2013-12-23 02:41:24 -0500 | received badge | ● Student (source) |

2013-12-23 02:16:22 -0500 | asked a question | Kalman filter: motion model Hi all, I'm using a Kalman filter to estimate the position of an object ( (x,y) coordinates ). Regarding the state equations, I've seen the following approaches in different examples: ## 1) Auto regressive model:x[t+1] = x[t] + V[t] + noise where V[t] = (x[t] - x[t-1]) Thus: x[t+1] = x[t] + (x[t] - x[t-1]) + noise = 2x[t] - x[t-1] + noise In this case, both x[t] and x[t-1] are kept in the state vector ## 2) Velocity estimation:x[t+1] = x[t] + V[t] + noise where V[t] = V[t-1] + noise In this case, only x[t] and V[t] are kept in the state vector. So my question is: is there really a difference between both approaches? Is any of them better than the other in certain situations? Thanks a lot! Kind regards, Arno |

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