EIA: Maximum Likelihood Based Angle-of-Arrival Estiamtion ina Diffuse Multipath Environenmt

该提案是为了获得工程启动奖，该奖项概述了对使用最大似然（ML）估计程序来确定色散介质中撞击传感器阵列的信号方向的研究。我们感兴趣的特殊应用处理了在存在漫射多径分量的情况下对低角度雷达目标的跟踪。ML估计器的使用是针对其处理少量数组日期快照的能力，并且因为最近的算法开发显着减少了相关的计算负担。这项工作的目标是定义和开发一种适合于连续源和点源的基于ml的估计程序。目前基于机器学习的方法假设发射源本质上是点状的，并且只严格适用于镜面多径环境。如果成功，所得到的ML算法将被推广到更复杂的漫射信号源场景中，如断层扫描、射电天文学和声纳中遇到的场景。

This proposal is for an Engineering Initiation Award that outlines an investigation into the use of the maximum likelihood (ML) estimation procedure for determining the directions of signals impinging on an array of sensors in a dispersive medium. The particular application of interest deals with the tracking of a radar target in the low-angle regime when a diffuse multipath component is present. The use of the ML estimator is targeted in light of its ability to contend with a small number of array date snapshots and because of recent algorithmic developments that significantly reduce the associated computational burden. The goal of this work is to define and develop an ML-based estimation procedure suitable for continuous sources as well as for point sources. Present ML-based methods assume the sources of emission to be point-like in nature and are only strictly applicable in a specular multipath environment. If successful, the resulting ML algorithm will be generalized for use in more complex diffuse signal source scenarios such as those encountered in tomography, radio astronomy, and sonar.