Data-driven techniques in signal restoration and detection problems
Lim, Teck-Yian
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Permalink
https://hdl.handle.net/2142/115945
Description
Title
Data-driven techniques in signal restoration and detection problems
Author(s)
Lim, Teck-Yian
Issue Date
2022-07-15
Director of Research (if dissertation) or Advisor (if thesis)
Do, Minh N
Doctoral Committee Chair(s)
Do, Minh N
Committee Member(s)
Schwing, Alexander G
Forsyth, David A
Gupta, Saurabh
Wang, Yuxiong
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
FMCW radar, image restoration, sensor fusion
Abstract
With the abundance of data and affordable computational power, data-driven approaches have exploded in the last few years, solving various problems in the field of computer vision with performance exceeding human performance. In this work, we study how learned priors can be applied to the task of image restoration, without having to explicitly train for such a task. We also study how traditional signal processing chains for radars can be augmented with modern data-driven techniques. Our experiments showed that there is sufficient information in a radar heatmap to reliably identify the class of the reflecting object. Using an automatically labeled dataset, we were able to achieve a classification accuracy of over 85% in the indoor scenario and over 98% in the outdoor scenario. Our evaluation, performed on real world data, suggests that the complementary nature of radar and camera signals can be leveraged to reduce the lateral error by 15% when applied to object detection Finally, we also propose a novel method for combining multiple sensor observations in a learned feature space, demonstrating robustness to sensor failure.
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