期刊
REMOTE SENSING
卷 13, 期 13, 页码 -出版社
MDPI
DOI: 10.3390/rs13132562
关键词
covariance matrix reconstruction; curvilinear trajectory; passive synthetic aperture; robust adaptive beamforming
类别
资金
- China Postdoctoral Science Foundation [2019M660049XB]
- Fundamental Research Funds for the Central Universities, CHD [300102240302]
- National Natural Science Foundation of China [61871059, 61901057]
This paper introduces an improved adaptive beamforming algorithm by reconstructing the interference-plus-noise covariance matrix to address model mismatch in unknown curvilinear trajectories, using the concept of signal subspace fitting to construct a joint optimization problem.
Recently, numerous reconstruction-based adaptive beamformers have been proposed, which can improve the quality of imaging or localization in the application of passive synthetic aperture (PSA) sensing. However, when the trajectory is curvilinear, existing beamformers may not be robust enough to suppress interferences efficiently. To overcome the model mismatch of unknown curvilinear trajectory, this paper presents an adaptive beamforming algorithm by reconstructing the interference-plus-noise covariance matrix (INCM). Using the idea of signal subspace fitting, we construct a joint optimization problem, where the unknown directions of arrival (DOAs) and array shape parameters are coupled together. To tackle this problem, we develop a hybrid optimization method by combining the genetic algorithm and difference-based quasi-Newton method. Then, a set of non-orthogonal bases for signal subspace is estimated with an acceptable computational complexity. Instead of reconstructing the covariance matrix by integrating the spatial spectrum over interference angular sector, we extract the desired signal covariance matrix (DSCM) directly from signal subspace, and then the INCM is reconstructed by eliminating DSCM from the sample covariance matrix (SCM). Numerical simulations demonstrate the robustness of the proposed beamformer in the case of signal direction error, local scattering and random curvilinear trajectory.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据