Body: <p>I'm using <a href="https://en.wikipedia.org/wiki/Kalman_filter" rel="nofollow noreferrer">Kalman Filter</a> approaches and I've just implemented the <a href="https://en.wikipedia.org/wiki/Extended_Kalman_filter" rel="nofollow noreferrer">extended Kalman filter (EKF)</a> with my object 2D trajectory. However, I have a mess of alternative approaches that may fit better like Unscented Kalman Filter (UFK), <a href="https://en.wikipedia.org/wiki/Particle_filter" rel="nofollow noreferrer">particle filters</a>, <a href="https://en.wikipedia.org/wiki/Adaptive_filter" rel="nofollow noreferrer">adaptive filtering</a>, etc.</p>
  <p>How can I choose the most suitable algorithm for my case? In addition, are there algorithms that can predict more than one step ahead?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-08T11:22:24.590Z
Comments.Text: Hello. Welcome to AI SE! I've added a few links to the corresponding Wikipedia pages for more context about these concepts. Please, make sure that I've not changed anything. Feel free to add also a link to some reference that describes "Unscented Kalman Filter (UFK)", which I've not added. Moreover, maybe you should describe more in detail your problem. "2d trajectory prediction" is not very descriptive. Maybe if you provide more details about your specific problem the answer will be more specific.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-08T11:47:16.310Z
Comments.Text: Thanks for the links. I'll change the title. My question is more advice about which approach to go with.
Comments.UserDisplayName: R2D2
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-08T14:12:10.237Z
Comments.Text: All predictors can predict more than one step using recursion.
Comments.UserDisplayName: Brian O'Donnell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-08T17:00:24.973Z
Comments.Text: As UKF it's recommended over EKF for efficiency. Which is the best implementation (python vs acc/cpu efficiency?
Comments.UserDisplayName: R2D2
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-03T15:51:55.207Z
Comments.Text: Somehow related:  https://stats.stackexchange.com/questions/391979/how-to-predict-routes-using-clustering-data,   https://stats.stackexchange.com/questions/125318/machine-learning-algorithm-to-predict-next-users-destination/125331#125331
Comments.UserDisplayName: kjetil b halvorsen
Tags: <prediction><autonomous-vehicles><forecasting><kalman-filter>
Title: Which is the best algorithm to predict the trajectory of a vehicle using lat/lon data?
UserDisplayName: R2D2
ThreadText: # Which is the best algorithm to predict the trajectory of a vehicle using lat/lon data?
  
  (Asked by: username_0 on 2021-01-08)
  
  I'm using [Kalman Filter](https://en.wikipedia.org/wiki/Kalman_filter) approaches and I've just implemented the [extended Kalman filter (EKF)](https://en.wikipedia.org/wiki/Extended_Kalman_filter) with my object 2D trajectory. However, I have a mess of alternative approaches that may fit better like Unscented Kalman Filter (UFK), [particle filters](https://en.wikipedia.org/wiki/Particle_filter), [adaptive filtering](https://en.wikipedia.org/wiki/Adaptive_filter), etc.
  
  How can I choose the most suitable algorithm for my case? In addition, are there algorithms that can predict more than one step ahead?
  
  **Tags:** prediction, autonomous-vehicles, forecasting, kalman-filter
  
  > **Comments:**
  > **username_1**: Hello. Welcome to AI SE! I've added a few links to the corresponding Wikipedia pages for more context about these concepts. Please, make sure that I've not changed anything. Feel free to add also a link to some reference that describes "Unscented Kalman Filter (UFK)", which I've not added. Moreover, maybe you should describe more in detail your problem. "2d trajectory prediction" is not very descriptive. Maybe if you provide more details about your specific problem the answer will be more specific.  
  > **username_0**: Thanks for the links. I'll change the title. My question is more advice about which approach to go with.  
  > **username_2**: All predictors can predict more than one step using recursion.  
  > **username_0**: As UKF it's recommended over EKF for efficiency. Which is the best implementation (python vs acc/cpu efficiency?  
  > **username_3**: Somehow related:  https://stats.stackexchange.com/questions/391979/how-to-predict-routes-using-clustering-data,   https://stats.stackexchange.com/questions/125318/machine-learning-algorithm-to-predict-next-users-destination/125331#125331  
  
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  ## 0 answers
  
  