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基于声压振速联合处理的稀疏协方差DOA估计

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为充分利用矢量水听器中声压振速信息之间的关系来提高DOA估计精度,本文提出了基于声压振速联合处理的稀疏协方差DOA(Direction of Arrival)估计方法.该方法首先利用声压振速之间的相关性,构造阵列协方差矩阵;其次,将空间入射角度集合进行等角度划分,构造超完备冗余字典;然后,在过完备基上寻找阵列协方差矩阵的最稀疏系数,利用系数向量中的非零行所对应的行号得到DOA估计值.将该算法与CBF算法及L1-SVD算法进行对比仿真实验,结果表明,在信号源数分别为3,4,5的情形下,本文所提算法在低信噪比和小快拍数情形时,具有更低的均方根误差,DOA估计性能优势明显.
Sparse Covariance DOA Estimation Based on Combined Information Processing of Sound Pressure and Particle Velocity
In order to make full use of the relationship between sound pressure and particle velocity in vec-tor hydrophone,and improve the accuracy of DOA estimation,sparse covariance DOA(Direction of Arrival)estimation method based on combined information processing of pressure and particle velocity was proposed.Firstly,the correlation between sound pressure and particle vibration velocity was used to construct the array of covariance matrix.Secondly,the spatial incident angle set was divided into equal angles and the super-complete redundant dictionary was built.Then looking for the most sparse coefficient of array covariance matrix on the over-complete basis and DOA estimates were obtained by using the row numbers corresponding to the non-zero rows in the coefficient vector.Finally,the algorithm was com-pared with CBF algorithm and L1-SVD algorithm,simulation results show that the proposed algorithm has lower root-mean-square error(RMSE)error when the number of signal sources is 3,4,5,respec-tively,the method has good DOA estimation performance compared to other algorithms under the condi-tions of small snapshot number and low signal-to-noise ratio(SNR).

DOA estimationsparse representationarrray covariance matrixvector sensor line array

禹秀梅、郑文康、王立府、王鹏

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中北大学 数学学院,山西 太原 030051

DOA估计 稀疏表示 阵列协方差矩阵 矢量线性阵

国家自然科学基金项目山西省基础研究计划资助项目山西省基础研究计划资助项目山西省基础研究计划资助项目山西省基础研究计划资助项目山西省回国留学人员科研项目

61774137202103021224195202103021224212202103021223189202103021230192020-104

2024

10.3969/j.issn.1673-3193.2024.03.007

中北大学学报(自然科学版)
中北大学

中北大学学报(自然科学版)

影响因子：0.258
ISSN：1673-3193
年,卷(期)：2024.45(3)