Name
Abstract | ||
|---|---|---|
In many model-based automatic target recognition (ATR) systems the size of the model catalog can be a critical factor in determining the viability of the system. We examine an ATR system which uses synthetic high range resolution (HRR) radar data to determine how the classification performance is affected by the compression of the HRR model catalog. For this purpose the data is preprocessed, clustered and classified using nearest neighbor and radial basis function (RBF) classifiers. The effect of compression on classification performance is examined through simulations for both of these classification schemes. For the data in question we show that significant (100:1 or greater) compression can be achieved with little degradation in classification performance |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/ICASSP.1995.479735 | Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference |
Keywords | Field | DocType |
data compression,feedforward neural nets,radar computing,radar target recognition,HRR model catalog,automatic target recognition systems,classification performance,high range resolution radar data,model-catalog compression,nearest neighbor classifier,radar target recognition,radial basis function classifier,simulations | k-nearest neighbors algorithm,Radar,Radial basis function,Pattern recognition,Automatic target recognition,Computer science,Azimuth,Feature extraction,Artificial intelligence,Data compression,Cluster analysis | Conference |
Volume | ISSN | ISBN |
5 | 1520-6149 | 0-7803-2431-5 |
Citations | PageRank | References |
3 | 0.45 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Batu Ulug | 1 | 3 | 0.45 |
| Ahalt, S.C. | 2 | 28 | 4.65 |
| Mitchell, R.A. | 3 | 3 | 0.45 |