ItemNear-Field 2-D SAR Imaging by Millimeter-Wave Radar for Concealed Item Detection(IEEE, 2019-01-20) Yanik, Muhammet Emin; Torlak, Murat; 0000-0001-7229-1765 (Torlak, M); Yanik, Muhammet Emin; Torlak, MuratRecent progress in complementary metaloxide semiconductor (CMOS) based frequency-modulated continuous-wave (FMCW) radars has made it possible to design low-cost and low-power millimeter-wave (mmWave) sensors. As a result, there is a strong desire to exploit the progress in mmWave sensors in wide range of imaging applications including medical, automotive, and security. In this paper, we present a low-cost high-resolution mmWave imager prototype that combines commercially available 77 GHz system-on-chip FMCW radar sensors and synthetic aperture radar (SAR) signal processing techniques for concealed item detection. To create a synthetic aperture over a target scene, the imager is constructed with a two-axis motorized rail system which can synthesize a large aperture in both horizontal and vertical directions. Our prototype system is described in detail along with signal processing techniques for two-dimensional (2-D) image reconstruction. The imaging examples of concealed items in various scenarios confirm that our low-cost prototype has a great potential for high-resolution imaging tasks in security applications. ItemImpact of Number of Noise Eigenvectors Used on the Resolution Probability of MUSIC(Institute of Electrical and Electronics Engineers Inc, 2019-01-31) Baral, Ashwin Bhobani; Torlak, Murat; 0000-0001-7229-1765 (Torlak, M); 0000-0002-5110-7727 (Baral, AB); Baral, Ashwin Bhobani; Torlak, MuratThe MUltiple SIgnal Classification (MUSIC) algorithm is a well-known eigenanalysis technique and has been studied extensively. The algorithm relies on accurate partitioning of the eigenvectors of the spatial correlation matrix between the signal (i.e., signal subspace) and noise eigenvectors (i.e., noise subspace). In this paper, we present a novel statistical framework for analyzing the resolution performance of the MUSIC algorithm in resolving closely spaced sources. The statistical framework is based on the first-order approximation of the perturbations in the noise subspace eigenvectors. Using this framework, we derive an analytical expression for the probability of resolution of the MUSIC algorithm according to the number of noise eigenvectors used in the spectrum computation. Such an investigation cannot be carried out with the existing probability of resolution expressions of the MUSIC algorithm. Using the analytical tools presented in this paper, it is possible to predict the resolution performance with respect to many important system parameters, i.e., signal-to-noise ratio (SNR), the number of samples, and the number of noise eigenvectors. For example, we found that the resolution threshold in terms of SNR is independent of the number of noise eigenvectors used. The simulation results are presented to verify the accuracy of the analytical expressions. More importantly, real radio-frequency experiments with a 24-GHz radar platform are carried out to demonstrate the resolution performance of MUSIC to support our findings in practical settings. ItemExact Outage Probability Analysis of Dual-Transmit-Antenna V-BLAST with Optimum Ordering(IEEE-Institute of Electrical Electronics Engineers Inc, 2018-11-09) Ozyurt, Serdar; Torlak, Murat; Torlak, MuratAn exact outage probability analysis of zero-forcing V-BLAST technique with two transmit antennas and r receive antennas (r ≥ 2) is provided under a decoding order that is optimum in the sense of minimizing the outage probability. Different from the earlier approximated analyses, an exact closed-form expression on the outage probability is attained without neglecting the effect of error propagation. Assuming a one-bit feedback from the receiver to the transmitter, the optimal power and rate allocation values are numerically computed based on the exact outage probability expression under different objective functions. The presented results are also applicable for the corresponding dual scenarios that may involve antenna selection and/or user scheduling. ItemNear-Field MIMO-SAR Millimeter-Wave Imaging With Sparsely Sampled Aperture Data(Institute of Electrical and Electronics Engineers Inc., 2019-03-04) Yanik, Muhammet Emin; Torlak, Murat; 0000-0001-8682-4577 (Yanik, ME); 0000-0001-7229-1765 (Torlak, M); Yanik, Muhammet Emin; Torlak, MuratThe primary challenge of a cost-effective and low-complexity near-field millimeter-wave (mmWave) imaging system is to achieve high resolution with a few antenna elements as possible. Multiple-input multiple-output (MIMO) radar using simultaneous operation of spatially diverse transmit and receive antennas is a good candidate to increase the number of available degrees of freedom. On the other hand, higher integration complexity of extremely dense transceiver electronics limits the use of MIMO only solutions within a relatively large imaging aperture. Hybrid concepts combining synthetic aperture radar (SAR) techniques and sparse MIMO arrays present a good compromise to achieve short data acquisition time and low complexity. However, compared with conventional monostatic sampling schemes, image reconstruction methods for MIMO-SAR are more complicated. In this paper, we propose a high-resolution mmWave imaging system combining 2-D MIMO arrays with SAR, along with a novel Fourier-based image reconstruction algorithm using sparsely sampled aperture data. The proposed algorithm is verified by both simulation and processing real data collected with our mmWave imager prototype utilizing commercially available 77-GHz MIMO radar sensors. The experimental results confirm that our complete solution presents a strong potential in high-resolution imaging with a significantly reduced number of antenna elements. © 2019 IEEE.