As an indispensable part of array signal processing, direction-of-arrival (DOA) estimation has been well investigated over the past few decades, and many excellent DOA estimation methods have been proposed. In this paper, a receiving domain covariance vector (RxCV) based unitary SBL algorithm is proposed for the off-grid DOA estimation of monostatic multiple-input multiple-output (MIMO) radar in unknown non-uniform noise environment. In the proposed algorithm, the data received by MIMO radar is firstly transformed into receiving domain by a reshape operation. Then the RxCV-based unitary off-grid sparse model without non-uniform noise is constructed through unitary transformation and first-order linear approximation, where the unknown non-uniform noise is got rid off by a linear transformation. Based on the RxCV-based unitary off-grid sparse model, the sparse Bayesian learning (SBL) criterion is adopted to estimate the parameters, where signal variance and off-gird error are estimated by using expectation-maximization (EM) strategy. The DOA estimation is ultimately realized through 1-dimensional spectrum search of the received data. Results of the simulation experiments have provided the evidence of that the proposed algorithm is robustness against nonuniform noise and off-grid error, and it can maintain superior DOA estimation performance compared with other reported sparse signal representation based methods.

作为阵列信号处理中不可或缺的一部分，波达方向（DOA）估计在过去的几十年中得到了很好的研究，并提出了许多优秀的 DOA 估计方法。本文提出了一种基于接收域协方差向量(RxCV)的酉SBL算法，用于未知非均匀噪声环境下单站多输入多输出(MIMO)雷达的离网DOA估计。在所提出的算法中，MIMO雷达接收到的数据首先通过reshape操作转换到接收域中。然后通过幺正变换和一阶线性逼近构建基于RxCV的无非均匀噪声酉离网稀疏模型，通过线性变换去除未知的非均匀噪声。基于RxCV的幺正离网稀疏模型，采用稀疏贝叶斯学习（SBL）准则估计参数，其中使用期望最大化（EM）策略估计信号方差和离网误差。DOA估计最终是通过对接收数据进行一维频谱搜索来实现的。仿真实验结果表明，该算法对非均匀噪声和离网误差具有鲁棒性，与其他报道的基于稀疏信号表示的方法相比，它可以保持优越的DOA估计性能。DOA估计最终是通过对接收数据进行一维频谱搜索来实现的。仿真实验结果表明，该算法对非均匀噪声和离网误差具有鲁棒性，与其他报道的基于稀疏信号表示的方法相比，它可以保持优越的DOA估计性能。DOA估计最终是通过对接收数据进行一维频谱搜索来实现的。仿真实验结果表明，该算法对非均匀噪声和离网误差具有鲁棒性，与其他报道的基于稀疏信号表示的方法相比，它可以保持优越的DOA估计性能。