Nearly orthogonal, doppler tolerant waveforms and signal processing for multi-mode radar applications

<p>In this research, we investigate the design and analysis of nearly orthogonal, Doppler tolerant waveforms for diversity waveform radar applications. We then present a signal processing framework for joint synthetic aperture radar (SAR) and ground moving target indication (GMTI) processing that is built upon our proposed waveforms. ^ To design nearly orthogonal and Doppler tolerant waveforms, we applied direct sequence spread spectrum (DSSS) coding techniques to linear frequency modulated (LFM) signals. The resulting transmitted waveforms are rendered orthogonal using a unique spread spectrum code. At the receiver, the echo signal can be decoded using its spreading code. In this manner, transmit orthogonal waveforms can be matched filtered only with the intended receive signals. ^ Our proposed waveforms enable efficient SAR and GMTI processing concurrently without reconfiguring a radar system. Usually, SAR processing requires transmit waveforms with a low pulse repetition frequency (PRF) rate to reduce range ambigu- ity; on the other hand, GMTI processing requires a high PRF rate to avoid Doppler aliasing and ambiguity. These competing requirements can be tackled by employing some waveforms (with low PRF) for the SAR mission and other waveforms (with high PRF) for the GMTI mission. Since the proposed waveforms allow separation of individual waveforms at the receiver, we can accomplish both SAR and GMTI processing jointly</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year

2014

Author dc:creator

• Majumder, Uttam Kumar

Contributors dc:contributor

• Mark R. Bell
• David J. Love
• Michael D. Zoltowski
• Muralidhar Rangaswamy

• Electrical and Computer Engineering
• Electrical and Electronics

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/328

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1432