Study On Radar Automatic Target Recognition Based On High Resolution Range Profile

The radar automatic target recognition is of great value in military and civilian use.High-Resolution Range Profile(HRRP) is characterized by rich information and accessibility,so itis one of the most important developments of radar target recognition. This thesis intends to explorerobust and practical recognition algorithms and the critical problems of HRRP recognition, includingtarget-aspect sensitivity, feature extraction, the robust recognition under noise background andsequence recognition, which is a useful exploration of the application of HRRP' target recognition.The main contents and the new ideas are as follows:1. According to the scattering center model, the impacts of the equiangular division underdifferent target-aspect on the target recognition is studied in this paper. A new frame segmentationmethod based on cross correlation coefficient is proposed. Comparing with the Max CorrelationCoefficient-Template Matching Method (MCC-TMM), the presented method can efficiently improverecognition performance.2．In the radar target recognition based on the statistic model, the problem of model mismatch isexisted when using classical parametric probability density model to describe the statistical propertiesof HRRP. Due to this problem, this thesis studies the modeling of the backward cloud model, andfigures out the algorithms of the certainty degree of cloud drop and the backward cloud membership,and proposes the radar target recognition method based on backward cloud model. Simulation resultsdemonstrate that the proposed approach is effective for radar target identification. Compared withGaussian models, it has advantages of higher identification accuracy, better anti-noise performance,more relaxed requirements for azimuth division, and can also achieve good recognition results in thecase of fewer training samples.3. When scatter feature within one angular sector mismatches, the HRRP energy within itscorresponding angular sector shows the feature of non-linear distribution. In view of this, an approachbased on kernel principle component analysis reconstruction is proposed in this thesis. Kernelprinciple component analysis is used to extract eigen subspace in every equiangular sector for a start,and then test sample is reconstructed by projecting it onto the eigen subspace of each angular sector,finally, the type of test sample is determined by the minimum reconstruction error. Simulations resultsshow that the proposed approach relaxes the angular sector, requires lower angular division precision, and meanwhile it has a better anti-noise performance compared with principle component analysisreconstruction method and MCC-TMM.4. As an effective feature extraction method in radar target HRRP recognition community, LinearDiscriminant Analysis (LDA) faces four main shortcomings. First, it relies on the assumption that thesamples in each class satisfy Gaussian distribution with the same covariance matrix; Second, thedimension of the eigen subspace has an upper limit after dimension reduction; Third, the effect ofboundary samples are not highlighted when calculating scatter matrix; Fourth, when the dimensionof samples is more than or close to the number of samples, the so-called "Small Sample Sizeproblem" is likely to emerge. To tackle these problems, the methods of nonparametric featureextraction are studied in this paper:(1) a radar target recognition method based on nonparametricfeature analysis (NFA) and backward cloud mode is proposed. NFA algorithm uses the local KNNmean instead of class mean when calculating scatter matrix; the rank of between-class scatter matrixis increased when it's calculated with samples' local information; the effect of class boundary samplesare strengthened by weighting function. So the first three defects of LDA algorithm can be offset inNFA. The deficiency of probability theory and fuzzy mathematics in dealing with uncertain problemsis improved by using backward cloud model, it is more consistent with the fuzzy distribution of eigensubspace's for the target HRRP after feature extraction;(2) a radar target recognition method based onNonparametric Maximum Margin Criterion (NMMC) is proposed. This method integrates theadvantages of maximum margin criterion and nonparametric feature extraction. The quotientoperation is replaced by difference operation in NMMC, in order to solve the problem of small samplesize in LDA algorithm, and the other three application defects of LDA algorithm are offset by usingnonparametric method to calculate within-class and between-class scatter matrix.Compared with parametric feature extraction, the simulation experiment demonstrates thatnonparametric feature extraction could increase within-class polymerization and between-classdivisibility, so it can improve target recognition rate and noise robustness.5. As traditional algorithms are not robust to noise, this thesis proposes HRRP's targetrecognition against the noisy environment. In this method, the formula of parameter's selection isgiven by analyzing the impacts of power transformation parameter's selection with different SNRs onrecognition effect, and the data-preprocessing based on adaptive power transformation is studiedintegrating the estimation method for real-time SNR. And an auto-correlation wavelet support vectormachine is proposed as the classifier, the kernel of which is constructed with a compactly supportedwavelet satisfies the translation invariant property. The simulation results show that the proposed algorithm is of better noise-robustness and higher recognition rate compared with Gaussian kernelSVM.6. To achieve steadier and more reliable recognition result, a sequence model of HRRP for radartarget recognition was designed in the thesis. For the backbone of this model, a radar targetrecognition algorithm is proposed based on HRRP's sequence. In this method, grey incidence operatoris introduced into the probability-reasoning theory, and the calculation method of MYCIN's uncertainfactor is given based on the framework of Bayes theory. Compared with recognition algorithms with asingle sample, the proposed algorithm has good prospects for engineering applications with higherrecognition rate, better stability, and stronger anti-interference.