分析了现有跳频信号二维波达方向(DOA)估计算法的优缺点，提出了一种基于稀疏贝叶斯学习的跳频信号二维DOA估计算法.该算法利用L型阵列特点，将方位角、俯仰角和跳频率三维信息转换为一维空间频率信息，降低了冗余字典长度和稀疏求解难度.其次，经过奇异值分解降维处理，减少了矩阵运算维数，降低了算法复杂度，通过稀疏贝叶斯算法和快速傅里叶变换估计出空间频率和跳频率，利用Capon空间频率配对算法将空间频率和跳频率正确配对，计算出空间角.最后，由空间角几何关系解算出方位角和俯仰角.模拟结果表明，在低信噪比或低快拍数条件下，该算法DOA估计精度较高，且不易受空间频率间隔和跳频信号源相干性的影响.
The advantages and disadvantages of the existing two-dimensional direction of arrival (DOA) estimation algorithm for frequency hopping signals are analyzed. A two-dimensional DOA estimation algorithm for frequency hopping signals based on sparse Bayesian learning is proposed. The algorithm uses the characteristics of the L-shaped array to convert the three-dimensional information of azimuth, elevation and hopping frequency into one-dimensional spatial frequency information, which reduces the length of redundant dictionary and the difficulty of sparse solution. Then, after singular value decomposition, the matrix operation dimension is reduced, and the algorithm complexity is reduced. The spatial frequency and the hopping frequency are estimated by the sparse bayes algorithm and the fast fourier transform. The spatial frequency and the hopping frequency are correctly paired by the capon spatial frequency matching algorithm to calculate the spatial angle. Finally, the azimuth and elevation angles are calculated according to the spatial angular relationship. The simulation results show that the DOA estimation performance of the algorithm is good under low signal noise ratio or low fast beat, and it is not easy to be affected by the spatial frequency interval and the coherence of the hopping signal source.
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