DocumentCode
1544597
Title
Robust statistical feature based aircraft identification
Author
Mitchell, Richard A. ; Westerkamp, John J.
Author_Institution
Qualia Comput. Inc., Beavercreek, OH, USA
Volume
35
Issue
3
fYear
1999
fDate
7/1/1999 12:00:00 AM
Firstpage
1077
Lastpage
1094
Abstract
The statistical feature based (StaF) classifier is presented for robust high range resolution (HRR) radar aircraft identification (ID). HRR signature peak features are selected “on the fly” with no a priori assumptions about the number or location of the features. Features extracted depends on the information content of the observed signature making the number, location, and amplitude of features random variables. A primary goal for this research is to increase classifier robustness by maintaining high known target ID while minimizing unknown target errors. Results are presented demonstrating that the StaF classifier can significantly reduce errors associated with unknown targets while maintaining a high probability of correct classification
Keywords
Bayes methods; belief networks; feature extraction; object recognition; pattern classification; radar resolution; radar target recognition; sensor fusion; Bayes classifier; automatic target recognition; belief hypothesis; belief table; classifier robustness; confusion matrix; feature extraction; high known target identification; high probability of correct classification; information content; multipeak decision fusion; on the fly selection; parameter estimation; quadratic classifier; radar aircraft identification; random variables; reduced errors; robust high range resolution radar; robust statistical feature based classifier; signature peak features; unknown targets; Airborne radar; Data mining; Feature extraction; Laboratories; Military aircraft; Probability; Radar scattering; Random variables; Robustness; Target recognition;
fLanguage
English
Journal_Title
Aerospace and Electronic Systems, IEEE Transactions on
Publisher
ieee
ISSN
0018-9251
Type
jour
DOI
10.1109/7.784076
Filename
784076
Link To Document