基于空域稀疏性的方位依赖阵列误差校正算法

Spatial Sparsity Based Method on Calibration of Direction-dependent Array Errors

针对方位依赖阵列误差的校正问题,通过引入少量精确校正的辅助阵元,该文给出一种基于空域稀疏性的方位依赖阵列误差校正算法。将受方位依赖阵列误差扰动的阵列流型表示为理想情况下的阵列流型与幅相误差系数矩阵的乘积形式。同时利用接收信号的空域稀疏性,对接收信号进行稀疏表示,将阵列误差自校正问题转化为一个二元最优化问题,再通过交替迭代的优化方式求得两个优化变量的最优解,从而实现了信号方位与方位依赖阵列误差的联合估计。该文所提算法相比于已有算法性能提升明显,参数估计性能优于传统算法且接近参数估计的Cramer-Rao下界,仿真实验也验证了算法的有效性和优越性。

For calibration of direction-dependent gain-phase errors, with a few precisely calibrated instrumental sensors, a method that jointly estimates the direction-dependent gain-phase errors and the target azimuth by spatial sparsity of the signal is proposed. The array manifold that perturbed by direction-dependent gain-phase errors is denoted by the multiplication form of ideally array manifold and a gain-phase errors coefficient matrix, then the received signal is represented by sparse form. The calibration for gain-phase error problem is formulated as a dual optimization problem, through alternating iterative optimization method to acquire the optimal solution of the two optimization variables, so as to realize the signal incident angle and azimuth dependent amplitude and phase errors of the optimized calculation. In this paper, the proposed algorithm has better performance than the existing algorithm, performance of the proposed algorithm is approximate to the Cramer-Rao low bound. The simulation exueriments verify the effectiveness and superiority of the proposed algorithm.