.png){kind=icon}
Return
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}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
source tracking
data-driven windows
root-MUSIC
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.9K
Citations:
1.7W
Organization
No organization information available
Cited Papers
.png){kind=icon}
Ansa-[(2,2-bis-η-cyclopentadienyl)propane]-molybdenum and -tungsten chemistry and related studies
PREAI
ShareSaveShareSaveShareSaveShareSave
no more
no more