Abstract
We propose an efficient technique for target classification using one-dimensional high resolution range profile (HRRP). The proposed technique utilizes the unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm to extract scattering centers and then reconstruct superresolution range profiles. Moreover, we employ the central moments to provide translation invariant and scale invariant feature vectors. Finally, the proposed unitary ESPRIT (U-ESPRIT) based range profile reconstruction method is applied to the simulated annealing resilient backpropagation (SARPROP) classification algorithm to evaluate the recognition performances. Recognition results using four different aircraft models are presented to assess the effectiveness of the proposed technique, and they are compared with those of the conventional range profiles obtained by fast Fourier transform (FFT). Comparison results on simulated data show that the HRRP reconstruction method is better than directly using HRRP in targets classification.
Highlights
High resolution range profile (HRRP) can reflect the radar radiation, that is, the scatters, which are projected onto the line of sight
HRRP has wide applications in automatic target recognition (ATR) field since Li and Yang [1] proposed this for the first time
Gaussian noise is added to the simulated target signature data with certain signal-to-noise ratio (SNR) defined by where Es is the total energy of the frequency domain data and σn2 is the variance of the additive noise
Summary
High resolution range profile (HRRP) can reflect the radar radiation, that is, the scatters, which are projected onto the line of sight. There are two major challenges to use the range profile in target recognition. One is that it is highly aspect dependent and with large data storage. Several research works proposed improving the range resolution through superresolution techniques such as Multiple Signal Classification (MUSIC) [3], maximum likelihood [4], maximum entropy method [5], and Prony [6]. We reconstruct the HRRP by means of the extracted scattering centers for target recognition. Due to the fact that there are only several isolated scattering centers at each aspect, the storage burden can be reduced in comparison with the conventional HRRP based classification method.
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