Research On Theory And Technique Of Ultra-wideband SAR Shallow Buried Targets Imaging And Detection

Air- or vehicle-borne ultra-wideband Synthetic Aperture Radar (SAR) can perform quick detection of shallow buried targets over large areas, which has the advantages of safety and efficiency. In this thesis, some theoretical and technical problems in ultra-wideband SAR shallow buried targets imaging and detection are studied on real systems.In the traditional UWB SAR information processing, imaging and detection are separated, which limits the improvement of final detection performance. Therefore, in this thesis, the theory of the imaging and detection integrated framework is proposed, which includes the detection oriented imaging and the imaging based detection. Based on this framework, a detection oriented Time-Frequency Representation Image Formation (TFRIF) is proposed, which can also solve the technical problem of imaging based detection and has applicable value in practice.For the problem of locating error and imaging focusing of shallow buried target caused by traditional image formations, two shallow buried target image formations, the Modified Wavefront Reconstruction (MWR) and the Subsurface Back-Projection (SBP) algorithms, are proposed, which have higher focusing and locating precision. According to the problem of no priori information of buried depth etc. in practical operating conditions, an Image Domain Refraction and Dispersion Correction (IDRDC) method for shallow buried targets focusing and locating is proposed, which can solve the problem of focusing and locating multiple targets with different buried depths in various soil environments without priori information.In order to improve the image quality further, a Radio Frequency Interference (RFI) suppression method on the region of support characteristic in the 2-dimensional frequency domain and a ground-plane multi-look registration technique for forward-looking systems are proposed, which can ensure RFI suppression performance while reduce system complex and improve the registration efficiency of the multi-look processing to suppress speckle noise in forward-looking systems, respectively.Nowadays, the major challenge limiting the practical use of UWB SAR in shallow buried targets detection is the too many false alarms. Therefore, efficient feature extraction and suitable discriminator design are key points to improve the shallow buried targets detection performance. In this thesis, the feature extraction technique for metallic landmines and unexploded ordnances is studied. For a metallic landmine target, a double-peak feature enhancement algorithm in the image domain and a Space-Wavenumber Distribution (SWD) based 4-dimensional scattering function estimation and associated feature selection method are proposed, which can extract the feature vector with the double-peak and aspect-invariance characteristics. For an unexploded ordnance target, based on the SWD and the Hu moment invariants, the multi-aspect feature is extracted.For the small sample learning and the one-class classification problems as shallow buried targets discrimination, considering the factors of the misclassification risk difference between targets and clutter and the buried environment diversity, the Fuzzy HyperSphere Support Vector Machine (FHS-SVM) shallow buried target discrimination algorithm is proposed. Furthermore, the evidence framework is applied to the problem of the Gaussian kernel FHS-SVM hyperparameters optimization, which can reduce the total misclassification risk of detection result and improve the discrimination performance for both metallic landmines and unexploded ordnances in varying detection environments. Based on the FHS-SVM, the hidden Markov model (HMM) kernel, which describes the multi-aspect characteristic of unexploded ordnance, is used to replace the Gaussian kernel to improve the FHS-SVM discrimination performance for unexploded ordnances further.