【题目】Direction-of-arrival Estimation Based on Compressive Sensing: From On-grid to Gridless 基于压缩感知的DOA估计方法：从on-grid到gridless
【报告人】南京邮电大学 吴晓欢博士
【时间】2019年3月18号下午15:00起
【地点】南海海洋资源利用国家重点实验室810报告厅
【英文摘要】Direction-of-arrival (DOA) estimation based on Compressive Sensing (CS) theory is a popular direction in array signal processing area. Compared to the subspace-based methods, the CS-based methods are able to overcome the imperfect scenarios such as small snapshots, low signal-to-noise ratio (SNR) and correlated sources, exhibiting satisfactory estimation performance. However, due to the inequivalent relation between the array model and CS model, these CS-based methods suffer from the limitation of the sparse reconstruction algorithms. More importantly, because of the high correlation and high complexity caused by the grid division, the sparsity-based methods cannot satisfy the requirement of different array processing system in terms of accuracy, computational complexity and adaptability as a whole. With the development of the CS theory, there are three development stages for the CS-base DOA estimation methods: on-grid, off-grid and gridless. The grid mismatch effect caused by the discretization is being overcome. In this talk, I will introduce these three kinds of methods and our recent works in these stagies. I will introduce three covariance-based DOA estimation methods, aiming to eliminate the limitation of the grid division, improve the estimation performance of the CS-based methods as well as reduce the computational co
【中文摘要】基于压缩感知理论的到达角估计研究是近几年阵列信号处理领域中的热门方向，相比于子空间类测向方法，压缩感知类测向方法能够克服诸如小快拍、低信噪比和相关信号等非理想场景的显著局限，展现出较好的测向性能。然而，由于阵列测向模型与稀疏重构模型之间的不等价关系，这些测向方法一方面受到了压缩感知算法自身局限性的限制，更重要的是，由于网格划分所带来的如高相关性、高计算量等问题，其在估计精度、计算复杂度和场景适应能力等方面仍旧难以满足各种阵列处理系统的要求。随着压缩感知理论的发展，基于压缩感知理论的测向方法出现了三个发展阶段：on-grid、off-grid和gridless，传统压缩感知测向方法所带来的网格划分正逐渐得到克服。本报告将介绍这三类方法，以及我们在这几个阶段所做的工作。我将介绍我们提出的三种基于协方差模型的测向方法，旨在解决稀疏表示类测向方法中的固有缺陷，突破网格划分的限制，提高稀疏表示类方法的测向性能。同时，我们还讨论了这三种方法之间的关联性。
【报告人简介】吴晓欢，于2017年在南京邮电大学通信与信息工程学院获得博士学位，2018年度“南京邮电大学优秀博士论文”获得者。博士毕业后留校任教。主要研究领域：阵列信号处理、到达角估计、毫米波通信。以第一作者身份在IEEE TVT、Sensors Journal、Signal Processing、ICASSP、DSP等国际知名期刊和会议上发表论文十余篇，其中ESI高被引论文1篇，目前Google学术引用100余次。主持国家青年基金、江苏省青年基金等相关项目5项，参与面上项目2项，同时担任IEEE TSP、TVT、SPL、Signal Processing等知名期刊审稿人。
