The paper focuses on extracting scattering centers of radar targets using compressive sensing and using them as features
in a target recognition system. It has been shown that a target’s high resolution range profile (HRRP) is sparse in time
corresponding to few scatterers that can be associated with target geometry. The recognition system is tested using real
radar data of commercial aircraft models. Classification is carried out using distance based and correlation based
techniques. Scenarios where the target aspect angle is unknown or known to be within a certain range are also examined.
Ismail Jouny, "Radar target classification using compressively sensed features," Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 1020019 (Presented at SPIE Defense + Security: April 12, 2017; Published: 2 May 2017); https://doi.org/10.1117/12.2249632.
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