Radar target classification using compressively sensed features

Ismail Jouny

doi:10.1117/12.2249632

2 May 2017 Radar target classification using compressively sensed features

Ismail Jouny

Proceedings Volume 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI; 1020019 (2017) https://doi.org/10.1117/12.2249632
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Ismail Jouny "Radar target classification using compressively sensed features", Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 1020019 (2 May 2017); https://doi.org/10.1117/12.2249632

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