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)

References

[1] Webb A R. Gamma mixture models for target recognition. Pattern Recognition, 2000, 33(12): 2045-2054

[2] Copsey K, Webb A R. Bayesian Gamma mixture model approach to radar target recognition. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1201-1217

[3] Du L, Liu H W, Bao Z, Zhang J Y. A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Transactions on Signal Processing, 2006, 54(6): 2226-2238

[4] Du L, Liu H W, Bao Z. Radar HRRP statistical recognition based on hypersphere model. Signal Processing, 2008, 88(5): 1176-1190

[5] Du L, Liu H W, Bao Z. Radar HRRP statistical recognition: Parametric model and model selection. IEEE Transactionson Signal Processing, 2008, 56(5): 1931-1944

[6] Shi L, Wang P H, Liu H W, Xu L, Bao Z. Radar HRRP statistical recognition with local factor analysis by automatic Bayesian Ying-Yang harmony learning. IEEE Transactions on Signal Processing, 2011, 59(2): 610-617

[7] Wang P H, Shi L, Du L, Liu H W, Xu L, Bao Z. Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 300-317

[8] Xu L, Krzyzak A, Oja E. Rival penalized competitive learning for clustering analysis, RBF net, and curve detection. IEEE Transactions on Neural Networks, 1993, 4(4): 636-649

[9] Xu L. Bayesian Ying-Yang system, best harmony learning and five action circling. Frontiers of Electrical and Electronic Engineering in China, 2010, 5(3): 281-328

[10] Xu L. Bayesian Ying-Yang system and theory as a unified statistical learning approach: (V) Temporal modeling for temporal perception and control. Invited Paper. In: Proceedings of the International Conference on Neural Information Processing (ICONIP’98). 1998, 2: 877-884

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg