Design of Ultrasonic Synthetic Aperture Imaging Systems Based on a Non-Grid 2D Sparse Array

This work provides a guide to design ultrasonic synthetic aperture systems for non-grid two-dimensional sparse arrays such as spirals or annular segmented arrays. It presents an algorithm that identifies which elements have a more significant impact on the beampattern characteristics and uses this information to reduce the number of signals, the number of emitters and the number of parallel receiver channels involved in the beamforming process. Consequently, we can optimise the 3D synthetic aperture ultrasonic imaging system for a specific sparse array, reducing the computational cost, the hardware requirements and the system complexity. Simulations using a Fermat spiral array and experimental data based on an annular segmented array with 64 elements are used to assess this algorithm.

A Novel Sparse Array Configuration with Low Coarray Redundancy for DOA Estimation in Mobile Wireless Sensor Network

For tacking and localizing sources in the mobile wireless sensor network, underdetermined direction of arrival (DOA) estimation with high-accuracy is a crucial issue. In this paper, a novel sparse array configuration is developed for accurate DOA estimation from the perspective of sum-difference coarray (SDCA). As compared with most of the existing sparse array configurations, the proposed array can effectively reduce the overlap between difference coarray (DCA) and sum coarray (SCA) and can achieve more consecutive degrees of freedom (DOF), more sources can be resolved accordingly. Additionally, the proposed array has hole-free DCA and SDCA. Then, the concept of coarray redundancy ratio (CRR) is introduced for evaluating the coarray overlap quantitatively and the closed-form CRR expressions of the proposed array are derived in detail. Based on the good properties of the proposed array, vectorized conjugate augmented MUSIC (VCAM) is adopted for underdetermined DOA estimation. The theoretical propositions and numerical simulations demonstrate the superior performance of the proposed array in terms of CRR, consecutive DOF, and DOA estimation accuracy.

DOA Estimation of a Space-limited MIMO Radar with High Degree of Freedom

Aiming at the problem of the small aperture of the traditional MIMO radar with virtual degrees of freedom, this paper designs a high degree of freedom space-limited MIMO radar. Both the transmitting and receiving elements of this radar adopt a sparse array structure. Array composition, the receiving array element is composed of a single array element and a uniform linear array. The number of virtual array elements can be realized by using array elements. Compared with the traditional sparse array MIMO radar with the same number of elements, the designed space-limited sparse array MIMO radar has a larger aperture. Experimental simulations verify the superiority of the space-limited MIMO radar angle estimation.

Compilation of sparse array programming models

This paper shows how to compile sparse array programming languages. A sparse array programming language is an array programming language that supports element-wise application, reduction, and broadcasting of arbitrary functions over dense and sparse arrays with any fill value. Such a language has great expressive power and can express sparse and dense linear and tensor algebra, functions over images, exclusion and inclusion filters, and even graph algorithms. Our compiler strategy generalizes prior work in the literature on sparse tensor algebra compilation to support any function applied to sparse arrays, instead of only addition and multiplication. To achieve this, we generalize the notion of sparse iteration spaces beyond intersections and unions. These iteration spaces are automatically derived by considering how algebraic properties annotated onto functions interact with the fill values of the arrays. We then show how to compile these iteration spaces to efficient code. When compared with two widely-used Python sparse array packages, our evaluation shows that we generate built-in sparse array library features with a performance of 1.4× to 53.7× when measured against PyData/Sparse for user-defined functions and between 0.98× and 5.53× when measured against SciPy/Sparse for sparse array slicing. Our technique outperforms PyData/Sparse by 6.58× to 70.3×, and (where applicable) performs between 0.96× and 28.9× that of a dense NumPy implementation, on end-to-end sparse array applications. We also implement graph linear algebra kernels in our system with a performance of between 0.56× and 3.50× compared to that of the hand-optimized SuiteSparse:GraphBLAS library.