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Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra

Science china(Information Sciences) 2010年第7期53卷 1446-1460页

作者： chen feng, liu hongwei, du lan & bao zheng national laboratory of radar signal processing, xidian university, xi’an 710071, china 1. national laboratory of radar signal processing Xidian University Xi’an 710071 China

Most low-resolution radar systems, especially ground surveillance radar systems, work at relatively low pulse repeat frequency (PRF) and with short time-on-target (TOT) (duration in scanning). Low PRF leads to Doppler ambiguity and short TOT results in low Doppler resolution, which poses a problem to target classification with low-resolution radar based on the jet engine modulation (JEM) characteristic of radar echo. From the pattern classification viewpoint, we propose a method of using dispersion situations of JEM eigenvalue spectra to categorize aeroplanes into three kinds, namely turbojet aircraft, prop aircraft and helicopter. We analyze the mathematical model of JEM echoes consisting of a series of line spectra and regard them as a sum of several series of harmonious waves. Classification features can be extracted based on the harmonious wave sum model. Some schemes for extracting features from echoes within or between pulses are proposed. Low-dimensional features are extracted to reduce computation burden. Our methods do not compensate for the fuselage echoes and are insensitive to the variation of fuselage Doppler. The feasibility of our methods is demonstrated by simulation experiment.

关键词： jet engine modulation (JEM) low-resolution radar low pulse repeat frequency (PRF) short time-on-target (TOT) target classification

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A WEIGHTED FEATURE REduCTION METHOD FOR POWER SPECTRA OF radar HRRPS

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Journal of Electronics(china) 2006年第3期23卷 365-369页

作者： du lan liu hongwei bao zheng Zhang Junying national laboratory of radar signal processing Xidian University Xi＇an 710071 China

Feature reduction is a key process in pattern recognition. This paper deals with the feature reduction methods for a time-shift invariant feature, power spectrum, in radar Automatic Target Recognition (RATR) using High-Resolution Range Profiles (HRRPs). Several existing feature reduction methods in pattern recognition are analyzed, and a weighted feature reduction method based on Fisher's Discriminant Ratio (FDR) is proposed in this paper. According to the characteristics of radar HRRP target recognition, this proposed method searches the optimal weight vector for power spectra of HRRPs by means of an iterative algorithm, and thus reduces feature dimensionality. Compared with the method of using raw power spectra and some existing feature reduction methods, the weighted feature reduction method can not only reduce feature dimensionality, but also improve recognition performance with low computation complexity. In the recognition experiments based on measured data, the proposed method is robust to different test data and achieves good recognition results.

关键词： radar Automatic Target Recognition (RATR) High-Resolution Range Profile (HRRP) Power spectrum Feature reduction Fisher's Discriminant Ratio (FDR)

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Robust radar automatic target recognition algorithm based on HRRP signature

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Frontiers of Electrical and Electronic Engineering in china 2012年第1期7卷 49-55页

作者： hongwei liu feng chen lan du zheng bao national laboratory of radar signal processing Xidian UniversityXi'an 710071China

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

关键词： radar target recognition high resolution range profile (HRRP) probabilistic principal component analysis (PPCA)

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radar automatic target recognition based on feature extraction for complex HRRP

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Science in china(Series F) 2008年第8期51卷 1138-1153页

作者： du lan liu hongwei bao zheng ZHANG JunYing national laboratory of radar signal processing Xidian University Xi'an 710071 China

radar high-resolution range profile （HRRP） has received intensive attention from the radar automatic target recognition （RATR） community. Usually, since the initial phase of a complex HRRP is strongly sensitive to target position variation, which is referred to as the initial phase sensitivity in this paper, only the amplitude information in the complex HRRP, called the real HRRP in this paper, is used for RATR, whereas the phase information is discarded. However, the remaining phase information except for initial phases in the complex HRRP also contains valuable target discriminant information. This paper proposes a novel feature extraction method for the complex HRRP. The extracted complex feature vector, referred to as the complex feature vector with difference phases, contains the difference phase information between range cells but no initial phase information in the complex HRRR According to the scattering center model, the physical mechanism of the proposed complex feature vector is similar to that of the real HRRP, except for reserving some phase information independent of the initial phase in the complex HRRP. The recognition algorithms, frame-template establishment methods and preprocessing methods used in the real HRRP-based RATR can also be applied to the proposed complex feature vector-based RATR. Moreover, the components in the complex feature vector with difference phases approximate to follow Gaussian distribution, which make it simple to perform the statistical recognition by such complex feature vector. The recognition experiments based on measured data show that the proposed complex feature vector can obtain better recognition performance than the real HRRP if only the cell interval parameters are properly selected.

关键词： complex high-resolution range profile (HRRP) radar automatic target recognition (RATR) feature extraction minimum Euclidean distance classifier adaptive Gaussian classifier (AGC)

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A new feature extraction method using the amplitude fluctuation property of target HRRP for radar automatic target recognition

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Frontiers of Electrical and Electronic Engineering in china 2006年第2期1卷 171-176页

作者： du lan liu Hong-wei bao zheng ZHANG Jun-ying national laboratory of radar signal processing Xidian UniversityXi'anShaanxi 710071China

due to the aspect sensitivity of high-resolution range profile(HRRP),traditional radar HRRP target recognition methods usually use average profile within some target-aspect region as the target-aspect ***,the amplitude fluctuation property of target HRRP also represents some feature information of the *** on the scattering center model,a new feature extraction method using the amplitude fluctuation property of target HRRP is proposed in this *** weighted HRRP feature extracted by the new method can represent the scatterer distribution in every range cell,thereby it can describe the scattering property of the target *** experimental results based on measured data show that the new feature extraction method can greatly improve recognition performances.

关键词： radar automatic target recognition(RATR) HRRP Feature extraction Scattering center model Average profile Variance profile

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