论文标题
有效的ML到达估计方向假设传感器噪声功率未知
Efficient ML Direction of Arrival Estimation assuming Unknown Sensor Noise Powers
论文作者
论文摘要
本文提出了一种有效的方法来计算到达的最大可能性（ML）方向（DOA）估计值，假设传感器噪声功率未知。该方法将有效的替代投影（AP）程序与牛顿迭代结合在一起。该方法的效率在于，其所有中间步骤的复杂性都低。本文的主要贡献是方法的最后一步，其中通过牛顿程序，在一些迭代中，在DOA和噪声功能中，集中的成本函数都可以最大化。此步骤的复杂性很低，因为它采用了在论文中呈现的成本函数梯度和黑森人的闭合形式表达式。在典型情况下，该方法的总计算负担仅为几个巨型流量。我们介绍了确定性和随机ML估计量的方法。对确定性ML成本函数梯度的分析表明，其相关估计器的意外缺点：如果噪声功率未知，则它是退化或不一致的。该方法的根平方（RMS）错误性能和计算负担数字评估。
This paper presents an efficient method for computing maximum likelihood (ML) direction of arrival (DOA) estimates assuming unknown sensor noise powers. The method combines efficient Alternate Projection (AP) procedures with Newton iterations. The efficiency of the method lies in the fact that all its intermediate steps have low complexity. The main contribution of this paper is the method's last step, in which a concentrated cost function is maximized in both the DOAs and noise powers in a few iterations through a Newton procedure. This step has low complexity because it employs closed-form expressions of the cost function's gradients and Hessians, which are presented in the paper. The method's total computational burden is of just a few mega-flops in typical cases. We present the method for the deterministic and stochastic ML estimators. An analysis of the deterministic ML cost function's gradient reveals an unexpected drawback of its associated estimator: if the noise powers are unknown, then it is either degenerate or inconsistent. The root-mean-square (RMS) error performance and computational burden of the method are assessed numerically.
