False‐alarm suppression with random forest by exploiting ambiguity features of targets

Suppressing false alarms without impacting detection rate is an essential issue for search radars. Traditional threshold detection methods identify true targets by simply comparing the energy of target candidates with a threshold. In this work, a new data‐driven false‐alarm suppression approach is p...

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Bibliographic Details
Published inElectronics letters Vol. 58; no. 24; pp. 917 - 919
Main Authors Wang, Zhifei, Yu, Junpeng, Yang, Yuhao
Format Journal Article
LanguageEnglish
Published Stevenage John Wiley & Sons, Inc 01.11.2022
Wiley
Subjects
Accuracy
Aircraft
Algorithms
Ambiguity
Datasets
Decision trees
False alarms
Fourier transforms
Radar
Signal processing
Target detection
Target recognition
Velocity
Wavelet transforms
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Summary:Suppressing false alarms without impacting detection rate is an essential issue for search radars. Traditional threshold detection methods identify true targets by simply comparing the energy of target candidates with a threshold. In this work, a new data‐driven false‐alarm suppression approach is proposed based on a random forest model. It discriminates true targets and false alarms in the developed multi‐dimensional ambiguity feature spaces, instead of the time‐frequency features used in previous works, to fulfill short dwell‐time requirement of search radars. Evaluations through field experiments demonstrate that the proposed method can achieve as high as 98% validation accuracy and a significant improvement of detection performance compared to the conventional threshold detection.
ISSN:0013-5194
1350-911X
DOI:10.1049/ell2.12642