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An approach based on orthogonal locality preserving projection (OLPP) and support vector data description (SVDD) is proposed for the performance degradation assessment of rolling bearings. Firstly, the extracted features are selected according to the correlation with root mean square of vibration signals. Secondly, dimension reduction is performed on the selected features with OLPP and principal components are obtained. In the following, the performance degradation indicator (PDI) is constructed with the principal components fused by SVDD. Finally, the proposed approach is verified with the lifecycle data of two rolling bearings. The results show that the PDI has good monotonicity and is sensitive to the initial fault. Besides, the noise in the PDI could be efficiently suppressed.

This letter focuses on a modified complex-valued Markov random field (CMRF) modeling filter to improve the performance in the filtering of the synthetic aperture radar interferometric phase. In the reference CMRF modeling phase map filter, the CMRF model was employed to update residues and their neighbors. By this, the residues were reduced, and phase jumps were preserved simultaneously. However, residue reduction was still insufficient. Furthermore, incorrect model parameter estimation leads to wrong filtering in some blocks. To solve the two aforementioned problems, we propose a modified CMRF modeling filter, where the original interferogram is divided into overlapped blocks. In addition, the adaptive weighted neighbor values are used to estimate the CMRF model parameters. Both simulated and real data experiments are performed to validate this method.

In theory, land subsidence measurement results with high accuracy can be obtained by using the Differential Interferometry Synthetic Aperture Radar (D-InSAR) at X-band. In practice, however, the measuring accuracy of D-InSAR at X-band has been seriously affected by some factors, e.g., decorrelation and high deformation gradient. In this work, the monitoring capability of D-InSAR for coal-mining subsidence is evaluated by using SAR data acquired by TerrraSAR-X system. The SAR image registration method for low coherence image pairs, the denoising phase filter for high noise level interferogram and atmospheric effects mitigation method are the key technical aspects which directly influence the measurement results of D-InSAR at X-band. Thus, a robust image registration method, an improved phase filter method and an atmospheric effects mitigation method are proposed in this paper. The proposed image registration method successfully achieves InSAR coregistration, while the amplitude cross-correlation cannot properly coregister low coherence SAR image pairs. Moreover, the time complexity of the proposed image registration method is obviously slighter than that of the Singular Value Decomposition (SVD) method. The comparing experiment results and the unwrapping phase results show that the improved Goldstein filter is more effective than the original Goldstein filter in noise elimination. The atmospheric influence correction experiment results show that the land subsidence areas with atmospheric influence correction are more clarified than that of without atmospheric influence correction. In summary, the presented methods directly improved the measurement results of D-InSAR at X-band.

This paper presents a closed-form robust phase correlation based algorithm for performing image registration to subpixel accuracy. The subpixel translational shift information is directly obtained from the phase of the normalized cross power spectrum by using Maximum Likelihood Estimation (MLE). The proposed algorithm also has slighter time complexity. Experimental results show that the proposed algorithm yields superior registration precision on the Cramer-Rao Bound (CRB) in the presence of aliasing and noise.

In this paper, an adaptive patch-based filter for Synthetic aperture Radar (SAR) Interferometric phase in the Fourier transform complex domain is proposed. The proposed filter fully utilizes the properties of locally planar terrain phase noise model so that the noise can be suppressed according to the local level of noise. Moreover, the denoising performance of the proposed filter is further improved by an adaptive selection strategy for patch size. The experimental results show that the proposed method delivers state-of-the-art denoising performance in terms of objective criteria.

In this letter, an approach based on spectral diversity and conventional Interferometric Synthetic Aperture Radar (InSAR) for Digital Elevation Model (DEM) reconstruction is proposed. Conventional InSAR technique often fails to correctly reconstruct the DEM due to the presence of discontinuities and/or interferometric noise. With the assistance of the spectral diversity method, the ill-posed phase-unwrapping can be regularized. Thus the capabilities of the conventional InSAR technique for DEM reconstruction can be notably improved. Comparing to the multi-channel InSAR technique, the proposed method requires less SAR images. Comparing to the multifrequency technique, the performance of the proposed method depends on the perpendicular or effective baseline and the ratio of the radar's carry frequency to the bandwidth between the central frequencies of the two sub-looks; while the multifrequency technique depends more on the range bandwidth. Thus, the proposed method is more flexible than the multifrequency technique. The effectiveness of the proposed algorithm is validated by experimental results using real data.