电子学报2025，Vol.53Issue(3)：1040-1062,23.DOI:10.12263/DZXB.20240504
面向结构化稀疏感知的张量阵列信号处理
Tensor Array Signal Processing for Structured Sparse Sensing
摘要
Abstract
With the continuous construction of new information infrastructures,multi-dimensional array signal pro-cessing plays a fundamental role in the filed of radar,wireless communication,remote sensing and so on.Multidimensional array signals contain rich spatial/temporal/frequentiol/polarization parametric information,offering great economic and so-cial values.To deal with the problem of structural information loss inherent in traditional vector/matrix models,the tensor algebra has been adopted to effectively retrieve multi-dimensional signal features.However,as the dimension of signals in-creases,the tensor signal volume following the Nyquist sampling theorem exponentially expands.Unfortunately,computa-tion resources of the system are approaching the physical limit,resulting in computational overload and high latency.Con-cerning these issues,the sparse sensing theory has been developed to exploit the spatial sparsity of signals for sub-Nyquist processing.The extension from one-dimensional sparse sensing to multi-dimensional sparse sensing becomes a promising solution to efficient tensor signal processing.Meanwhile,by imposing structured sparse sensing paradigm such as coprime and nested sensing,the performance of the system can be enhanced via augmented coarray signal processing.Thus,to pur-sue the high economy of multi-dimensional array signal processing,this paper endeavors to the research on Structured Sparse Tensor Signal Processing for Sensor Arrays.In particular,the paper introduces the statistical theory of sub-Nyquist tensor signals.By deriving the augmented coarray tensor model and devising the corresponding strategy of source identifi-ability enhancement,this theory facilitates Nyquist matching in the virtual domain and underdetermined parameter estima-tion.Based upon this theory,this paper introduces a coarray tensor completion algorithm for sparse array DOA estimation,exploiting the full information of the discontinuous virtual array to achieve high accuracy and resolution.Meanwhile,this paper introduces a coprime tensor weights optimization algorithm for sparse array beamforming,which yields a beampatten with a sharper mainlobe and lower sidelobes,and increases the output signal-to-interference-plus-noise ratio.Furthermore,this paper introduces a resource-efficient tensorized neural network for robust sparse tensor signal processing,which com-pensates the performance deterioration for the model-driven methods in non-ideal conditions by efficiently learning tensor signal features.关键词
多维阵列信号处理/张量信号处理/结构化稀疏感知/波达方向估计/波束成形Key words
multi-dimensional array signal processing/tensor signal processing/structured sparse sensing/direction-of-arrival estimation/beamforming分类
电子信息工程引用本文复制引用
郑航,史治国,王勇,周成伟..面向结构化稀疏感知的张量阵列信号处理[J].电子学报,2025,53(3):1040-1062,23.基金项目
国家自然科学基金(No.U21A20456,No.62271444) （No.U21A20456,No.62271444）
浙江省自然科学基金(No.LZ23F010007) National Natural Science Foundation of China(No.U21A20456,No.62271444) （No.LZ23F010007）
Natural Sci-ence Foundation of Zhejiang Province(No.LZ23F010007) （No.LZ23F010007）
