In an increasing number of high-gradient linear accelerator applications, accelerating structures must operate with both high surface electric fields and low breakdown rates. Understanding the statistical properties of breakdown occurrence in such a regime is of practical importance for optimizing accelerator conditioning and operation algorithms, as well as of interest for efforts to understand the physical processes which underlie the breakdown phenomenon. Experimental data of breakdown has been collected in two distinct high-gradient experimental setups: A prototype linear accelerating structure operated in the Compact Linear Collider Xbox 12 GHz test stands, and a parallel plate electrode system operated with pulsed DC in the kV range. Collected data is presented, analyzed and compared. The two systems show similar, distinctive, two-part distributions of number of pulses between breakdowns, with each part corresponding to a specific, constant event rate. The correlation between distance and number of pulses between breakdown indicates that the two parts of the distribution, and their corresponding event rates, represent independent primary and induced follow-up breakdowns. The similarity of results from pulsed DC to 12 GHz rf indicates a similar vacuum arc triggering mechanism over the range of conditions covered by the experiments.

Abstract-Sparse sensor arrays can match the performance of fully populated arrays using substantially fewer elements. However, finding the array configuration with the smallest number of elements is generally a computationally difficult problem. Consequently, simple to generate array configurations that may be suboptimal are of high practical interest. This paper presents a novel closed-form sparse linear array configuration designed for active sensing, called the Concatenated Nested Array (CNA). The key parameters of the CNA are derived. The CNA is also compared to the optimal Minimum-Redundancy Array (MRA) in numerical simulations. The CNA is shown to require only about 10% more elements than the MRA in the limit of large apertures.

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.

Abstract-Sparse sensor arrays can match the performance of fully populated arrays in many tasks, such as direction-of-arrival estimation, using substantially fewer elements. However, finding the sparse array configuration that uses the smallest number of elements is generally a hard problem. Consequently, several closed-form, but sub-optimal solutions have been developed in the past. These designs are typically specified for a given number of elements, although when the area occupied by the array is the main limitation, it is more convenient to compare arrays of similar aperture instead. This paper outlines procedures for synthesizing three sparse linear array geometries for a specified aperture, namely the Wichmann, Nested and Super nested array. These configurations are compared to the optimal Minimumredundancy array and their deviation from optimality is quantified in the limit of large apertures.

This paper studies hybrid beamforming for active sensing applications, such as millimeter-wave or ultrasound imaging. Hybrid beamforming can substantially lower the cost and power consumption of fully digital sensor arrays by reducing the number of active front ends. Sparse arrays can be used to further reduce hardware costs. We consider phased arrays and employ linear beamforming with possibly sparse array configurations at both the transmitter and receiver. The quality of the acquired images is improved by adding together several component images corresponding to different transmissions and receptions. In order to limit the acquisition time of an image, we formulate an optimization problem for minimizing the number of component images subject to achieving a desired point spread function. Since this problem is not convex, we propose algorithms for finding approximate solutions in the fully digital beamforming case, as well as in the more challenging hybrid and analog beamforming cases that employ quantized phase shifters. We also determine upper bounds on the number of component images needed for achieving the fully digital solution using fully analog and hybrid architectures, and derive closed-form expressions for the beamforming weights in these cases. Simulations demonstrate that a hybrid sparse array with very few elements, and even fewer front ends, can achieve the resolution of a fully digital uniform array at the expense of a longer image acquisition time.