Robust radar automatic target recognition algorithm based on HRRP signature

Abstract

Automatic target recognition (ATR) is an important function for modern radar. High resolution range profile (HRRP) of target contains target structure signatures, such as target size, scatterer distribution, etc., which is a promising signature for ATR. Statistical modeling of target HRRPs is the key stage for HRRP statistical recognition, including model selection and parameter estimation. For statistical recognition algorithms, it is generally assumed that the test samples follow the same distribution model as that of the training data. Since the signal-to-noise ratio (SNR) of the received HRRP is a function of target distance, the assumption may be not met in practice. In this paper, we present a robust method for HRRP statistical recognition when SNR of test HRRP is lower than that of training samples. The noise is assumed independent Gaussian distributed, while HRRP is modeled by probabilistic principal component analysis (PPCA) model. Simulated experiments based on measured data show the effectiveness of the proposed method.

Keywords

radar target recognition / high resolution range profile (HRRP) / probabilistic principal component analysis (PPCA)

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg