Radar target identification using HRRP-based features and Extreme Learning Machines

Ismail Jouny

doi:10.1117/12.2514479

7 May 2019 Radar target identification using HRRP-based features and Extreme Learning Machines

Ismail Jouny

Proceedings Volume 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII; 1101813 (2019) https://doi.org/10.1117/12.2514479
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Radar target recognition performance using extreme learning machines (ELM) is examined in this study and compared with optimal classifiers. Classification under various adverse scenarios involving additive noise, azimuth ambiguity, azimuth mismatch between library and unknown target, presence of extraneous scatterers, signature occlusion, absolute phase knowledge, etc. are examined. ELM can be trained expeditiously and are suited for radar target recognition particularly with large training database. The effectiveness of ELM (single layer or multilayered) as a target recognition tool is the focus in this study that relies on real radar data collected in a compact range environment using a stepped-frequency system.

Conference Presentation

Citation Download Citation

Ismail Jouny "Radar target identification using HRRP-based features and Extreme Learning Machines", Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 1101813 (7 May 2019); https://doi.org/10.1117/12.2514479

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