In this paper we investigate the problem of fast and accurate classification of naval targets from radar returns. The algorithms can be applied to both 1-D and 2-D data (i.e. high range resolution and imaging radar returns). We describe the structure of these algorithms and report experimental results on their performance with synthetic returns from ships.
We have successfully addressed the problem of reducing the target model representations with respect to viewpoint variations and other Sensor parametric variations. Our method can be viewed as a quantization of the space of sensing Operations. The resulting multiresolution aspect graph is a (relational) graph representation of this quantization. Aspect graphs of target radar returns are generated algorithmically. Since our off-line model/parameter tuning methods are based on general vector quantization, our methods extend naturally and efficiently to multi-sensor data: LADAR, TV, mmWave, SAR, etc.. We describe new results on the “continuity” of the aspect graph, new properties and improvements of our algorithmic constructions.
Our basic classification algorithm utilizes a cascade of a wavelet preprocessor followed by a tree-structured clustering algorithm: a learning mode can be also added. We develop a high performance parallel progressive classification algorithm and report On its performance and Complexity. We show experiments illustrating that the parallel algorithm Outperforms a Compound version (which is the more intuitive choice) and that it has performance close to Bayes optimal classification (via comparison to Learning Vector Quantization). We Outline an analytical framework for establishing these results theoretically. We also discuss similar experiments from face recognition and medical image classification problems.