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DOA estimation using non-redundant cumulants sparse representation in correlated colored noise
Received date: 2016-04-19
Revised date: 2016-07-13
Online published: 2016-09-05
Supported by
National Natural Science Foundation of China (61461012, 61371186); Guangxi Key Laboratory of Wire-less Wideband Communication & Signal Processing (GXKL06160110); Center for Collaborative Innovation in the Technology of IOT and the Industrialization (WLW20060205); Innovation Project of GUET Graduate Education (2016YJCX87)
This paper aims at the problem that conventional fourth-order cumulants have high computational complexity and are sensitive to data samples. A new direction of arrival (DOA) estimation method is proposed to eliminate the redundancy quickly. The massive redundant data are removed by selection matrix to reduce the dimension of fourth-order cumulants matrix. By vectorizing the non-redundant cumulants matrix and reducing the dimension again, the vector measurement model with better statistical performance is then obtained. The sparse representation of the measurement model corresponding to the related over-complete basis is constructed for DOA estimation. Then the method is extended to L array for two dimensional DOA estimation. Compared with conventional fourth-order cumulants methods, the proposed method can greatly reduce the computational complexity and the impact of the size of data samples, and can efficiently suppress correlated colored noise. Theoretical analysis and simulation experiments verify that the proposed method has higher resolution and better estimation accuracy for one and two dimensional DOA estimation.
LIU Qinghua , ZHOU Xiuqing , JIN Liangnian . DOA estimation using non-redundant cumulants sparse representation in correlated colored noise[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017 , 38(4) : 320331 -320331 . DOI: 10.7527/S1000-6893.2016.0247
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