基于广义秩模型的分数低阶协方差波束形成
发布时间：2018-10-19 06:32
【摘要】：在广义秩信号模型基础上,设计了一种鲁棒波束自适应波束形成方法。通过引入分数低阶变换来抑制非高斯稳定分布噪声,提出脉冲噪声环境下能够处理信号失配的鲁棒波束形成器算法,并讨论了分数低阶协方差矩阵的可逆性。由于采用Frobenius范数建立明确的模型来刻画期望阵列响应与数据的协方差矩阵之间的不确定性,因此所提算法具有解析闭合形式解。仿真结果表明:与传统波束形成算法相比,所提算法在脉冲稳定分布噪声和信号失配同时存在的情况时具有更好的鲁棒性。
[Abstract]:Based on the generalized rank signal model, a robust adaptive beamforming method is designed. By introducing fractional low order transform to suppress non-Gao Si stable distributed noise, a robust beamformer algorithm which can deal with signal mismatch in impulse noise environment is proposed, and the reversibility of fractional low order covariance matrix is discussed. Because the uncertainty between the expected array response and the covariance matrix of the data is described by using the Frobenius norm to establish a definite model, the proposed algorithm has an analytical closed form solution. The simulation results show that the proposed algorithm is more robust than the traditional beamforming algorithm when the impulsive stable distribution noise and signal mismatch exist at the same time.
【作者单位】： 大连理工大学电子信息与电气工程学部;大连交通大学理学院;
【基金】：国家自然科学基金(61139001,61172108,81241059)资助课题
【分类号】：TN911.7
本文编号：2280420
[Abstract]:Based on the generalized rank signal model, a robust adaptive beamforming method is designed. By introducing fractional low order transform to suppress non-Gao Si stable distributed noise, a robust beamformer algorithm which can deal with signal mismatch in impulse noise environment is proposed, and the reversibility of fractional low order covariance matrix is discussed. Because the uncertainty between the expected array response and the covariance matrix of the data is described by using the Frobenius norm to establish a definite model, the proposed algorithm has an analytical closed form solution. The simulation results show that the proposed algorithm is more robust than the traditional beamforming algorithm when the impulsive stable distribution noise and signal mismatch exist at the same time.
【作者单位】： 大连理工大学电子信息与电气工程学部;大连交通大学理学院;
【基金】：国家自然科学基金(61139001,61172108,81241059)资助课题
【分类号】：TN911.7
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