you are at the moment.
Having a delay in a feedback loop may cause oscillations. If you react way slower than you measure, you might not need Kalman filter. Proposed GPS example is relevant here, because position updates come in slowly.