基于软件共享存储的Co-Array Fortran编译器实现

Co-Array Fortran(CAF)已经成为Fortran语言标准的一部分,在科学计算领域逐渐被接受。基于软件共享存储实现了一个CAF编译器,其通过直接的数组赋值实现Co-array数据通信,利用数据垫塞技术提高数据局部性,减少伪共享,优化CAF程序性能。典型科学计算程序测试表明,CAF能够获得和MPI相当的性能。

llo标量势差分计算的Co-array Fortran解决方案及算例

在64位Intel Visual Fortran开发环境下,基于Co-array并行处理规范,给出详尽的多路多核心硬件支撑下的差分数值处理方案。采用该方案计算了长直直流圆柱导体空间的二维电势分布并进行了讨论。

Co-array Fortran编译器的设计与实现

基于GUN Fortran编译器,设计并实现了co-array Fortran(CAF)编译器。通过源到源的转换将CAF代码转换为带有运行库调用的Fortran 90程序。典型用例的测试表明,CAF具有较好的可编程性,且CAF程序通过对数据分布的显式控制可获得比OpenMP程序更为高效的执行性能。

Optimal Design of Improved L-shaped Coprime Array Based on Difference and Sum Co-array

The concept of difference and sum co-array(DSCA)has become a new design idea for planar sparse arrays.Inspired by the shifting invariance property of DSCA,a specific configuration named here as the improved L-shaped array is proposed.Compared to other traditional 2D sparse array configurations such as 2D nested arrays and hourglass arrays,the proposed configuration has larger central consecutive ranges in its DSCA,thus increasing the DOF.At the same time,the mutual coupling effect is also reduced due to the enlarged spacing between the adjacent sensors.Simulations further demonstrate the superiority of the proposed arrays in terms of detection performance and estimation accuracy.

Triad-displaced ULAs configuration for non-circular sources with larger continuous virtual aperture and enhanced degrees of freedom

Non-uniform linear array(NULA)configurations are well renowned due to their structural ability for providing increased degrees of freedom(DOF)and wider array aperture than uniform linear arrays(ULAs).These characteristics play a significant role in improving the direction-of-arrival(DOA)estimation accuracy.However,most of the existing NULA geometries are primarily applicable to circular sources(CSs),while they limitedly improve the DOF and continuous virtual aperture for noncircular sources(NCSs).Toward this purpose,we present a triaddisplaced ULAs(Tdis-ULAs)configuration for NCS.The TdisULAs structure generally consists of three ULAs,which are appropriately placed.The proposed antenna array approach fully exploits the non-circular characteristics of the sources.Given the same number of elements,the Tdis-ULAs design achieves more DOF and larger hole-free co-array aperture than its sparse array competitors.Advantageously,the number of uniform DOF,optimal distribution of elements among the ULAs,and precise element positions are uniquely determined by the closed-form expressions.Moreover,the proposed array also produces a filled resulting co-array.Numerical simulations are conducted to show the performance advantages of the proposed Tdis-ULAs configuration over its counterpart designs.

Underdetermined direction of arrival estimation with nonuniform linear motion sampling based on a small unmanned aerial vehicle platform

Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suffers from significant performance degradation owing to the limited number of physical elements.To improve the underdetermined DOA estimation performance of a ULA radar mounted on a small UAV platform,we propose a nonuniform linear motion sampling underdetermined DOA estimation method.Using the motion of the UAV platform,the echo signal is sampled at different positions.Then,according to the concept of difference co-array,a virtual ULA with multiple array elements and a large aperture is synthesized to increase the degrees of freedom(DOFs).Through position analysis of the original and motion arrays,we propose a nonuniform linear motion sampling method based on ULA for determining the optimal DOFs.Under the condition of no increase in the aperture of the physical array,the proposed method obtains a high DOF with fewer sampling runs and greatly improves the underdetermined DOA estimation performance of ULA.The results of numerical simulations conducted herein verify the superior performance of the proposed method.

Generalized Array Architecture with Multiple Sub-Arrays and Hole-Repair Algorithm for DOA Estimation

Arranging multiple identical sub-arrays in a special way can enhance degrees of freedom(DOFs)and obtain a hole-free difference co-array(DCA).In this paper,by adjusting the interval of adjacent sub-arrays,a kind of generalized array architecture with larger aperture is proposed.Although some holes may exist in the DCA of the proposed array,they are distributed uniformly.Utilizing the partial continuity of the DCA,an extended covariance matrix can be constructed.Singular value decomposition(SVD)is used to obtain an extended signal sub-space,by which the direction-of-arrival(DOA)estimation algorithm for quasi-stationary signals is given.In order to eliminating angle ambiguity caused by the holes of DCA,the estimation of signal parameters via rotational invariance techniques(ESPRIT)is used to construct a matrix that includes all angle information.Utilizing this matrix,a secondary extended signal sub-space can be obtained.This signal sub-space is corresponding to a hole-free DCA.Then,dealing with the further extended signal sub-space by multiple signal classification(MUSIC)algorithm,the unambiguous DOAs of all incident signals can be estimated.Some simulation results are shown to prove the improved performance of proposed generalized array architecture in DOA estimation and the effectiveness of corresponding hole-repair algorithm in eliminating angle ambiguity.

Direction-of-arrival estimation of quasi-stationary signals using two-level Khatri-Rao subspace and four-level nested array

The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.