基于酉变换和稀疏贝叶斯学习的离格DOA估计

Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning

针对传统稀疏贝叶斯学习算法(SBL)在解决低信噪比条件下信号到达角(DOA)估计有效性的问题,提出基于酉变换的实数域稀疏贝叶斯学习(RV-OGSBL)的快速离格DOA估计方法。该方法首先对均匀线阵的实际接收信号通过构造增广矩阵作为DOA估计的处理信号,然后利用酉变换将估计模型从复数域转化到实数域,进一步在实数域下将离格模型与稀疏贝叶斯学习算法相结合迭代处理实现DOA估计,获得较高的估计精度。仿真结果表明,RV-OGSBL方法不仅能保持传统SBL算法的性能,而且显著降低了计算复杂度。在低信噪比和低快拍数的情况下,算法运行时间降低约50%,表明该方法是一种快速的DOA估计算法。

A rapid off-grid DOA estimating method of RV-OGSBL was raised based on unitary transformation, against the problem of traditional sparse Bayesian learning （SBL） algorithm in solving effectiveness of signal’s DOA estimation under condition of lower signal noise ratio （SNR）. Actual received signal of uniform linear array was generated through constructing augment matrix as the processing signal used by DOA estimation. Then, estimation model was transformed from complex value to real value by using unitary transformation. In the next step, off-grid model and sparse Bayesian learning algorithm were combined together to process the realization of DOA estimation iteratively. The accuracy of es-timation could made relatively high. The simulation result demonstrates that the RV-OGSBL method not only maintains the performance of traditional SBL algorithm, but also reduces the computational complexity significantly. Under the sit-uation of lower signal noise ratio （SNR） and low number of snapshots, the running time of algorithm is reduced about 50%. This shows the RV-OGSBL method is a rapid DOA estimation algorithm.