An implementation of online approximator for an extended Kalman filter tracker implemented over FPGA is presented.
Please 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.
Also, please run Demo_Exact_Computation.m for exact computation, i.e target tracking with an extended Kalman filter without any approximation.
@inproceedings{Emambakhsh:2017,
title={Learning to approximate computing at run-time},
author={P. Garcia, M. Emambakhsh and A. Wallace},
booktitle={IET 3rd International Conference on Intelligent Signal Processing (ISP)},
pages={1--8},
year={2017}
}