广义复相关熵与相干分布式非圆信号DOA估计
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摘要
针对相干分布式非圆信号参数估计算法在脉冲噪声环境下性能退化的问题,本文提出了广义复相关熵的概念,并给出了基于广义复相关熵的相干分布式非圆信号DOA(Direction of Arrival)估计方法。该算法首先由分布式信源模型获得入射信号的阵列输出信号,利用信号的非圆特性得到扩展阵列输出信号,再通过扩展阵列输出信号的广义复相关熵矩阵获取信号子空间,避开了传统二阶统计量算法在脉冲噪声下不适应的问题,最后由信号子空间旋转不变特性得到信号的中心波达方向角度。仿真实验结果表明,在Alpha稳定分布噪声条件下,与传统算法相比,本文所提算法具有更好的性能。
To solve the problem of performance degradation of parameter estimation algorithms for coherently distributed non-circular sources under the impulsive noise environment, this paper proposes the concept of generalized complex correntropy and a new DOA estimation method based on generalized complex correntropy for coherently distributed non-circular sources. Firstly, the algorithm obtains an array output matrix of incident signal through the distributed source model, and constructs the extended array output by using the non-circular characteristic of the signal. Then the generalized complex correntropy matrix of the extended array output is used to get the signal subspace, which avoids the degradation problem of the traditional second-order statistic algorithm in the impulsive noise environment. And finally the central direction of arrival of the signal is obtained by the rotation of the signal subspace. The simulation results show that the proposed algorithm has better performance than the traditional algorithm under the condition of Alpha stable distributed noise.
To solve the problem of performance degradation of parameter estimation algorithms for coherently distributed non-circular sources under the impulsive noise environment, this paper proposes the concept of generalized complex correntropy and a new DOA estimation method based on generalized complex correntropy for coherently distributed non-circular sources. Firstly, the algorithm obtains an array output matrix of incident signal through the distributed source model, and constructs the extended array output by using the non-circular characteristic of the signal. Then the generalized complex correntropy matrix of the extended array output is used to get the signal subspace, which avoids the degradation problem of the traditional second-order statistic algorithm in the impulsive noise environment. And finally the central direction of arrival of the signal is obtained by the rotation of the signal subspace. The simulation results show that the proposed algorithm has better performance than the traditional algorithm under the condition of Alpha stable distributed noise.
引文
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[16] 王鹏,邱天爽,金芳晓,等.脉冲噪声下基于稀疏表示的韧性DOA估计方法[J].电子学报,2018,46(7):1537-1544.Wang Peng,Qiu Tianshuang,Jin Fangxiao,et al.A robust DOA estimation method based on sparse representation for impulsive noise environments[J].Acta Electronica Sinica,2018,46(7):1537-1544.(in Chinese)
[2] 麻妍梅,邓科,殷勤业.混合信源波达方向估计算法[J].信号处理,2017,33(11):1468-1474.Ma Yanmei,Deng Ke,Yin Qinye.Direction-of-arrival estimation for mixed sources[J].Journal of Signal Processing,2017,33(11):1468-1474.(in Chinese)
[3] Valaee S,Champagne B,Kabal P.Parametric localization of distributed sources[J].IEEE Transactions on Signal Processing,1995,43(9):2144-2153.
[4] Shahbazpanahi S,Valaee S,Bastani M H.Distributed source localization using ESPRIT algorithm[J].IEEE Transactions on Signal Processing,2001,49(10):2169-2178.
[5] Roy R,Kailath T.ESPRIT-estimation of signal parameters via rotational invariance techniques[J].IEEE Transactions on Acoustics,Speech,and Signal Processing,1989,37(7):984-995.
[6] Zhou Y,Fei Z,Yang S,et al.Joint angle estimation and signal reconstruction for coherently distributed sources in massive MIMO systems based on 2-D unitary ESPRIT[J].IEEE Access,2017,PP(99):1-1.
[7] 代正亮,巴斌,张彦奎,等.回溯降维相干分布式非圆信号DOA快速估计[J].航空学报,2017,38(9):269-278.Dai Zhengliang,Ba Bin,Zhang Yankui,et al.Fast DOA estimation for coherently distributed noncircular sources by backtracking reduced dimension[J].Acta Aeronautica et Astronautica Sinica,2017,38(9):269-278.(in Chinese)
[8] Yang X,Li G,Zheng Z,et al.2D DOA estimation of coherently distributed noncircular sources[J].Wireless Personal Communications,2014,78(2):1095-1102.
[9] Wan L,Han G,Jiang J,et al.DOA estimation for coherently distributed sources considering circular and noncircular signals in massive MIMO systems[J].IEEE Systems Journal,2017,11(1):41- 49.
[10] Shao M,Nikias C L.Signal processing with fractional lower order moments:stable processes and their applications[J].Proceedings of the IEEE,1993,81(7):986-1010.
[11] Liu T,Qiu T,Luan S.Cyclic correntropy:foundations and theories[J].IEEE Access,2018,6:34659-34669.
[12] Zhang J,Qiu T.The fractional lower order moments based ESPRIT algorithm for noncircular signals in impulsive noise environments[J].Wireless Personal Communications,2017,96(7):1673-1690.
[13] Steinwandt J,Roemer F,Haardt M,et al.R-dimensional ESPRIT-type algorithms for strictly second-order non-circular sources and their performance analysis[J].IEEE Transactions on Signal Processing,2014,62(18):4824- 4838.
[14] Santamaria I,Pokharel P P,Principe J C.Generalized correlation function:definition,properties,and application to blind equalization[J].IEEE Transactions on Signal Processing,2006,54(6):2187-2197.
[15] Guimaraes J P F,Fontes A I R,Rego J B A,et al.Complex correntropy:probabilistic interpretation and application to complex-valued data[J].IEEE Signal Processing Letters,2016,24(1):42- 45.
[16] 王鹏,邱天爽,金芳晓,等.脉冲噪声下基于稀疏表示的韧性DOA估计方法[J].电子学报,2018,46(7):1537-1544.Wang Peng,Qiu Tianshuang,Jin Fangxiao,et al.A robust DOA estimation method based on sparse representation for impulsive noise environments[J].Acta Electronica Sinica,2018,46(7):1537-1544.(in Chinese)