传统的极化域MUSIC算法当存在幅相误差扰动时会引起算法性能下降,并且在估计波达方向(DOA)和极化参数时需要四维谱峰搜索,计算量巨大。针对此问题,文中提出一种适用于任意极化敏感阵型的DOA-极化-误差的降维迭代自校正算法。首先将传统自校正算法推广至极化域,把DOA-极化和误差分离开,每次迭代分为估计误差参数和估计DOA-极化联合参数;然后在每次迭代过程中采用基于矩阵秩亏损的降维MUSIC算法来联合估计DOA和极化参数,将四维搜索优化成只与方位角和俯仰角有关的二维搜索,并利用搜索结果直接计算出极化参数;最后固定DOA-极化参数,问题转化为二次型极值问题,完成对幅相误差的估计,经过多次迭代算法可收敛。仿真实验验证了算法的有效性,仿真结果表明本文算法具有良好的误差校正效果。

The traditional polarization domain MUSIC algorithm will cause the performance degradation of the algorithm in the presence of gain-phase error, and it requires a four-dimensional spectral search when estimating DOA and polarization parameters, which is a huge amount of computation. Aiming at this problem, this paper proposes a DOA-polarization-error reduced-dimension iterative self-correction algorithm for arbitrary polarization-sensitive formations. First, the traditional self-correction algorithm is extended to the polarization domain, and DOA-polarization and error are separated. Each iteration is divided into estimated error parameters and estimated DOA-polarization joint parameters; Then, a reduced-dimension MUSIC algorithm based on matrix rank loss is used to jointly estimate the DOA and polarization parameters in each iteration, the four-dimensional search is optimized into a two-dimensional search only related to the azimuth and elevation angle, and directly calculate the polarization parameters using the search results; Finally, when the DOA-polarization parameters are fixed, the problem is transformed into a quadratic extremum problem, and the estimation of the gain-phase errors is completed. The algorithm can converge after multiple iterations. Simulation experiments verify the effectiveness of the algorithm. Simulation results show that the algorithm in this paper has a good error correction effect.

TN957

山东省重点研发计划(军民科技融合)资助项目