Spread spectrum communication is a common communication method in underwater communication. Based on the space-time processor received by the array, it can filter the signals arriving along each path separately. Combined with the diversity of space-time clusters, it can effectively improve the communication system’s reliability. The core problem of the space-time processor is the direction of arrival (DOA) and signal source number estimation. Based on the good self-coherence of the spread spectrum sequence, this paper proposes a multiple signal classification algorithm (MUSIC) for accurate DOA estimation. However, since the MUSIC algorithm uses the received signal’s covariance matrix for DOA estimation, the number of sources needs to be predicted in advance. Under a low signal-to-noise ratio (SNR), the signal eigenvalues and the noise eigenvalues of the covariance matrix differ slightly, which makes signal source number estimation difficult. To address this issue, a singular value decomposition method using the delay structure information of the array element is proposed to estimate the number of sources of the spreading sequence under a low SNR. The method proposed in this paper can well estimate the DOA of the signal under a low SNR. Meanwhile, there is no need to convert the signal to the individual sub-bands, which effectively reduces the calculation overhead. At the same time, the Hankel matrix is used to solve the problem that the MUSIC algorithm cannot accurately estimate the number of signal sources under the condition of low SNR. Compared with the conventional algorithm, the Hankel matrix can more accurately estimate the number of signal sources in the case of low SNR. Through simulation experiments, the effectiveness of our DOA estimation algorithm is validated under a low SNR.

扩频通信是水下通信中常用的一种通信方式。基于阵列接收到的时空处理器，它可以分别过滤沿每条路径到达的信号。结合时空簇的多样性，可以有效提高通信系统的可靠性。时空处理器的核心问题是波达方向（DOA）和信号源数估计。基于扩频序列良好的自相干性，本文提出了一种多信号分类算法(MUSIC)，用于准确的DOA估计。但是，由于 MUSIC 算法使用接收信号的协方差矩阵进行 DOA 估计，因此需要提前预测源的数量。在低信噪比 (SNR) 下，协方差矩阵的信号特征值和噪声特征值略有不同，这给信号源个数估计带来了困难。针对这一问题，提出了一种利用阵元延迟结构信息的奇异值分解方法来估计低信噪比下扩频序列的源数。本文提出的方法可以很好地估计低信噪比下信号的DOA。同时，无需将信号转换为单独的子带，有效降低了计算开销。同时，利用汉克尔矩阵解决了MUSIC算法在低信噪比条件下无法准确估计信号源数量的问题。与传统算法相比，Hankel 矩阵在低信噪比的情况下可以更准确地估计信号源的数量。通过仿真实验，在低信噪比下验证了我们的 DOA 估计算法的有效性。