论文标题
在分布式传感器阵列的盲定位中利用光线
Exploiting Rays in Blind Localization of Distributed Sensor Arrays
论文作者
论文摘要
如果已知传感器的位置，许多分布式传感器的信号处理算法能够提高其性能。在本文中，我们着重于推断分布式阵列和源的相对几何形状，即设置几何形状到缩放系数。首先，我们介绍在到达方向遵循von mises-fisher分布的假设下得出的最大似然估计器。其次，使用统一符号，我们显示了许多最新的相对几何估计器的成本函数之间的关系。第三，我们得出了一个新颖的估计器，该估计器利用阵列和源事件位置之间的光线概念。最后，我们显示了各种条件下提出的估计量的评估结果，这表明可以通过使用拟议的基于射线的估计器来实现与现有方法相对于现有方法的最佳解决方案的概率的重大改善。
Many signal processing algorithms for distributed sensors are capable of improving their performance if the positions of sensors are known. In this paper, we focus on estimators for inferring the relative geometry of distributed arrays and sources, i.e. the setup geometry up to a scaling factor. Firstly, we present the Maximum Likelihood estimator derived under the assumption that the Direction of Arrival measurements follow the von Mises-Fisher distribution. Secondly, using unified notation, we show the relations between the cost functions of a number of state-of-the-art relative geometry estimators. Thirdly, we derive a novel estimator that exploits the concept of rays between the arrays and source event positions. Finally, we show the evaluation results for the presented estimators in various conditions, which indicate that major improvements in the probability of convergence to the optimum solution over the existing approaches can be achieved by using the proposed ray-based estimator.
