經(jīng)典kalman濾波ppt(編輯修改稿)
2024-09-01 10:56 本頁面
【文章內容簡介】 ate = Variance of prediction * (1 – Kalman Gain) 11 0 10 20 30 40 50 60 70 80 90 10000 . 0 20 . 0 40 . 0 60 . 0 80 . 10 . 1 20 . 1 40 . 1 6Conceptual Overview ? At time t3, boat moves with velocity dy/dt=u ? Na239。ve approach: Shift probability to the right to predict ? This would work if we knew the velocity exactly (perfect model) ?(t2) Na239。ve Prediction ?(t3) 12 0 10 20 30 40 50 60 70 80 90 10000 . 0 20 . 0 40 . 0 60 . 0 80 . 10 . 1 20 . 1 40 . 1 6Conceptual Overview ? Better to assume imperfect model by adding Gaussian noise ? dy/dt = u + w ? Distribution for prediction moves and spreads out ?(t2) Na239。ve Prediction ?(t3) Prediction ?(t3) 13 0 10 20 30 40 50 60 70 80 90 10000 . 0 20 . 0 40 . 0 60 . 0 80 . 10 . 1 20 . 1 40 . 1 6Conceptual Overview ? Now we take a measurement at t3 ? Need to once again correct the prediction ? Same as before Prediction ?(t3) Measurement z(t3) Corrected optimal estimate ?(t3) 14 Conceptual Overview ? Lessons learnt from conceptual overview: – Initial conditions (?k1 and ?k1) – Prediction (?k , ?k) ? Use initial conditions and model (eg. constant velocity) to make prediction – Measurement (zk) ? Take measurement – Correction (?k , ?k) ? Use measurement to
環(huán)評公示相關推薦
鄂ICP備17016276號-1