Liang Yujie, Ying Rendong, Lu Zhenqi, Liu Peilin
School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China.
Sensors (Basel). 2014 Nov 20;14(11):21981-2000. doi: 10.3390/s141121981.
In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach.
在阵列信号处理中传感器阵列的设计阶段，估计性能和系统成本在很大程度上由阵列孔径大小决定。在本文中，我们解决了分布式稀疏线性阵列（SLA）的联合到达方向（DOA）估计问题，并提出了一种基于分布式压缩感知的离网格同步方法，以获得更大的阵列孔径。我们关注实际应用中的复杂源分布，并根据源信号是否能照射到所有SLA或特定的一个SLA，将源分为公共部分和创新部分。对于每个SLA，我们构建一个相应的虚拟均匀线性阵列（ULA），以分别创建这两个阵列观测到的信号之间的随机线性映射关系。将不同SLA的包含公共/创新源的信号集合抽象为一个联合空间稀疏模型。并且我们通过半定规划使用级联原子范数最小化来解决联合DOA估计问题。对所有SLA观测到的信号进行联合计算，利用了由公共源引起的冗余，并降低了对阵列大小的要求。数值结果说明了所提方法的优点。
