Localization of incoherently distributed near-field sources: A low-rank matrix recovery approach

This paper considers the localization problem of incoherently distributed near-field (IDNF) sources. It is observed that the angular and range spreads of IDNF source signals produce a useful low-rank structure, which can be used to estimate the joint angular-range distribution (JARD) for IDNF sources. Then, by analyzing the low-rank property of the JARD matrix, a rank minimization problem is formulated to directly estimate the JARD matrix, which can be solved efficiently by the truncated nuclear norm regularization with accelerated proximal gradient line search method (TNNR-APGL). Finally, for performance comparisons, off-grid estimators are applied to estimate the key parameters of the JARD. Compared with conventional algorithms, the proposed method enjoys better parameter estimation performance and faster computation, requiring no parameterized distribution model and multi-dimensional search. Numerical experiments are included to demonstrate the performance of the proposed solution. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper addresses the localization problem of incoherently distributed near-field sources, utilizing the low-rank structure to estimate the joint angular-range distribution. By formulating a rank minimization problem and applying a specialized method, the proposed solution achieves better parameter estimation performance and faster computation compared to conventional algorithms. Numerical experiments are conducted to demonstrate the effectiveness of the method.