Automatic recognition of ground radar targets based on target RCS and short time spectrum variance

Author

Liaqat, S. ; Khan, S.A. ; Ihsan, M.B. ; Asghar, S.Z. ; Ejaz, A. ; Bhatti, A.I.

Author_Institution

Coll. of Electr. & Mech. Eng., Nat. Univ. of Sci. & Technol., Islamabad, Pakistan

fYear

2011

fDate

15-18 June 2011

Firstpage

164

Lastpage

167

Abstract

This paper presents a novel feature vector to be used with a robust automatic target recognition (ATR) classifier designed for a ground surveillance radar. A three element feature vector has been used where features are based on radar audio signal of 100 milliseconds duration. The short feature length allows fast real-time implementation of the classifier. Classification is done using a k-nearest neighbor (k-NN) classifier. There are two input classes to the classifier; automobile and pedestrian. Training has been done on real radar data. Classifier performance has been tested using test data from two different data sets. It has been demonstrated that in both cases, the overall classifier performance is above 80%.

Keywords

object detection; object recognition; pattern classification; radar imaging; search radar; automatic recognition; automobile; ground radar target; ground surveillance radar; k-nearest neighbor classifier performance; pedestrian; radar audio signal; real radar data; robust automatic target recognition classifier; short time spectrum variance; target RCS; three element feature vector; Automobiles; Doppler effect; Doppler radar; Feature extraction; Radar cross section; Support vector machine classification; automatic target recognition; classification; feature extraction; ground surveillance radar; k-nearest neighbour; micro Doppler phenomenon; radar audio signal;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on

Conference_Location

Istanbul

Print_ISBN

978-1-61284-919-5

Type

conf

DOI

10.1109/INISTA.2011.5946127

Filename

5946127