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Compressive sampling in radar imaging

Sugavanam, Nithin
http://rave.ohiolink.edu/etdc/view?acc_num=osu1503277528254942

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Multi-channel wideband radar has proven to be an indispensable tool for many surveillance applications. However, achieving higher resolution with current architectures comes at the cost of lower dynamic range for the sensor. Recent theoretical advances in the area of compressive sensing provide a new framework for sampling and processing sensor signals at a rate that scales with the information content and complexity of the scene. For the case of delay estimation - a core problem in radar sensing - compressive sensing provides a theoretical guarantee for successful recovery using K\log (N/K) compressed measurements of K scatterers over a delay space of N bins. Previous practical implementations of compressive sampling radar attempted to reduce sampling complexity at the expense of increased complexity in receivers realizing unstructured random projections. In this thesis, we study the problem of developing structured acquisition systems that exploit the underlying structure of radar signals to provide provable performance guarantees and reduced design complexity . Broadly, our work is divided into two parts. In the first part, we present a compressive radar design that employs structured waveforms on transmit and reduced complexity sub-sampling on receive with recovery guarantees of target parameters at sub-Nyquist rates. The proposed framework lends itself to practical hardware implementation as it utilizes standard linear frequency modulated waveforms mixed with sinusoidal tones and receivers with an approximated matched filter termed as stretch processor and a uniform sampling rate Analog to digital converter (ADC). Also, this structure simplifies the calibration step in practical systems because the number of random elements is minimized. We extend this illumination approach to a multiple input and output (MIMO) radar architecture and establish uniform as well as non-uniform recovery guarantees, given a sufficient number of modulating tones. We also present a method for calibrating the system for a class of uncertainty that arises in practical implementations. In the second part, we consider wide-angle synthetic aperture radar (SAR) imaging with collocated transmitter and receiver. Wide-angle SAR plays a key role in solving the target recognition problem as this scheme obtains a detailed description of the scene as a function of both viewing angle and spatial coordinates. We recover the scattering coefficients as a function of spatial locations and viewing angle from incomplete and noisy measurements. We classify an observed sub-sequence of phase-history measurements based on the nearest neighbor method using the models obtained. We empirically verify the performance of such an approach by utilizing the probability of error as a metric. We note that the performance does not deteriorate as long as we have a sufficient number of samples. We extend this method to the bistatic setup with separated transmitter and receiver to recover the scattering coefficients as a function of the spatial locations, bistatic angle, and the bisector of the bistatic angle.
Emre Ertin (Advisor)
Lee Potter (Committee Member)
Yuejie Chi (Committee Member)
162 p.
Electrical Engineering
Compressive sampling; MIMO radar imaging; Monostatic synthetic aperture radar imaging; Bistatic synthetic aperture radar imaging

Recommended Citations

Citations

  • Sugavanam, N. (2017). Compressive sampling in radar imaging [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503277528254942

    APA Style (7th edition)

  • Sugavanam, Nithin. Compressive sampling in radar imaging. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1503277528254942.

    MLA Style (8th edition)

  • Sugavanam, Nithin. "Compressive sampling in radar imaging." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503277528254942

    Chicago Manual of Style (17th edition)

osu1503277528254942
609
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This open access ETD is published by The Ohio State University and OhioLINK.