Title :
A neural network ATR for high range resolution radar signature recognition of moving ground targets
Author_Institution :
Veridian Eng., Dayton, OH, USA
Abstract :
High range resolution (HRR) radar produces high resolution target signatures of moving ground targets that can be separated from ground clutter using Doppler processing. As a result, HRR sensors are a leading candidate to provide all weather, day/night, long-standoff identification of moving ground targets. Previous automatic target recognition (ATR) studies have demonstrated the usefulness of HRR ATR with traditional statistical techniques such as mean-squared error template-based classifiers. This paper presents the results on an initial investigation of the use of artificial neural network (ANN) classifiers for HRR ATR in a continuous tracking (CT) scenario.
Keywords :
image classification; mean square error methods; military radar; neural nets; radar clutter; radar computing; radar imaging; radar resolution; radar target recognition; statistical analysis; ANN classifiers; DARPA; Doppler processing; HRR sensors; MTI radar; SAR data; all weather identification; artificial neural network; automatic target recognition; automatic target verification; continuous tracking; day/night identification; ground clutter; high range resolution radar; high range resolution radar signature recognition; high resolution target signatures; long-standoff identification; mean-squared error template-based classifiers; military radar; moving ground targets; neural network ATR; statistical techniques; Artificial neural networks; Computed tomography; Doppler radar; Land vehicles; Neural networks; Radar imaging; Radar tracking; Synthetic aperture radar; Target recognition; Target tracking;
Conference_Titel :
Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-7803-5700-0
DOI :
10.1109/ACSSC.1999.831904
