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

EIA：弥散多径环境中基于最大似然的到达角估计

• 批准号: 8707681
• 负责人: Michael Zoltowski
• 金额: $ 6万
• 依托单位: Purdue Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-08-01 至 1990-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8707681&HistoricalAwards=false
• 关键词: EIA; Maximum; Likelihood; Based; Angle

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.

该提案是为了获得工程启动奖，概述了对使用最大似然 (ML) 估计程序来确定撞击色散介质中传感器阵列的信号方向的调查。 感兴趣的特定应用涉及当存在扩散多径分量时在低角度范围内跟踪雷达目标。 ML 估计器的使用是有针对性的，因为它能够应对少量数组日期快照，而且最近的算法开发可以显着减少相关的计算负担。 这项工作的目标是定义和开发适用于连续源和点源的基于机器学习的估计程序。 目前基于机器学习的方法假设发射源本质上是点状的，并且仅严格适用于镜面多路径环境。 如果成功，所得的机器学习算法将被推广用于更复杂的漫射信号源场景，例如断层扫描、射电天文学和声纳中遇到的场景。