Sparse Bayesian Learning for DOA estimation in MIMO radar with unknown nonuniform noise
Sparse Bayesian Learning for DOA estimation in MIMO radar with unknown nonuniform noise
复制标题
DOI：
10.1109/radar.2016.8059247
复制
发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Xianpeng Wang;Mengxing Huang;G. Bi
中科院分区：
文献类型：
--
作者：
Xianpeng Wang;Mengxing Huang;G. Bi
针对非均匀噪声背景下的多输入多输出（MIMO）雷达DOA估计问题，提出了一种稀疏贝叶斯学习框架。该方法利用降维变换消除MIMO雷达的冗余阵元。然后，通过假设先验源功率是独立的零均值高斯分布，其未知方差的超参数，建立协方差向量的稀疏贝叶斯模型。超参数和非均匀噪声方差估计分别利用期望最大化（EM）算法和最小二乘（LS）准则。最后，利用超参数谱估计出粗DOA，并在此基础上进行一维搜索，实现高精度DOA估计。仿真结果表明，该方法可以很好地工作在不同的非均匀噪声，并取得了更好的性能。
In this paper, a sparse Bayesian learning framework for DOA estimation in multiple input multiple output (MIMO) radar is proposed with unknown nonuniform noise. In the proposed method, the redundant elements of MIMO radar can be eliminated by using the reduced dimensional (RD) transformation. Then a sparse Bayesian model of covariance vector is formulated by assuming that the prior source power is independent zero-mean Gaussian distributed with hyperparameters for its unknown variance. The hyperparameters and nonuniform noise variances are estimated by utilizing the expectation-maximization (EM) algorithm and least squares (LS) criterion, respectively. Finally, the spectrum of hyperparameters is used to estimate the coarse DOA, and a high-precision DOA estimation is achieved by using a refined 1-D searching procedure based on the reconstruction result. Simulation results have demonstrated that the proposed method can work well with different nonuniform noise and achieve better performance.