Traditional subspace methods which are based on the spatial time-frequency distribution (STFD) matrix have been investigated for direction-of-arrival (DOA) estimation of linear frequency modulation (LFM) signals. However, the DOA estimation performance may degrade substantially when multiple LFM signals are spectrally overlapped in time-frequency (TF) domain. In order to solve this problem, this paper proposes single-source TF points selection algorithm based on Hough transform and short-time Fourier transform (STFT). Firstly, the signal intersections in TF domain can be solved based on the Hough transform, and multiple-source TF points around the intersections are removed, so that the single-source TF points set is reserved. Then, based on the Euclidean distance operator, the single-source TF points set belonging to each signal can be obtained according to the property that TF points of the same signal have same eigenvector. Finally, the averaged STFD matrix is constructed for each signal, and DOA estimation is achieved based on multiple signal classification (MUSIC) algorithm. In this way, the proposed algorithm exhibit remarkable superiority in estimation accuracy and angular resolution over the state-of-the-art schemes and can achieve DOA estimation in the underdetermined cases. In addition, the proposed algorithm can still perform DOA estimation when multiple LFM signals intersect at one point. Numerical simulations demonstrate the validity of the proposed method.

已经研究了基于空间时频分布（STFD）矩阵的传统子空间方法，用于线性调频（LFM）信号的到达方向（DOA）估计。但是，当多个LFM信号在时频（TF）域中频谱重叠时，DOA估计性能可能会大大降低。为了解决这个问题，本文提出了一种基于霍夫变换和短时傅立叶变换（STFT）的单源TF点选择算法。首先，可以基于霍夫变换求解TF域中的信号交点，并去除交点周围的多源TF点，从而保留了单源TF点集。然后，基于欧几里得距离运算符，根据同一信号的TF点具有相同特征向量的性质，可以得到属于每个信号的单源TF点集。最后，为每个信号构造平均的STFD矩阵，并基于多信号分类（MUSIC）算法实现DOA估计。这样，与现有技术方案相比，所提出的算法在估计精度和角度分辨率方面显示出显着的优势，并且可以在不确定的情况下实现DOA估计。另外，当多个LFM信号在一个点相交时，所提出的算法仍可以执行DOA估计。数值模拟证明了该方法的有效性。DOA估计是基于多信号分类（MUSIC）算法实现的。这样，与现有技术方案相比，所提出的算法在估计精度和角度分辨率方面显示出显着的优势，并且可以在不确定的情况下实现DOA估计。另外，当多个LFM信号在一个点相交时，所提出的算法仍可以执行DOA估计。数值模拟证明了该方法的有效性。DOA估计是基于多信号分类（MUSIC）算法实现的。这样，与现有技术方案相比，所提出的算法在估计精度和角度分辨率方面显示出显着的优势，并且可以在不确定的情况下实现DOA估计。另外，当多个LFM信号在一个点相交时，所提出的算法仍可以执行DOA估计。数值模拟证明了该方法的有效性。当多个LFM信号在一个点相交时，该算法仍然可以执行DOA估计。数值模拟证明了该方法的有效性。当多个LFM信号在一个点相交时，该算法仍然可以执行DOA估计。数值模拟证明了该方法的有效性。