稀疏贝叶斯框架下DOA与极化参数联合估计算法  

Joint Estimation for DOA and Polarization Parameters inSparse Bayesian Framework

作　　者：徐海峰[1] Xu Haifeng(Nanjing Research Institute of Electronics Technology,Nanjing 210039,China)

机构地区：[1]南京电子技术研究所,南京210039

出　　处：《航空兵器》2021年第2期113-118,共6页Aero Weaponry

摘　　要：针对传统极化敏感阵列测向算法在相干信号入射条件下估计精度低、运算复杂度大的问题,提出一种稀疏贝叶斯学习框架下的波达方向与极化参数联合估计算法。该算法首先将数据接收矩阵稀疏得到观测矩阵,再利用酉变换将观测数据矩阵从复数域转化为实数域,并且对模型参数施加一个三层的稀疏先验。然后,根据变分贝叶斯理论,用得到的模型参数均值和方差构造稀疏信号的功率谱函数,通过谱峰搜索得到信号的DOA。最后,利用已估计的信号DOA和模值约束算法,获取信号极化信息。仿真试验表明,本文所提算法在入射信号相干时能够正确测向,并且具有较高的测向精度和较低的运算复杂度。Aiming at the problems of low precision and high computational complexity in estimating coherent signals by traditional polarization sensitive array,a joint parameter estimation algorithm based on sparse Bayesian learning framework for direction of arrival and polarization information is proposed.Firstly,the observation matrix is obtained by sparse data receiving matrix,then the observation data matrix is transformed from complex domain to real domain by unitary transformation,and a three-layer sparse prior is applied to the model parameters.Then,according to the variational Bayesian theory,the power spectrum function of sparse signal is constructed by the mean and variance of the model parameters,and the DOA of the signal is obtained by peak search.Finally,the estimated signal DOA and modulus constraint are used to obtain the polarization information.The simulation results show that the proposed algorithm can correctly locate coherent incident signals,and has higher direction finding accuracy and lower computational complexity.

关 键 词：极化敏感阵列 联合参数估计 稀疏贝叶斯学习 模值约束 酉变换 

分 类 号：TJ765[兵器科学与技术—武器系统与运用工程] TN911.7[电子电信—通信与信息系统]