mehryaragha/Approxi​mate_Filtering

Version 2.0.0.0 (13.3 KB) by Mehryar Emambakhsh
Extended Kalman Filtering Exact and Approximation
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Updated 2 Oct 2017
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Approximate Computing for an Extended Kalman Filtering Tracker
An implementation of the paper: P. Garcia, M. Emambakhsh, and A. Wallace, “Learning to approximate computing at run-time,” IET 3rd International Conference on Intelligent Signal Processing (ISP 2017), to appear.

Run Demo_detailed_visualisation.m and Demo.m to simulate Figure 4 of the paper, which contains dynamic approximation via four different Kullback–Leibler (KL) thresholds.

Run Demo_Exact_Computation.m for exact computation, i.e target tracking with an extended Kalman filter without any approximation.

Cite As

Mehryar Emambakhsh (2024). mehryaragha/Approximate_Filtering (https://github.com/mehryaragha/Approximate_Filtering), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2017b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes
2.0.0.0

More detailed visualisation of the output, using the Dynamic_approximation_detailed_visualisation.m and Demo_detailed_visualisation.m functions.
1.0.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.