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From Theory to Practice: Randomly Sampled Arrays for Passive Radar

Elgayar, Saad M
http://orcid.org/0000-0002-9699-9212
http://rave.ohiolink.edu/etdc/view?acc_num=osu1503304471335023

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Passive radar is a type of radar sensor that exploits non-cooperative radio frequency transmissions to detect, localize and track targets of interest. A plethora of sources of illumination were suggested in previous work including many wireless communication signals such as GSM, OFDM, WIFI, DVB-T and DTV. Using these ubiquitously available sources of RF energy enables covert operation, however this comes with the penalty of lower performance in terms of sensitivity, resolution, and ambiguity when compared to active monostatic radar systems. Improving passive radar system performance through improved receive processing algorithms has been the focus of researchers in the past few decades. In our work, we propose alternative processing techniques that can improve detection performance in two distinct problem settings. First, we study widely separated passive radar receiver scenarios, most notably with direct path signal obstruction and imprecise knowledge of transmitter positions. In this setting, the joint detection of the target and active propagation paths can be posed as a model order selection problem. We formulate a composite detection problem with unknown reference transmitted signal parameters and unknown model order. We derive penalty terms for Bayesian Information Criterion (BIC) and Exponentially Embedded Family (EEF) methods of model order selection. Simulation experiments show that properly modified BIC outperforms the alternatives at low and high SNR. Second, we consider passive radar systems that employ randomly subsampled antenna arrays with access to a noisy copy of the transmitted signal. In this setting, the sparsity of targets within a discretized angle-range-Doppler domain is a natural assumption. Hence, we propose applying spatial compressive sensing with matched filtering techniques to a collocated randomly subsampled passive antenna array system to accomplish a system with higher angular resolution and lower hardware complexity. A sparse localization framework is introduced by randomly activating a small subset of a uniform linear array (ULA) elements at a time and applying compressive sensing (CS) to the subsampled acquired data to recover the full ULA high resolution. Adapting a CS inversion scheme, the search for target position on a three-dimensional grid (range-angle-Doppler) is formulated as a linear underdetermined inverse problem. For this inversion problem we derive theoretical performance guarantees to provide a bound on the number of detectable targets as a function of resolution and system parameters. Finally, we validate our theoretical findings using controlled experiments with a passive radar system. Our design for the passive radar system uses a wideband recorder system and a configurable linear array.
Emre Ertin (Advisor)
130 p.
Electrical Engineering
Passive Radar, Compressive sensing, randomly subsampled arry, Spatial compressive sensing, matched filter compressive sensing

Recommended Citations

Citations

  • Elgayar, S. M. (2017). From Theory to Practice: Randomly Sampled Arrays for Passive Radar [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503304471335023

    APA Style (7th edition)

  • Elgayar, Saad. From Theory to Practice: Randomly Sampled Arrays for Passive Radar. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1503304471335023.

    MLA Style (8th edition)

  • Elgayar, Saad. "From Theory to Practice: Randomly Sampled Arrays for Passive Radar." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503304471335023

    Chicago Manual of Style (17th edition)

osu1503304471335023
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© 2017, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.