Polarimetric super-resolution algorithm for radar range imaging via spatial smoothing processing

A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing processing（SSP）.Then the range and polarimetric scattering matrix of the scattering centers are estimated.The impact of different lengths of the smoothing window on the imaging quality is mainly analyzed with different signal-to-noise ratios（SNR）.Simulation and experimental results show that an improved radar super-resolution range profile and more precise estimation can be obtained by adjusting the length of the smoothing window under different SNR conditions.

Segregation between the parietal memory network and the default mode network： effects of spatial smoothing and model order in ICA

Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network （DMN）, its function has been associated with memory processing, and it has been referred to as the parietal memory network （PMN）. Independent component analysis （ICA） is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determi- nation in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.

Spatially smoothed quaternion-Capon beamforming in the presence of coherent interferences

The performance the quaternion-Capon（ Q-Capon） beamformer degraded when suppressing the interferences that are coherent with the signal of interest（ SOI）. To tackle the problem,the spatial smoothing technique is adopted in quaternion domain to decorrelate the interferences by using linearly and uniformly spaced two-component electromagnetic vector-sensors. By averaging several translational invariant subarray quaternion covariance matrices,the quaternion spatial smoothing is performed to prevent the SOI cancellation phenomena caused by the presence of coherent interferences. It is demonstrated that the quaternion spatial smoothing Q-Capon beamformer can suppress the coherent interferences remarkably while the computational cost is lower than the complex domain long vector spatial smoothing counterpart. Theoretical analyses and simulation results validate the efficacy of the spatially smoothed Q-Capon beamformer in terms of coherent interference suppression capability.

The Method of Spatially Smoothed Seismicity Parameters in Consideration of Seismotectonic Background and Its Application to Seismic Hazard Estimation

In this study, the North China seismic region was selected as the study area, and evaluation of seismic hazard using the spatial smoothing seismicity model was performed. Firstly, the study area is divided into grids, and some parameters （e. g. b-value, Mo, Me, azimuth and M-L relationship ） for each seismotectonic model were assigned. Secondly, using elliptical smoothing based on a seismotectonic background model, the statistical earthquake incidence rate in each grid is successively calculated. Lastly, the relevant ground motion attenuation relationship is chosen to assess seismic hazard of general sites. The maps for the distribution of horizontal peak ground acceleration with 10% probability of exceedance in 50 years were obtained by using the seismic hazard analysis method based on grid source. This seismicity model simplifies the methodology of probabilistic seismic hazard analysis, especially appropriate for those places where seismic tectonics is not yet clearly known. This method can provide valuable references for seismic zonation and seismic safety assessment for significant engineering projects.

Parameter estimation of GTD model and RCS extrapolation based on a modified 3D-ESPRIT algorithm

The noise robustness and parameter estimation performance of the classical three-dimensional estimating signal parameter via rotational invariance techniques(3D-ESPRIT)algorithm are poor when the parameters of the geometric theory of the diffraction(GTD)model are estimated at low signal-to-noise ratio(SNR).To solve this problem,a modified 3D-ESPRIT algorithm is proposed.The modified algorithm improves the parameter estimation accuracy by proposing a novel spatial smoothing technique.Firstly,we make cross-correlation of the auto-correlation matrices;then by averaging the cross-correlation matrices of the forward and backward spatial smoothing,we can obtain a novel equivalent spatial smoothing matrix.The formula of the modified algorithm is derived and the performance of this improved method is also analyzed.Then we compare root-meansquare-errors(RMSEs)of different parameters and the locating accuracy obtained by different algorithms.Furthermore,radar cross section(RCS)of radar targets is extrapolated.Simulation results verify the effectiveness and superiority of the modified 3DESPRIT algorithm.

New targets number estimation method under colored noise background

A multiple targets detection method based on spatial smoothing （MTDSS） is proposed to solve the problem of the source number estimation under the colored noise background. The forward and backward smoothing based on auxiliary vectors which are received data on some specific elements is computed. By the spatial smoothing with auxiliary vectors, the correlated signals are decorrelated, and the colored noise is partially alleviated. The correlation matrix formed from the cross correlations between subarray data and auxiliary vectors is computed. By exploring the second-order statistics property of the covariance matrix, a threshold based on Gerschgorin radii of the smoothing correlation matrix is set to estimate the number of sources. Simulations and experimental results validate that MTDSS has an effective performance under the condition of the colored noise background and coherent sources, and MTDSS is robust with the correlated factor of signals and noise.

Inferring seismic hazard in Sichuan-Yunnan region of China based on the modern earthquake catalogue (1980-2019)

Based on the modern earthquake catalogue,the incomplete centroidal voronoi tessellation(ICVT)method was used in this study to estimate the seismic hazard in Sichuan-Yunnan region of China.We calculated spatial distributions of the total seismic hazard and background seismic hazard in this area.The Bayesian delaunay tessellation smoothing method put forward by Ogata was used to calculate the spatial distributions of b-value.The results show that seismic hazards in Sichuan-Yunan region are high,and areas with relatively high hazard values are distributed along the main faults,while seismic hazards in Sichuan basin are relatively low.

Multipath Time Delay Estimation Based on MUSIC Algorithm Under Small Sample Conditions

On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC algorithm is improved.The algorithm combines the idea of spatial smoothing,constructs a new covariance matrix using the covariance information of the measurement data,and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum.Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation.The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.

Application of Markovian models for non-ergodic and non-stationary earthquake times series for the identification of seismic patterns and future projections

The current earthquake forecast algorithms are not free of shortcomings due to inherent limitations.Especially,the requirement of stationarity in the evaluation of earthquake time series as a prerequisite,significantly limits the use of forecast algorithms to areas where stationary data is not available.Another shortcoming of forecast algorithms is the ergodicity assumption,which states that certain characteristics of seismicity are spatially invariant.In this study,a new earthquake forecast approach is introduced for the locations where stationary data are not available.For this purpose,the spatial activity rate density for each spatial unit is evaluated as a parameter of a Markov chain.The temporal pattern is identified by setting the states at certain spatial activity rate densities.By using the transition patterns between the states,1-and 5-year forecasts were computed.The method is suggested as an alternative and complementary to the existing methods by proposing a solution to the issues of ergodicity and stationarity assumptions at the same time.