Radar target classification using fusion of  compressively sensed backscatter

Ismail Jouny

doi:10.1117/12.2587049

12 April 2021 Radar target classification using fusion of compressively sensed backscatter

Ismail Jouny

Proceedings Volume 11729, Automatic Target Recognition XXXI; 117290T (2021) https://doi.org/10.1117/12.2587049
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

The focus in this paper is on fusing compressively sensed radar data to accomplish non-cooperative radar target identification. The primary motivation is to assess the benefit of fusing compressively sensed radar signals in target identification compared to systems that do not use compressive sensing, and systems that fuse individual sensor decisions instead of sensor data. The paper will use fusion techniques developed in the past decade to combine compressively sensed radar returns and then render a target classification decision. The paper shows the difference between fusing the radar data and making a decision and fusing target identification decisions made at individual compressively sensed radar systems. Reconstructing the radar target down range profile from a fusion of compressively sensed data is also examined. Alternatively, scattering centers are extracted at each separate radar system and fused as features for a radar target recognition system. The radar used in this study is a stepped frequency radar. The radar returns examined represent the backscatter from four commercial aircraft models at various azimuth positions. The data may be corrupted by additive noise. The compressive sensing techniques rely on using a random Gaussian measuring matrix, and the signal recovery techniques use the well-known OMB methods.

Conference Presentation

Citation Download Citation

Ismail Jouny "Radar target classification using fusion of compressively sensed backscatter", Proc. SPIE 11729, Automatic Target Recognition XXXI, 117290T (12 April 2021); https://doi.org/10.1117/12.2587049

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