This paper introduces a novel direction-of-arrival (DOA) estimation method for the closely related wideband sources. The new method estimates the DOAs accurately by evaluating the probability relation between the signal and the noise subspaces of multiple frequency components of the sources using supervised singular value decomposition (SupSVD) and likelihood mean shift (LMS) methods. Also, the introduced method uses the selective criteria of the reference frequency in minimum noisy subband-TOPS (MNS-TOPS) which is an improved version of test of orthogonality of projected subspaces (TOPS) method. This reference frequency is used as the primary set of interest and other sub-bands are used as supervised set to accurately extract the DOAs from a very noisy reception of the signals. The performance of the introduced method compared with well-known methods such as incoherent signal subspace method (ISSM), TOPS, weighted squared-TOPS (WS-TOPS), and MNS-TOPS. The simulations show that the new method outclasses all other methods in all ranges of SNR values. The newly introduced method tested with the lowest ever used number of snapshots (12) and very low SNR values (\(<-\)10 dB). The new method estimates the exact DOAs at the lowest computational cost while the conventional methods can not produce accurate results.

本文介绍了一种针对紧密相关的宽带源的新的到达方向（DOA）估计方法。该新方法通过使用监督奇异值分解（SupSVD）和似然平均漂移（LMS）方法评估信号与源的多个频率分量的噪声子空间之间的概率关系来准确估计DOA。此外，引入的方法在最小噪声子带TOPS（MNS-TOPS）中使用参考频率的选择标准，这是投影子空间正交性测试（TOPS）方法的改进版本。该参考频率被用作主要的关注集，其他子带被用作监督集，以从非常嘈杂的信号接收中准确提取DOA。与不相干信号子空间方法（ISSM），TOPS，加权平方TOPS（WS-TOPS）和MNS-TOPS等众所周知的方法相比，该方法的性能更高。仿真表明，在所有SNR值范围内，新方法都优于其他所有方法。新引入的方法经过测试，使用的快照数量最少（12），信噪比非常低（\（<-\） 10 dB）。新方法以最低的计算成本估算出准确的DOA，而常规方法则无法产生准确的结果。