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I think the magic acronym is CHCV, "constant heading constant velocity". This returns at least a few results on Google.

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You basically have 4 models here: Accelerating to constant speed. Moving at constant speed. Decelerating to zero speed. Standing. So the basic solution is building the 4 models and switching using Hard Switch between them. Yet there is a smoother framework to handle smooth transition between them called Interacting Multiple Model (IMM) Kalman Filter. Using ...

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My question is, is there a more appropriate model of a Kalman filter for the type of car that I'm trying to predict? Probably yes, because you're generating a command for the car that (I presume) you know, but you're not using that knowledge in the filter. The model you're using in the Kalman filter is \$\mathbf{x}_k = \mathbf{x}_{k-1} + \mathbf{w_k},\ \...

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My question is, is there a more appropriate model of a Kalman filter for the type of car that I'm trying to predict? No. (But also please see below). Although the model works well for constant velocity, there's a trailing when the velocity goes from V to zero as Fig. shown. Is there a good solution to that? Yes. The Kalman filter includes a term for "...

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Short Answer: Basic RLS (no forgetting, no weird weighting, etc.) is ALWAYS Lyapunov stable. If the regressor sequence for the LS problem is persistently exciting--which is data and problem dependent, not algorithm dependent--then RLS is exponentially stable. So I don't know what you mean by "LMS is more stable than RLS"--more stable in what sense? ...

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Hi: I've never dealt with discretization in the kalman filter ( my models were already discrete ) so take the following with a level of uncertainty ( no pun intended ). Also, you didn't show the original equations so I'll refer to them as the observation equation and the system equation. Based on your updating equations, it seems that you're using the non-...

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