Full-Wave Modelling of Ground-Penetrating Radars: Antenna Mutual Coupling Phenomena and Sub-Surface Scattering Processes

Ground-penetrating radar (GPR) technology finds applications in many areas such as geophysical prospecting, archaeology, civil engineering, environmental engineering, and defence applications as a non-invasive sensing tool [3], [6], [18]. One key component in any GPR system is the receiver/transmitter antenna. Desirable features for GPR antennas include efficient radiation of ultra-wideband pulses into the ground, good impedance matching over the operational frequency band, and small size. As the attenuation of radio waves in geophysical media increases with frequency [9], [13], ground-penetrating radars typically operate at frequencies below 1GHz [4]. For either impulse [13] or steppedfrequency continuous-wave applications [17], the wider the frequency range, the better the range resolution of the radar. Continuous wave multi-frequency radars are advantageous over impulse radars in coping with dispersion of the medium, the noise level at the receiver end, and the controllability of working frequency. It requires, however, mutual coupling between the transmit (Tx) and receive (Rx) antennas, which determines the dynamic range of the sys-tem, to be kept as small as possible [12]

Generalized Matrix-Pencil Approach to Estimation of Complex Exponentials with Gapped Data

A generalized matrix-pencil approach is proposed for the estimation of complex exponential components with segmented signal samples, which is very efficient and provides super-resolution estimations. It is applicable to the signals sampled segmentally with the same sampling frequency and direction of arrival (DOA) estimation with distributed arrays within which array elements are placed uniformly with the same inter-element spacing.Comment: 5 pages, 6 Figure

Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network to extrapolate the antenna element's response. Afterward, the trained network is used to enhance the angular resolution of compact, low-cost radars. One million scenarios are simulated in a Monte-Carlo fashion, varying the number of targets, their Radar Cross Section (RCS), and location to evaluate the method's performance. Finally, the method is tested in real automotive data collected outdoors with a commercial radar system. A significant increase in the ability to resolve targets is demonstrated, which can translate to more accurate and faster responses from the planning and decision making system of the vehicle.Comment: Under revision at IEEE Transactions on Vehicular Technolog

Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition

In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking advantage of these different time and frequency features between the useful signals and the interferences, the interference mitigation is formulated as an optimization problem: a sparse and low-rank decomposition of a Hankel matrix constructed by lifting the measurements. Then, an iterative optimization algorithm is proposed to tackle it by exploiting the Alternating Direction of Multipliers (ADMM) scheme. Compared to the existing methods, the proposed approach does not need to detect the interference and also improves the estimation accuracy of the separated useful signals. Both numerical simulations with point-like targets and experiment results with distributed targets (i.e., raindrops) are presented to demonstrate and verify its performance. The results show that the proposed approach is generally applicable for interference mitigation in both stationary and moving target scenarios.Comment: 12 pages, 8 figure

A Through-the-Wall Radar Imaging Method Based on a Realistic Model

An image focusing method based on a realistic model for a wall is proposed for through-the-wall radar imaging using a multiple-input multiple-output array. A technique to estimate the wall parameters (i.e., position, thickness, and permittivity) from the radar returns is developed and tested. The estimated wall properties are used in the developed penetrating image formation to form images. The penetrating image formation developed is computationally efficient to realize real-time imaging, which does not depend on refraction points. The through-the-wall imaging method is validated on simulated and real data. It is shown that the proposed method provides high localization accuracy of targets concealed behind walls