Influence of B1 code correlation loop for vector tracking structures under complicated environment

The code tracking loop is a key component for user positioning. The pseudorange information of Bei Dou B1 signals has been fused and changed for vector tracking, so a correlation output model for complex scenarios is designed to prevent the propagation of error and valuate the signal performance. The relevant software and hardware factors that affect the output are analyzed.A single channel time-division multiplexing(TDM) method for multicorrelation data extraction is proposed. Statistical characteristics of the correlation output data for both vector and scalar structures are evaluated. Simulation results show that correlation outputs for both structures follow normal or Chi-squared distributions in normal conditions, and the Gamma distribution in harsh conditions. It is shown that a tracking model based on the multi-channel fusion hardly changes the probability distribution of the correlation output in the normal case, but it reduces the ranging error of the code loop, and hence the tracking ability of the code loop for weak signals is improved. Furthermore, vector tracking changes the pseudorange characteristics of channels anytime, and affects the mutual correlation outputs of the code loops in the abnormal case. This study provides a basis for the subsequent design of autonomous integrity algorithms for vector tracking.

AltBOC navigation signal quality assessment and measurement

In order to ensure that Chinese BeiDou satellite navigation system runs smoothly,the assessment of signal quality has become a significant task.Alternative binary offset carrier(AltBOC)is BeiDou B2 frequency signal.The acquisition of BeiDou signal is processed in off-line mode and the evaluation is performed by taking signal power spectrum,eye diagram,constellation,correlation,loss and s-curve deviation on AltBOC as signal quality evaluation parameters.The results illustrate that the new system signal,namely AltBOC signal,has the best performance in code tracking precision,anti-jamming and anti-multipath.

A Rapid Estimation Method for Post-earthquake Building Losses

Rapid estimation of post-earthquake building damage and loss is very important in urgent response efforts.The current approach leaves much room for improvement in estimating ground motion and correctly incorporating the uncertainty and spatial correlation of the loss.This study proposed a new approach for rapidly estimating post-earthquake building loss with reasonable accuracy.The proposed method interpolates ground motion based on the observed ground motion using the Ground Motion Prediction Equation(GMPE)as the weight.It samples the building seismic loss quantile considering the spatial loss correlation that is expressed by Gaussian copula,and kriging is applied to reduce the dimension of direct sampling for estimation speed.The proposed approach was validated using three historical earthquake events in Japan with actual loss reports,and was then applied to predict the building loss amount for the March 2022 Fukushima Mw7.3 earthquake.The proposed method has high potential in future emergency efforts such as search,rescue,and evacuation planning.

Fault Diagnosis for Rolling Element Bearing in Dataset Bias Scenario

Recently, data-driven methods, especially deep learning, outperform other methods for rolling elementbearing (REB) fault diagnosis. Nevertheless, most research work assumes that REB dataset is unbiased. Inthe real industry applications, the dataset bias exists with REB owing to varying REB working conditions andnoise interference. Recently proposed adversarial discriminative domain adaptation (ADDA) is an increasinglypopular incarnation to solve dataset bias problem. However, it mainly devotes to realizing domain alignments, andignores class-level alignments;it can cause degradation of classification performance. In this study, we proposea new REB fault diagnosis model based on improved ADDA to address dataset bias. The proposed diagnosismodel realizes domain- and class-level alignments in dataset bias scenario;it consists of two feature extractors,a domain discriminator, and two label classifiers. The feature extractors and domain discriminator are trainedin an adversarial manner to minimize the domain difference in feature extractors. The domain discrepancy inlabel classifier is reduced by minimizing correlation alignment (CORAL) loss. We evaluate the proposed model onthe Case Western Reserve University (CWRU) bearing dataset and Paderborn University bearing dataset. Theproposed method yields better results than other methods and has good prospects for industrial applications.