Sparse Active Rectangular Array with Few Closely Spaced Elements

Sparse sensor arrays offer a cost effective alternative to uniform arrays. By utilizing the co-array, a sparse array can match the performance of a filled array, despite having significantly fewer sensors. However, even sparse arrays can have many closely spaced elements, which may deteriorate the array performance in the presence of mutual coupling. This paper proposes a novel sparse planar array configuration with few unit inter-element spacings. This Concentric Rectangular Array (CRA) is designed for active sensing tasks, such as microwave or ultra-sound imaging, in which the same elements are used for both transmission and reception. The properties of the CRA are compared to two well-known sparse geometries: the Boundary Array and the Minimum-Redundancy Array (MRA). Numerical searches reveal that the CRA is the MRA with the fewest unit element displacements for certain array dimensions.Comment: 4+1 pages, 5 figures, 1 tabl

Enhanced High-Resolution Imaging through Multiple-Frequency Coarray Augmentation

In imaging, much attention is paid to increasing the resolution capabilities of a system. Increasing resolution allows for high-accuracy source location and the ability to discriminate between two closely-spaced objects. In conventional narrowband techniques, resolution is fundamentally limited by the size of the aperture. For apertures consisting of individual elements, direction-of-arrival techniques allow for high-resolution images of point sources. The main limiting factor on conventional high-resolution imaging is the number of elements in the aperture. For both passive and active imaging, to resolve K point sources/targets, there must be at least K+1 elements receiving radiation. In active imaging, when these targets reflect coherently - the more difficult case in imaging - an additional constraint is that at least K of the elements must also be transmitting radiation to illuminate the targets. For small arrays consisting of only a few elements, this constraint can be problematic. In this dissertation, we focus on improving resolution by using multiple frequencies in both passive and active imaging, especially for small arrays. Using multiple frequencies increases the size of the coarray, which is the true limiting factor for resolution of an imaging system when virtual arrays are considered. For passive imaging, we show that the number of sources that can be resolved is limited only by the bandwidth available for certain types of sources. In active imaging, we develop a frequency-averaging method that permits resolution of K coherent point targets with fewer than K transmitting and receiving elements. These methods are investigated primarily for linear arrays, but planar arrays are also briefly examined. Another resolution improvement method researched in this work is a retransmission scheme for active imaging using classical beamforming techniques. In this method, the coarray is extended not by using multiple frequencies, but by retransmitting the received data back into the scene as a second transmission and processing the returns. It is known that when this method is used to image multiple targets, the resulting image is contaminated by crossterms. We investigate methods to reduce the crossterms

Hybrid Beam-Steering OFDM-MIMO Radar: High 3-D Resolution With Reduced Channel Count

We report on the realization of a multichannel imaging radar that achieves uniform 2-D cross-range resolution by means of a linear array of a special form of leaky-wave antennas. The presented aperture concept enables a tradeoff between the available range resolution and a reduction in the number of channels required for a given angular resolution. The antenna front end is integrated within a multichannel radar based on stepped-carrier orthogonal frequency-division modulation, and the advantages and challenges specific to this combination are analyzed with respect to signal processing and a newly developed calibration routine. The system concept is fully implemented and verified in the form of a mobile demonstrator capable of soft real-time 3-D processing. By combining radio frequency (RF) components operating in the W-band (85-105 GHz) with the presented aperture, a 3-D resolution of less than 1.5° x 1.5° x 15 cm is demonstrated using only eight transmitters and eight receivers

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Compressive and Noncompressive Power Spectral Density Estimation from Periodic Nonuniform Samples

This paper presents a novel power spectral density estimation technique for band-limited, wide-sense stationary signals from sub-Nyquist sampled data. The technique employs multi-coset sampling and incorporates the advantages of compressed sensing (CS) when the power spectrum is sparse, but applies to sparse and nonsparse power spectra alike. The estimates are consistent piecewise constant approximations whose resolutions (width of the piecewise constant segments) are controlled by the periodicity of the multi-coset sampling. We show that compressive estimates exhibit better tradeoffs among the estimator's resolution, system complexity, and average sampling rate compared to their noncompressive counterparts. For suitable sampling patterns, noncompressive estimates are obtained as least squares solutions. Because of the non-negativity of power spectra, compressive estimates can be computed by seeking non-negative least squares solutions (provided appropriate sampling patterns exist) instead of using standard CS recovery algorithms. This flexibility suggests a reduction in computational overhead for systems estimating both sparse and nonsparse power spectra because one algorithm can be used to compute both compressive and noncompressive estimates.Comment: 26 pages, single spaced, 9 figure