.png){kind=icon}
返回
Sensor array signal tracking using a data-driven window approach
.png){kind=icon}
PRE
AI
DOI：10.1016/S0165-1684(00)00139-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In many practical source tracking applications, the interval of source stationarity may severely vary with time, so that array observations may contain both almost stationary data blocks and nonstationary data intervals with rapidly moving sources. Moreover, typical situations may occur where some sources move rapidly within the window exploited, whereas the motion of the other sources is weak. In such scenarios, the traditional fixed-window approach appears to be nonoptimal because it may lead to a very poor tracking performance. Below, we address the narrowband direction of arrival (DOA) tracking problem using a new adaptive-window approach. In our technique, a separate data-driven window is used for each source of interest. The optimization of window lengths is based on the bias-to-variance tradeoff. The comparison of our approach with conventional fixed-window algorithms is presented showing that the underlying idea has an evident potential in nonstationary scenarios with rapidly moving sources. A natural price for the improved tracking performance is a higher computational cost and the restriction of our approach by the scenarios with 'well-separated' sources. (C) 2000 Elsevier Science B.V. All rights reserved.
Keyword:
source tracking
data-driven windows
root-MUSIC
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.9K
被引数:
1.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
Ansa-[(2,2-bis-η-cyclopentadienyl)propane]-molybdenum and -tungsten chemistry and related studies
PREAI
分享收藏分享收藏分享收藏
没有更多内容
没有更多内容