有源欺骗干扰环境下的DOA估计

针对有源欺骗干扰环境下基于小样本的DOA估计问题，该文提出自适应极化滤波(APF)联合块稀疏贝叶斯学习(BSBL)算法的DOA估计方法。首先，通过APF抑制干扰能量，提高信干比。然后，建立有源欺骗干扰环境下的稀疏贝叶斯模型，基于相邻快拍相关性，利用BSBL算法进行DOA估计。仿真和实测数据处理结果表明，所提方法降低了干扰对BSBL算法的影响，且与APF联合子空间类算法或最大似然算法(ML)相比，具有更高的空间分辨率和DOA估计精度。

DOA Estimation Under Active Deception Jamming Environment

For the target DOA estimation under active deception jamming environment with limited samples, a novel DOA estimation method based on the combination of Adaptive Polarization Filter(APF) and Block Sparse Bayesian Learning(BSBL) algorithm is proposed. First, the interference energy is suppressed using APF. Then, the proposed method constructs a sparse Bayesian model under active deception jamming environment. The target DOA is estimated using the BSBL algorithm based on the neighbor time sampling correlation. Simulated and measured data processing results prove that the proposed method reduces the influence of interference on the BSBL algorithm, and has higher spatial resolution and higher angle measurement accuracy, comparing with the method based on the combination of APF and subspace-based DOA algorithms or maximum likelihood DOA algorithm.