Radar target recognition using LVQ network and majority voting

This paper describes a novel method for radar target classification based on high range resolution profile (HRRP). In view of the non-stationary characteristic of radar signal, adaptive Gaussian basis representation (AGR) is utilized to extract features from raw HRRP signatures to fully retain the physics information of target. Then learning vector quantization (LVQ) network is adopted to tackle the classification of single echo (after features extraction) with complicated space distribution. Finally,c! combined classification scheme combining LVQ networks with the majority voting rule is designed to circumvent the sensitivity of HRAP to target aspects based on sequential echoes. A actual example using three scaled aircraft model data collected in microwave anechoic chamber is presented to demonstrate the effectiveness of proposed scheme.