Synthesis of Sparse Linear Arrays via Low-Rank Hankel Matrix Completion

基于一位汉克尔矩阵补全的稀疏线阵车载雷达成像

Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion

本文针对稀疏线阵车载雷达的粗量化测量问题，提出基于一位汉克尔矩阵补全的奇异值阈值算法，可仅用一位数据准确估计方位角，为低成本高分辨率车载雷达提供了有效方案。

Vandermonde Factorization of Hankel Matrix for Complex Exponential Signal Recovery -- Application in Fast NMR Spectroscopy

Many signals are modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. This paper studies the problem of recovering exponential signals from a random subset of samples. We exploit the Vandermonde structure of the Hankel matrix formed by the exponential signal and formulate signal recovery as Hankel matrix completion with Vandermonde factorization (HVaF). A numerical algorithm is developed to solve the proposed model and its sequence convergence is analyzed theoretically. Experiments on synthetic data demonstrate that HVaF succeeds over a…

Two-snapshot DOA Estimation via Hankel-structured Matrix Completion

In this paper, we study the problem of estimating the direction of arrival (DOA) using a sparsely sampled uniform linear array (ULA). Based on an initial incomplete ULA measurement, our strategy is to choose a sparse subset of array elements for measuring the next snapshot. Then, we use a Hankel-structured matrix completion to interpolate for the missing ULA measurements. Finally, the source DOAs are estimated using a subspace method such as Prony on the fully recovered ULA. We theoretically provide a sufficient bound for the number of required samples (array elements) for perfect recovery. Th…

Low-Rank Hankel Tensor Completion for Traffic Speed Estimation

This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors. Most existing TSE methods either rely on well-defined physical traffic flow models or require large amounts of simulation data as input to train machine learning models. Different from previous studies, we propose a purely data-driven and model-free solution in this paper. We consider the TSE as a spatiotemporal matrix completion/interpolation problem, and apply spatiotemporal delay embedding to transform the original incomplete matrix into a fourth-order Hankel structured tensor. By im…

基于混合精度分布式阵列补全的协同汽车雷达感知

Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion

针对协同汽车雷达感知的稀疏线性阵列测量，提出利用混合精度量化Hankel矩阵低秩特性的方位角恢复方法，通过拟等距嵌入分析性能，数值验证其高效重构效果。

Ocean Reverberation Suppression via Matrix Completion with Sensor Failure

In the ocean environment, reverberation is a common and strong interference which significantly degrades the performance of target bearing estimation. Meanwhile, sensor failure is inevitable in actual sonar deployment as the underwater scene is complicated. Therefore, it is a challenge for target detection in the ocean reverberation with sensor failure. To address this issue, we propose an improved approach based on the principle of low rank characteristics of matrix in this paper. Firstly, we utilize Hankel structured matrix to counteract the problem of sensor failure. Then, the algorithm of …