International Journal of Microwave Science and Technology

Volume 2016, Article ID 5787895, 21 pages

http://dx.doi.org/10.1155/2016/5787895

## Ultrawideband Noise Radar Tomography: Principles, Simulation, and Experimental Validation

^{1}Pennsylvania State University, University Park, PA 16802, USA^{2}Air Force Research Laboratory, WPAFB, Dayton, OH 45433, USA

Received 2 November 2015; Accepted 15 March 2016

Academic Editor: Walter De Raedt

Copyright © 2016 Hee Jung Shin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The paper introduces the principles, simulation results, and hardware implementation of ultrawideband (UWB) noise radar for obtaining tomographic images of various scenarios of rotating cylindrical objects using independent and identically distributed UWB noise waveforms. A UWB noise radar was designed to transmit multiple UWB random noise waveforms over the 3–5 GHz frequency range and to measure the backward scattering data for the validation of the theoretical analysis and numerical simulation results. The reconstructed tomographic images of the rotating cylindrical objects based on experimental results are seen to be in good agreement with the simulation results, which demonstrates the capability of UWB noise radar for complete two-dimensional tomographic image reconstruction of various shaped metallic and dielectric target objects.

#### 1. Introduction

Noise radar has been developed and refined for covert detection and imaging applications over the past few decades due to several advantages, such as low probability of interception (LPI) [1–3], electronic counter-countermeasure (ECCM) [4–6], and relatively simple hardware configurations [7–9]. Further, ultrawideband (UWB) noise radar has achieved high-resolution detection and imaging in foliage penetration (FOPEN) imaging [10, 11], through-wall imaging (TWI) [12–14], and ground penetration radar (GPR) imaging [15, 16] by employing an ultrawide bandwidth. For obtaining high-resolution images of various shaped targets, tomographic radar imaging is emerging as a powerful imaging technique [17–19]. In general, radar imaging techniques tend to be formulated in the time domain to use computationally efficient back-projection algorithms and to provide accurate object shape and location results of the target objects [20–22]. On the other hand, the objective of tomography-based radar imaging is to characterize the material property profiles of the target objects from the scattered fields and reconstruct specific scatterers within the interrogation medium by solving the inverse scattering problem [23–26].

Practical tomography systems have been developed and implemented in a way that the multidimensional images of the target object are successfully obtained from electromagnetic scattering with various waveforms [27–29]. The requirement when using this method is the need for mechanical rotation of either the body or the antenna in order to obtain measurements in different views [30–32]. Every object, when inserted into an electromagnetic field, causes a well-defined field change, and diffraction occurs when the wavelength of the radiation is of the order of the dimension of the object. A one-to-one relationship relating the scattered field to the object complex permittivity can be obtained within the Born approximation, via a Fourier transform using the so-called Fourier diffraction theorem [33–35].

This paper introduces the principle, simulation, and experiment results of tomographic imaging of various target scenarios using diffraction tomography for a bistatic UWB noise radar. Since the frequency behavior of the electrical parameters of the target objects due to the illuminating electromagnetic fields depends on the frequency used in the system, UWB imaging clearly provides advantages over single or narrow band frequency operation in terms of resolution and accuracy. Furthermore, transmitting random noise waveform inherits the aforementioned advantages of noise radar. The paper is organized as follows. In Section 2, theoretical analysis of the image reconstruction for a bistatic noise radar system with two-dimensional scattering geometry using Fourier diffraction theorem under the assumption of plane wave illumination is presented. The numerical simulation results of diffraction tomography using UWB random noise waveforms are presented in Section 3. Section 4 describes the RF hardware implementation and experimental results from that noise radar system. Further, we compare the images obtained with simulations and experiments. Conclusions are drawn in Section 5.

#### 2. Theory and Formulation

The main advantage of transmitting a random noise waveform is to covertly detect and image a target without alerting the presence of radar system. Such LPI characteristic of the noise radar is achieved by using a random noise waveform, which is constantly varying and never repeats itself exactly [36, 37]. The random noise waveform herein is mathematically defined as discrete time wide-sense stationary (WSS) and ergodic random process and a sequence of independent and identically distributed (i.i.d.) random variable drawn from a normal distribution, . The power spectral density (PSD) of white Gaussian noise (WGN) waveform is ideally a nonzero constant for all frequencies. In practical situations, however, the PSD of the random noise waveform may not be uniform across the wide frequency ranges because a finite number of random noise amplitude samples must be chosen for WGN waveform generation. In order to bypass such a shortcoming, multiple sequences of i.i.d. noise waveforms are transmitted to flatten the spectral density without changing the mean and variance [38].

Figure 1 shows two-dimensional backward scattering geometry for a cylindrical target object. The values of receiver spacing, , and the frequency sampling interval, , are determined based on the maximum operating frequency and the size of the object, respectively, to ensure an image that is free of false aliases [39–41].