Xudong Dong , Jun Zhao , Meng Sun , Xiaofei Zhang , Yide Wang
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引用次数: 0
Abstract
Nested arrays are extensively employed in array signal processing to augment the degrees of freedom and enhance estimation precision, and the Cramér–Rao Bound (CRB) for nested arrays in a Gaussian noise environment has been established. Nevertheless, in a practical wireless communication environment, noise usually exhibits an impulsive characteristic. The impulsive noise applied in a uniform linear array (ULA) has been extensively studied in the literature, but only closed-form expressions of CRB with Cauchy and Gaussian noise distributions are given. Although nested arrays have garnered significant attention recently, research on the CRB under impulsive noise conditions remains scarce. In this paper, we provide the CRB expression for direction of arrival (DOA) estimation with nested arrays in an impulsive noise environment, which indicates that the CRB is formulated in terms of the fractional low-order statistics (FLOSs) of received data. Moreover, we also calculate the CRB results for different FLOSs as well as various sparse arrays and validate that the derived CRB makes an important contribution to the performance analysis of sparse arrays in impulsive noise.
嵌套阵列被广泛应用于阵列信号处理,以增加自由度和提高估计精度,并建立了高斯噪声环境下嵌套阵列的cram r - rao边界(CRB)。然而,在实际的无线通信环境中,噪声通常表现为脉冲特性。文献对均匀线性阵列中的脉冲噪声进行了广泛的研究,但只给出了具有柯西和高斯噪声分布的脉冲噪声的封闭表达式。虽然嵌套阵列近年来引起了广泛的关注,但对脉冲噪声条件下的CRB的研究仍然很少。本文给出了脉冲噪声环境下嵌套阵列到达方向(DOA)估计的CRB表达式,表明CRB是用接收数据的分数阶低阶统计量(FLOSs)来表示的。此外,我们还计算了不同FLOSs和各种稀疏阵列的CRB结果,并验证了推导的CRB对稀疏阵列在脉冲噪声中的性能分析有重要贡献。
期刊介绍:
Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing.
Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.