The performance of the weighted sparse Bayesian inference (OGWSBI) algorithm for off-grid coherent DOA estimation is not satisfactory due to the inaccurate weighting information. To increase the estimation accuracy and efficiency, an improved OGWSBI algorithm based on a higher-order off-grid model and unitary transformation for off-grid coherent DOA estimation is proposed in this paper. Firstly, to reduce the approximate error of the first-order off-grid model, the steering vector is reformulated by the second-order Taylor expansion. Then, the received data is transformed from complex value to real value and the coherent signals are decorrelated via utilizing unitary transformation, which can increase the computational efficiency and restore the rank of the covariance matrix. Finally, in the real field, the steering vector higher-order approximation model and weighted sparse Bayesian inference are combined together to realize the estimation of DOA. Extensive simulation results indicate that under the condition of coherent signals and low SNR, the estimation accuracy of the proposed algorithm is about 50% higher than that of the OGWSBI algorithm, and the calculation time is reduced by about 60%.

中文翻译：

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相干信号精确离网 DOA 估计的有效改进 OGWSBI 算法

由于权重信息不准确，用于离网相干DOA估计的加权稀疏贝叶斯推理（OGWSBI）算法的性能并不令人满意。为了提高估计精度和效率，本文提出了一种基于高阶离网模型和酉变换的离网相干DOA估计改进OGWSBI算法。首先，为了减少一阶离网模型的近似误差，通过二阶泰勒展开式重新制定导向向量。然后，将接收到的数据从复值变换为实值，并利用酉变换对相干信号进行去相关，可以提高计算效率，恢复协方差矩阵的秩。最后，在现实领域，将导向向量高阶逼近模型与加权稀疏贝叶斯推理相结合，实现对DOA的估计。大量仿真结果表明，在相干信号和低信噪比条件下，所提算法的估计精度比OGWSBI算法提高约50%，计算时间减少约60%。