Resonant Region NCTR Research
OHIO STATE UNIV COLUMBUS ELECTROSCIENCE LAB
Pagination or Media Count:
This report discusses progress being made in research on resonant region radar target identification. Topics studied include the use of Complex Natural Resonances, syntactic-classifier-based identification techniques, optimization criteria for feature selection, and target substructure studies. Under the topic of Complex Natural Resonances CNR, classification probability results using the Lai model are shown for the case of two commercial aircraft. Predictor-correlator results are also shown for a set of three land vehicles. In the case of syntactic target identification, an analysis of the performance of three different pattern representation schemes has been begun. Level and octant crossing examples have been used with the OSUESL data base to derive a set of syntactic primitives. Studies are given showing the classification performance for the three types of primitives derived here. This area of research has also begun the development of a feature vector optimization scheme based on various cost functions. Results are given. The study of substructure identification based on transient waveforms and polarimetric color images has also begun to produce results. Some preliminary conclusions are given in Chapter V on this topic. Keywords Algorithms Radar cross sections.
- Active and Passive Radar Detection and Equipment