基于非线性共轭梯度法的唯相直接数据域算法

提出一种基于非线性共轭梯度法的唯相直接数据域最小二乘算法。根据标准直接数据域算法得到代价函数,由小相位扰动效应和泰勒展式推导得到代价函数的梯度,使用非线性共轭梯度法对代价函数进行优化,最终确定最优唯相权值向量。作为一种唯相自适应算法,它在硬件实现上比传统算法更具简单性。同时,它只对单快拍数据进行处理,避免了样本协方差矩阵的构造以及矩阵求逆运算,更适合于实时处理。仿真结果表明,算法具有良好的信号恢复和干扰置零性能。

Phase-only Direct Data Domain Algorithm Based on Nonlinear Conjugate Gradient Method

A phase-only direct data domain least square (D3LS) algorithm based on the nonlinear conjugate gradient method was proposed. After the cost function was obtained via the normal D3LS algorithm, its gradient was derived according to small phase perturbations effect and Taylor Expansion. The cost function was optimized with the nonlinear conjugate gradient method to determine the optimal phase-only weight vector as a final result. As a phase-only adaptive algorithm, the proposed algorithm has a better simplicity on hardware implementation than conventional algorithms. Moreover, it processes only a single snapshot data with no use for the formation of the sample covariance matrix and matrix inversion operation. Therefore, it may be so effective in real-time processing. Simulation results show the proposed algorithm has a good signal recovery and interferences nulling performance.