高效的多跳频信号2D-DOA估计算法

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,在时频图处理过程中采用形态学滤波进行去噪,并在修正的时频图上完成了跳频信号每一跳的提取。通过理论论证和实验仿真表明,本文算法相比于MUSIC算法,在保证均方根误差相当和估计成功率有所提高的情况下,计算复杂度降低了一半。

Efficient 2D-DOA estimation algorithm for multi-FH signals

In order to sort frequency-hopping (FH) networks efficiently using frequency domain feature parameters, a two dimensional direction of arrival (2D-DOA) estimation algorithm based on the multiple signal classification (MUSIC) symmetrical compressed spectrum is proposed by using the space-time frequency analyzing method. Firstly, the spatial time-frequency matrix of each hop is constructed by using the time-frequency domain features of FH signals, and the covariance matrix of time-frequency domain is obtained consequently. Then the idea of conjugate subspace is introduced into the traditional MUSIC algorithm, and the dimension of the noise subspace is descended by the singular value decomposition on the intersection of noise subspace and its conjugate one. Finally efficient DOA estimation is realized through half-spectrum searching. At the same time, the time-frequency map is amended via the morphological filtering method in order to enhance the performance of the low SNR algorithm. Efficient extraction of hop will be implemented on amended maps. Theoretical analysis and simulation results show that the proposed algorithm can reduce the computational complexity of the traditional MUSIC algorithm by 50% while the RMSE is equivalent to it and the estimated success rate is higher than it.