DocumentCode :
1441524
Title :
A Statistical Approach to Mitigating Persistent Clutter in Radar Reflectivity Data
Author :
Lakshmanan, Valliappa ; Zhang, Jian ; Hondl, Kurt ; Langston, Carrie
Author_Institution :
Cooperative Inst. of Mesoscale Meteorol. Studies, Univ. of Oklahoma, Norman, OK, USA
Volume :
5
Issue :
2
fYear :
2012
fDate :
4/1/2012 12:00:00 AM
Firstpage :
652
Lastpage :
662
Abstract :
Although there are several effective signal processing methods for identifying and removing radar echoes due to ground clutter, the need to mitigate persistent clutter in radar moment data still exists if such techniques were not applied during data collection and the time series data are not available. A statistical approach to creating a clutter map from “found data”, i.e., data not specifically collected in clear air is described in this paper. Different methods of mitigating ground clutter are then compared using an information theory statistical approach and the best mitigation approach chosen. The technique described in this paper allows for the mitigation of persistent ground clutter returns in archived data where signal processing techniques have not been applied or have been conservatively applied. It is also helpful for correcting mobile radar data where the creation of a clear-air clutter map is impractical. Accordingly, the technique is demonstrated in each of the above situations.
Keywords :
atmospheric techniques; data analysis; echo; information theory; meteorological radar; signal processing; statistical analysis; time series; clear-air clutter map; effective signal processing methods; ground clutter data; information theory; meteorological radar; mobile radar data; persistent ground clutter mitigation; radar echoes; radar moment data; radar reflectivity data; signal processing techniques; statistical approach; time series data; Biology; Clutter; Histograms; Interpolation; Radar clutter; Radar remote sensing; Clutter; meteorological radar;
fLanguage :
English
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Publisher :
ieee
ISSN :
1939-1404
Type :
jour
DOI :
10.1109/JSTARS.2011.2181828
Filename :
6146381
Link To Document :
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