Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/71241
Title: Radar-signal-based classification of man-made objects – A
Authors: Wang, Chen
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2017
Abstract: In recent years, using radar for monitoring Unmanned Aerial Vehicle (UAV) and distinguishing UAV from other slow-moving objects is a critical and popular topic among scientists and researchers. This is mainly because while UAVs are able to perform certain tasks, it may also create some potential invasions of privacy, especially in the defense-related area. Therefore, radar signal processing techniques and methods to extract radar echoes to build ideal classification databases are crucial in order to further enhance different kinds of radars’ accuracy and ability to distinguish UAVs and other slow-moving targets. In the first phase of this project, various signal-processing methodologies behind different operating radar systems were studied and analyzed. An improved MTI filter was also designed to enhance ground clutters suppression performance. Comparison between the improved MTI filter and traditional MTI filter was conducted. In order to have a direct understanding of the radar operating system, a Monostatic Radar System was also simulated. For the second phase, different experimental data collection scenarios for both CW and FMCW radar were planned and executed. Various radar signal-processing methods were applied. For CW radar, spectrograms comparison of targets at different ranges and targets with different rotational blade speeds were conducted to have a preliminary conclusion. And for FMCW radar, Range-Doppler maps were compared for different targets and targets at different ranges. Classification databases for different UAVs and slow-moving targets were built at the end.
URI: http://hdl.handle.net/10356/71241
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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