Polarimetric radar target recognition framework based on LSTM

Abstract

Polarimetric information is of great importance for radar target recognition. Conventional polarimetric features are hand-designed based on scattering mechanism. In this study, a novel polarimetric target recognition framework based on long–short-term memory (LSTM) network is proposed. The different polarimetric channels are regarded as the sequential inputs in LSTM, and the features are extracted automatically. Experimental results on dual-polarised high-resolution range profile recognition demonstrate that the features learnt by LSTM are more discriminating than conventional features. The recognition performance of the proposed method outperforms the state-of-the-art methods as well.

Keywords

• radar imaging
• radar target recognition
• feature extraction
• synthetic aperture radar
• radar polarimetry
• scattering mechanism
• novel polarimetric target recognition framework
• long–short-term memory network
• lstm
• different polarimetric channels
• dual-polarised high-resolution range profile recognition
• features learnt
• conventional features
• recognition performance
• polarimetric radar target recognition framework
• polarimetric information
• conventional polarimetric features
• hand-designed