基于辅助阵元的方位依赖幅相误差最大似然自校正:针对确定信号模型

Maximum likelihood self-calibration for direction-dependent gain-phase errors with carry-on instrumental sensors:case of deterministic signal model

针对确定信号模型条件下方位依赖幅相误差的自校正问题,给出了一种基于辅助阵元的方位依赖幅相误差最大似然自校正方法;针对最大似然估计器中出现的高维非线性优化问题,推导了一种改进型交替投影迭代算法,从而实现了信号方位和方位依赖幅相误差的优化计算。此外,还推导了信号方位和方位依赖幅相误差的无偏克拉美罗界(CRB)。仿真实验结果验证了新方法的有效性和优越性。

Aim at the self-calibration of direction-dependent gain-phase errors in case of deterministic signal model,the maximum likelihood method（MLM） for calibrating the direction-dependent gain-phase errors with carry-on instrumental sensors was presented.In order to maximize the high-dimensional nonlinear cost function appearing in the MLM,an improved alternative projection iteration algorithm,which could optimize the azimuths and direction-dependent gain-phase errors was proposed.The closed-form expressions of the Cramér-Rao bound（CRB） for azimuths and gain-phase errors were derived.Simulation experiments show the effectiveness and advantage of the novel method.