kalman-filter | Kalman Filter implementation in Python using Numpy | Widget library

by zziz Python Version: Current License: No License

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kandi X-RAY | kalman-filter Summary

kalman-filter is a Python library typically used in User Interface, Widget, Numpy applications. kalman-filter has no bugs, it has no vulnerabilities and it has high support. However kalman-filter build file is not available. You can download it from GitHub.

Implementation of Kalman filter in 30 lines using Numpy. All notations are same as in Kalman Filter Wikipedia Page. It is a generic implementation of Kalman Filter, should work for any system, provided system dynamics matrices are set up properly. Included example is the prediction of position, velocity and acceleration based on position measurements. Synthetic data is generated for the purpose of illustration.

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Support

kalman-filter has a highly active ecosystem.

It has 276 star(s) with 77 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 0 have been closed. On average issues are closed in 467 days. There are 1 open pull requests and 0 closed requests.

It has a positive sentiment in the developer community.

The latest version of kalman-filter is current.

Quality

kalman-filter has 0 bugs and 0 code smells.

Security

kalman-filter has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

kalman-filter code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

kalman-filter does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

kalman-filter releases are not available. You will need to build from source code and install.

kalman-filter has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 46 lines of code, 4 functions and 1 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed kalman-filter and discovered the below as its top functions. This is intended to give you an instant insight into kalman-filter implemented functionality, and help decide if they suit your requirements.

Example of the Kalman filter
Update the tensor
Predict for the model

Get all kandi verified functions for this library.

kalman-filter Key Features

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kalman-filter Examples and Code Snippets

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Community Discussions

Trending Discussions on kalman-filter

Including parameters in state space model from statsmodels

How can I compute the sigma points for UKF?

QUESTION

Including parameters in state space model from statsmodels

Asked 2022-Jan-03 at 16:00

Building up the model from a previous post, and the helpful answer, I've subclassed the MLEModel to encapsulate the model. I'd like to allow for two parameters q1 and q2 so that the state noise covariance matrix is generalized as in Sarkka (2013)'s example 4.3 (terms re-arranged for my convention):

I thought I would accomplish this with the update method below, but I'm running into problems with the fit method, as it returns a UFuncTypeError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind'. What am I missing here?

...

ANSWER

Answered 2022-Jan-03 at 16:00

The error message you are receiving is about trying to set a complex value in a dtype=float matrix. You would get the same error from:

Source https://stackoverflow.com/questions/70553360

QUESTION

How can I compute the sigma points for UKF?

Asked 2020-Dec-16 at 16:19

Image above & tutorial: https://towardsdatascience.com/the-unscented-kalman-filter-anything-ekf-can-do-i-can-do-it-better-ce7c773cf88d

I am confused about how to compute the sigma points for the unscented Kalman filter. For me, mu is a 2-dim vector, so n is 5 and cov is a 2x2 matrix. lambda is 3-n, so 1. Now, I don't understand the index i, since non of the values/matrices are dependent on i. What would be the difference between X[1] and X[2]?

Thanks for any help, I think I'm probably just confused.. :)

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ANSWER

Answered 2020-Dec-16 at 16:19

I realized i simply stands for the i'th columnn.

Source https://stackoverflow.com/questions/65281277

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install kalman-filter

You can download it from GitHub.
You can use kalman-filter like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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