Abstract In target recognition based on high range resolution profile (HRRP), the major challenge is to extract invariant and robust features from the measured high dimension range profile samples. Focused on the reduction of dimensionality, a novel algorithm for extracting HRRP features is proposed in this paper, and the Legendre moments with orthogonal and translation-invariant properties was adopted. The proposed algorithm utilizes the average HRRP in aspect-frame, and then preprocesses the HRRP with amplitude ℓ1 normalization. The Legendre moments are calculated with mapping range values into orthogonal definition domain to provide invariant feature vectors. Meanwhile, spectrum analysis (SA), which can be regard as first-order approximation of relax algorithm, is adopted to reconstruct the real HRRP obtained by inverse fast fourier transform (IFFT) in order to desensitize moments in low SNR environment. Several experiments with four different targets measured data demonstrate that Legendre moments features with flexible maximum order to be chosen have significant advantages over central moments. SA can provide a more robust recognition performance in low signal noise ratio (SNR) environment as 5 dB compared with those of HRRP directly obtained by IFFT.
Target Recognition of Radar HRRP Using the Envelope Reconstruction
2017-07-27
20 pages
Article/Chapter (Book)
Electronic Resource
English