Autonomous Orbit Determination System of Navigation Satellite Based on Spaceborne GPS Technology

In recent years, many low-orbit satellites have been widely used in the field of scientific research and national defense in China. In order to meet the demand of high-precision satellite orbit in China’s space, surveying and mapping, and other related fields, navigation satellites are of great significance. The UKF (unscented Kalman filter) method is applied to space targets’ spaceborne GPS autonomous orbit determination. In this paper, the UKF algorithm based on UT transformation is mainly introduced. In view of the situation that the system noise variance matrix is unknown or the dynamic model is not accurate, an adaptive UKF filtering algorithm is proposed. Simulation experiments are carried out with CHAMP satellite GPS data, and the results show that the filtering accuracy and stability are improved, which proves the algorithm’s effectiveness. The experimental results show that the Helmert variance component estimation considering the dynamics model can solve the problem of reasonable weight determination of BDS/GPS observations and effectively weaken the influence of coarse error and improve the accuracy of orbit determination. The accuracy of autonomous orbit determination by spaceborne BDS/GPS is 1.19 m and 2.35 mm/s, respectively.

Intelligent Building Data Fusion Algorithm by Using the Internet of Things Technology

Analyzed in this paper based on the Internet of things technology for intelligent building data, redundancy of data fusion are pointed out, based on the dynamic Kalman filter algorithm of multi-sensor fusion, first using the theory of fuzzy and covariance matching technique to adjust the noise covariance of traditional algorithm, combined with weighted minimum variance matrix under the optimal information fusion algorithm of data fusion, Finally, the simulation results show that this algorithm can effectively reduce the redundancy of intelligent data and make the estimated value of data fusion more close to the actual value.

A SINS/SAR/GPS Fusion Positioning System Based on Sensor Credibility Evaluations

A reliable framework for SINS/SAR/GPS integrated positioning systems is proposed for the case that sensors are in critical environments. Credibility is used to describe the difference between the true error and the initial setting standard deviation. Credibility evaluation methods for inertial measurement unit (IMU), synthetic aperture radar (SAR), and global positioning system (GPS) are presented. In particular, IMU credibility is modeled by noises and constant drifts that are accumulated with time in a strapdown inertial navigation system (SINS). The quality of the SAR image decides the credibility of positioning based on SAR image matching. In addition, a cumulative residual chi-square test is used to evaluate GPS credibility. An extended Kalman filter based on a sensor credibility evaluation is introduced to integrate the measurements. The measurement of a sensor is either discarded when its credibility value is below a threshold or the variance matrix for the estimated state is otherwise adjusted. Simulations show that the final fusion positioning accuracy with credibility evaluation can be improved by 1–2 times compared to that without evaluation. In addition, the derived standard deviation correctly indicates the value of the position error with credibility evaluation. Moreover, the experiments on an unmanned ground vehicle partially verify the proposed evaluation method of GPS and the fusion framework in the actual environment.

Estimating the Variance of Estimator of the Latent Factor Linear Mixed Model Using Supplemented Expectation-Maximization Algorithm

This paper deals with symmetrical data that can be modelled based on Gaussian distribution, such as linear mixed models for longitudinal data. The latent factor linear mixed model (LFLMM) is a method generally used for analysing changes in high-dimensional longitudinal data. It is usual that the model estimates are based on the expectation-maximization (EM) algorithm, but unfortunately, the algorithm does not produce the standard errors of the regression coefficients, which then hampers testing procedures. To fill in the gap, the Supplemented EM (SEM) algorithm for the case of fixed variables is proposed in this paper. The computational aspects of the SEM algorithm have been investigated by means of simulation. We also calculate the variance matrix of beta using the second moment as a benchmark to compare with the asymptotic variance matrix of beta of SEM. Both the second moment and SEM produce symmetrical results, the variance estimates of beta are getting smaller when number of subjects in the simulation increases. In addition, the practical usefulness of this work was illustrated using real data on political attitudes and behaviour in Flanders-Belgium.

Robust SCKF Filtering Method for MINS/GPS In-Motion Alignment

This paper presents a novel multiple strong tracking adaptive square-root cubature Kalman filter (MSTASCKF) based on the frame of the Sage–Husa filter, employing the multi-fading factor which could automatically adjust the Q value according to the rapidly changing noise in the flight process. This filter can estimate the system noise in real-time during the filtering process and adjust the system noise variance matrix Q so that the filtering accuracy is not significantly reduced with the noise. At the same time, the residual error in the filtering process is used as a measure of the filtering effect, and a multiple fading factor is introduced to adjust the posterior error variance matrix in the filtering process, so that the residual error is always orthogonal and the stability of the filtering is maintained. Finally, a vibration test is designed which simulates the random noise of the short-range guided weapon in flight through the shaking table and adds the noise to the present simulation trajectory for semi-physical simulation. The simulation results show that the proposed filter can significantly reduce the attitude estimation error caused by random vibration.

Single-station PPP-RTK: correction latency and ambiguity resolution performance

Single-station PPP-RTK is a special case of PPP-RTK in that corrections are computed, instead of a network, by only one single GNSS receiver. The present contribution aims to develop a framework to generate multi-epoch, single-station corrections, thereby providing PPP-RTK users the capability to time-predict corrections that are subject to time delay or latency. By presenting analytical expressions of the user ambiguity variance matrix, we address how the ambiguity resolution performance is driven by the correction latency and therefore by the uncertainty involved in the time-prediction of single-station PPP-RTK corrections. Supported by numerical results, our analytical study shows that the number of satellites and number of frequencies work in tandem to enable one to increase the correction latency, yet ensuring successful single-receiver ambiguity resolution.

Inference for Iterated GMM Under Misspecification

This paper develops inference methods for the iterated overidentified Generalized Method of Moments (GMM) estimator. We provide conditions for the existence of the iterated estimator and an asymptotic distribution theory, which allows for mild misspecification. Moment misspecification causes bias in conventional GMM variance estimators, which can lead to severely oversized hypothesis tests. We show how to consistently estimate the correct asymptotic variance matrix. Our simulation results show that our methods are properly sized under both correct specification and mild to moderate misspecification. We illustrate the method with an application to the model of Acemoglu, Johnson, Robinson, and Yared (2008).

Identifying The Key Variables for Assessing The Reclamation Success on Early Growth Vegetation in Ex-exploration of Oil and Gas Mining Areas

This paper examines the identification of key indicators that could be used to measure the success of reclamation plants in post-exploration oil and gas mining areas. The main objective of this research was to find key indicators or variables for evaluating the level success of reclamation results in the post-mining of oil and gas area. In this study, 44 environmental variables of the physical, biological, soil, water and air indicators were analyzed from 70 field plots of 6 reclamation and 2 natural forest sites. The analysis methods included (1) cluster analysis using the Agglomerative Hierarchical Clustering method with the Ward's method, and (2) quadratic discriminant analysis. The results of the clustering analysis showed that there were some clusters due to variation of biomass, water, soil and air conditions. The three clusters developed based on water and/or air variables provided high cophenetic correlation (0.80) with low within-cluster (14.5%) and high between-cluster variations (85.5%). Based on the multicollinearity analysis, average vector difference test, variance matrix variance test, unidimensional test of each variable and quadratic discriminant function, this study found that there were 3 key indicators determining variations of the quality of the reclamation plantations within the study sites, namely, biological indicator of biomass volume (Bio_B); soil indicator of P content in the soil (Tnh_P), saturation base of soil (Tnh_Kb), Manganese (Mn) content in the soil (Tnh_Mn), Sulfur content in the soil (Tnh_S), percentage of ash in the soil (Tnh_Ab), percentage of clay in the soil (Tnh_Li), and water indicator of chloride content in the surface water (Air_Cl). The examination on four classes of the reclamation quality showed that the classes were successfully classified having excellent cross-validation error matrix with overall accuracy more than 90%.

Aplicação do Filtro de Kalman Estendido com matrizes pré sintonizadas ao acionamento sensorless de motores de indução

Sensorless control methods stand out as an alternative for cost reduction and maintenance in AC electric drive systems. This work deals with the application of the Extended Kalman Filter (EKF) to the estimation of the speed and position of the rotor aiming at the implementation of the indirect vector control technique in a speed control system for three- phase induction motors. The Kalman lter, despite its mathematical and computational complexity, performs well under variable speed and load conditions as well as convergence times consistent with the usual requirements of high performance systems. The main contributions of this work are the use of a reduced-order EKF (ROEKF) and the co-variance matrix pretuning in order to accelerate the convergence in the velocity and position estimates for futureimplementations in digital signal processors currently accessible.

Work systems analysis of sterile processing: assembly

BackgroundSterile processing departments (SPDs) play a crucial role in surgical safety and efficiency. SPDs clean instruments to remove contaminants (decontamination), inspect and reorganise instruments into their correct trays (assembly), then sterilise and store instruments for future use (sterilisation and storage). However, broken, missing or inappropriately cleaned instruments are a frequent problem for surgical teams. These issues should be identified and corrected during the assembly phase.ObjectiveA work systems analysis, framed within the Systems Engineering Initiative for Patient Safety (SEIPS) model, was used to develop a comprehensive understanding of the assembly stage of reprocessing, identify the range of work challenges and uncover the inter-relationship among system components influencing reliable instrument reprocessing.MethodsThe study was conducted at a 700-bed academic hospital in the Southeastern United States with two reprocessing facilities from October 2017 to October 2018. Fifty-six hours of direct observations, 36 interviews were used to iteratively develop the work systems analysis. This included the process map and task analysis developed to describe the assembly system, the abstraction hierarchy developed to identify the possible performance shaping factors (based on SEIPS) and a variance matrix developed to illustrate the relationship among the tasks, performance shaping factors, failures and outcomes. Operating room (OR) reported tray defect data from July 2016 to December 2017 were analysed to identify the percentage and types of defects across reprocessing phases the most common assembly defects.ResultsThe majority of the 3900 tray defects occurred during the assembly phase; impacting 5% of surgical cases (n=41 799). Missing instruments, which could result in OR delays and increased surgical duration, were the most commonly reported assembly defect (17.6%, n=700). High variability was observed in the reassembling of trays with failures including adding incorrect instruments, omitting instruments and failing to remove damaged instrument. These failures were precipitated by technological shortcomings, production pressures, tray composition, unstandardised instrument nomenclature and inadequate SPD staff training.ConclusionsSupporting patient safety, minimising tray defects and OR delays and improving overall reliability of instrument reprocessing require a well-designed instrument tracking system, standardised nomenclature, effective coordination of reprocessing tasks between SPD and the OR and well-trained sterile processing technicians.