COMPENSATION FOR THE MUTUAL COUPLING EFFECT FOR THE ESPRIT ALGORITHM IN SINGLE SNAPSHOT ARRAY PROCESSING

An effective method is introduced to compensate the effects of mutual coupling for the Estimation of Signal Parameter via Rotational Invariance Techniques (ESPRIT) direction finding algorithm in application of signal snapshot array processing.Changing the covariance matrix into a Teoplitz matrix can achieve high resolution in the Direction Of Arrive (DOA) estimation.How the mutual coupling affects the array antennas has been discussed and a new definition of mutual im- pedance has been used to characterize the mutual coupling effects between the array elements.Based on the new mutual impedance matrix,a practical method is presented to eliminate the effects of mutual coupling for ESPRIT in the single snapshot data processing.The simulation results show that, this new method not only properly reduces the effects of mutual coupling,but also maintains its steady performance even for weak signals.

Fast algorithm for coherent source number estimation

In order to estimate the number of coherent sources, a Hankel matrix with the size of half the number of the received arrays is constructed using snapshot data of observed vectors. And the rank of the Hankel matrix is only related with the number of signal sources, no matter the signals are uncorrelated or coherent. We can get the signal and noise eigenvalues by conducting the singular value decomposition （SVD） to the Hankel matrix, the source number can be obtained by calculating the maximum ratio of each eigenvalue pair. The complexity of the algorithm is reduced greatly as only part of the observed data （single snapshot） is used. The Monte-Carlo simulation results demonstrate the feasibility of the algorithm.

Direction-of-Arrival Method Based on Randomize-Then-Optimize Approach

The direction-of-arrival(DOA)estimation problem can be solved by the methods based on sparse Bayesian learning(SBL).To assure the accuracy,SBL needs massive amounts of snapshots which may lead to a huge computational workload.In order to reduce the snapshot number and computational complexity,a randomize-then-optimize(RTO)algorithm based DOA estimation method is proposed.The“learning”process for updating hyperparameters in SBL can be avoided by using the optimization and Metropolis-Hastings process in the RTO algorithm.To apply the RTO algorithm for a Laplace prior,a prior transformation technique is induced.To demonstrate the effectiveness of the proposed method,several simulations are proceeded,which verifies that the proposed method has better accuracy with 1 snapshot and shorter processing time than conventional compressive sensing(CS)based DOA methods.

Construction and evaluation of in-house methylation-sensitive SNaPshot system and three classification prediction models for identifying the tissue origin of body fluid

The identification of tissue origin of body fluid can provide clues and evidence for criminal case investigations.To establish an efficient method for identifying body fluid in forensic cases,eight novel body fluid-specific DNA methylation markers were selected in this study,and a multiplex single base extension reaction(SNaPshot)system for these markers was constructed for the identification of five common body fluids(venous blood,saliva,menstrual blood,vaginal fluid,and semen).The results indicated that the in-house system showed good species specificity,sensitivity,and ability to identify mixed biological samples.At the same time,an artificial body fluid prediction model and two machine learning prediction models based on the support vector machine(SVM)and random forest(RF)algorithms were constructed using previous research data,and these models were validated using the detection data obtained in this study(n=95).The accuracy of the prediction model based on experience was 95.79%;the prediction accuracy of the SVM prediction model was 100.00%for four kinds of body fluids except saliva(96.84%);and the prediction accuracy of the RF prediction model was 100.00%for all five kinds of body fluids.In conclusion,the in-house SNaPshot system and RF prediction model could achieve accurate tissue origin identification of body fluids.