One-dimension range profile identification of radar targets based on a linear interpolation neural network

One-dimension range profile can reflect the precise geometric structure features of radar targets. The approach is comprehensively used for radar target identification (RTI), however it varies with target posture. This paper presents a novel neural network model - linear interpolation neural network (LINN) to solve the problem. LINN combines the variation information of one-dimension range profile with its invariant feature information. Simulation results show that this method greatly improves the target identification performance of radar systems.