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为了利用跳频信号的空域特征参数辅助多跳频信号的网台分选，在空时频分析的基础上，提出一种基于多重信号分类（multiple signal classification, MUSIC）对称压缩谱（MUSIC symmetrical compressed spectrum, MSCS）的多跳频信号二维波达方向（two dimensional direction of arrival, 2D-DOA）高效估计算法。首先根据跳频信号的时频域特征，构建每一跳的空时频矩阵（Spatial time-frequency distribution, STFD），获取时频域的协方差矩阵；然后将共轭子空间的思想引入到 MUSIC 算法中，通过对噪声子空间及其共轭的交集进行奇异值分解，实现噪声子空间的降维；最终通过半谱搜索实现 2D-DOA 的高效估计。同时为了提高低信噪比条件下算法的性能，采用形态学滤波的方法对获得的时频图进行修正，在修正的时频图上完成了跳频信号每一跳的提取。理论分析与仿真实验表明，该算法在均方根误差与 MUSIC 算法相当且估计成功率高于 MUSIC 算法的基础上，将 MUSIC 算法的复杂度降低一半。
In order to utilize the space-domain characteristic parameters of frequency-hopping signals to assist network-station sorting of multi-frequency-hopping signals, a MUSIC symmetrical compression spectrum (MUSIC) spectrum, MSCS) two-dimensional direction of arrival (2D-DOA) efficient estimation algorithm. Firstly, based on the time-frequency features of frequency-hopping signals, a Spatial time-frequency distribution (STFD) of each hop is constructed to obtain the covariance matrix in the time-frequency domain. Then the idea of conjugate subspace is introduced into MUSIC In the algorithm, the dimension of noise subspace is reduced by singular value decomposition of the intersection of noise subspace and its conjugate. Finally, the efficient estimation of 2D-DOA is achieved by using the half-spectrum search. At the same time, in order to improve the performance of the algorithm under low signal-to-noise ratio, the morphological filtering method is used to correct the time-frequency map, and the hop-frequency signal is extracted for each hop on the modified time-frequency map. The theoretical analysis and simulation results show that the proposed algorithm reduces the complexity of the MUSIC algorithm by half when the root mean square error is equivalent to that of the MUSIC algorithm and the estimated success rate is higher than that of the MUSIC algorithm.