Study On Target Feature Extraction Based On Radar Image

Radar imaging technique has the ability of all-weather, day/night and long rangeapplications, which enhances the radar capability of information acquisitiondramatically. Therefore, radar imaging technique plays an important role in manymilitary and civilian fields. As radar imaging technique has developed rapidly, thecapability of image collection has grown much stronger. The aim of radar imaging is toacquire information of target for target detection, classification and recognition, andradar image based target recognition has attracted more and more attention especially.The performance of data-driven recognition method depends on the information thatmeasurements reflect, while the information is influenced by the environmental factorsgreatly in practical application, and this is a challenge for the data-driven recoginitonmethod. The environmental factors have minor impact on physical feature of target,such as geometrical dimension and physical structure, which can be reflected by themechanism of radar imaging. This dissertation studies on the physical feature extractionfrom radar image based on the parametric signal model, and focuses on the technique ofphysical mechanism feature extraction in three aspects: cross-range scaling ofinterferometric ISAR, micro-motion feature extraction of target and electromagneticfeature extraction of target (attributed scattering center and polarimetric feature). Themain work can be summarized as follows:1. Aiming at the phase wrapping problem in the cross-range scaling ofinterferometric ISAR, a novel algorithm is proposed based on Randomized HoughTransform. Using the linear relationship between the azimuth positions derived from thewrapping interferometric phases and the Doppler frequencies of the dominant scatters inISAR image, the proposed algorithm estimates the scale factor between true azimuthposition and Doppler frequency and determines the ISAR image scale in the cross rangedirection, consequently avoiding the complex phase unwrapping procedure. Thesimulation results verify the validity and anti-noise capability of this algorithm, and thegeometrical dimension of target can be obtained according to the cross-range scaledISAR image.2. The micro-motion feature extraction is studied in this section, and the main workconcerns the following two aspects. Firstly, based on the analysis of narrowband echoesof target, we point out that its micro-motion feature extraction is equivalent to theinstantaneous frequency estimation of multi-component non-stationary signal. A novelmethod based on curve tracking algorithm is proposed to extract micro-motion feature.The proposed method separates the time-frequency curves successfully in time-frequency domain with the Nearest Neighbor Data Association (NNDA) algorithm,and the separated time-frequency curve of each component signal is smoothed by theextended Kalman filter, then the parameter of micro-motion can be estimated with thesmoothed time-frequency curves. Secondly the wideband echoes are analyzed withsome conclusions that the wideband echoes posess micro-range feature indownrange-slow time domain and micro-doppler feature in time-frequency domain.APES (Amplitude and phase estimation) is adopted to obtain the superresolution ofmicro-range, and then CT algorithm is used to separate and extract the micro-rangecurve; Signals of multi range cells are jointly analyzed to obtain the intacttime-frequency distribution of target, then CT algorithm is utilized to extractmicro-doppler feature. Simulation results on electromagnetic data verify the validity ofthe proposed algorithms.3. Attributed scattering center extraction is discussed in this section and it containstwo parts.(1) Considering the sparsity of the frequency-aspect backscattered data in theattributed scattering center model parameter domain, a novel method based ondictionary refinement is proposed to extract attributed scattering center and estimateparameters in frequency-aspect domain. Due to the high dimension of model parameter,one high dimensional joint dictionary needs to be constructed, which may cost a massstorage. Aiming at this problem, dictionary zooming and alternative optimization areexplored to reduce the dictionary dimension in the proposed algorithm, sparse signalrecovery problem is solved by utilizing OMP (Orthognal Matching Pursuit) algorithmcombined with RELAX to alleviate the influence of closely-spaced scattering centers oneach other, and attributed scattering centers are extracted from frequency-aspect domain.(2) A new method based on range characteristic and aspect characteristic decoupling isdeveloped to reduce the storage resources request further. Two low dimensionaldictionaries including localization and aspect attribute parameters respectively areconstructed to replace the high dimensional joint dictionary to decouple the rangecharacteristic and aspect characteristic and obtain the parameters estimation. With theextracted attributed scattering centers, geometrical dimensions of the target or its mainstructure can be estimated. Numerical results both on electromagnetic computation dataand measured data verify the validity of the proposed method.4. This section focuses on the extraction of fully polarimetric attributed scatteringcenter:(1) Considering the joint sparsity of the fully polarimetric measurements in theattributed scattering center model parameter domain, a novel method based on jointsparsity is proposed for attributed scattering center extraction; and the recovered sparse coefficient matrix is used for polarimetric signature extraction, the three dimensionalpose of target can be acquired by interferometric processing. Alternative optimizationand dictionary refinement are utilized for dimension reduction, which are also adoptedin the dictionary refinement based attributed scattering center extraction method, whilejoint sparsity constraint is imposed on the sparse coefficient matrix, and simultaneousorthogonal matching pursuit (SOMP) are adopted to find the solution of the joint sparseoptimization problem for attributed scattering center extraction.(2) Aiming to extractattributed features of overlapped attributed scattering centers, row sparse constraint andmatrix sparse constraint are imposed on the polarimetric decomposition coefficientmatrix of target based on the sparsity of scattering center in the parameter domain andscattering mechanism domain, coordinate decent technique is employed to optimizepolarimetric decomposition coefficient matrix and polarimetric scattering mechanismdictionary to extract attributed scattering center and polarimetric signaturesimultaneously. Numerical results on electromagnetic computation data verify thevalidity of the proposed algorithms.