An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

Polar motion prediction using the combination of SSA and ARMA

High-precision polar motion(PM) prediction is of important significance in astronomy, geodesy, aviation,hydrographic mapping, interstellar navigation, and so on. SSA can effectively extract the trend and period terms of PM,in the process of achieving high-precision medium-and long-term polar motion prediction,it is necessary to solve the end effect problem and overfitting problem of SSA forecasting method;therefore, ARMA was applied to decreasethe end effect, and a suitable combination of reconstructed components was determined to avoid the high variance reaction of SSA overfitting. Based on the decomposition and reconstruction of the PM by SSA, the reconstructed components are determined to participate in the SSA iterative fitting model according to the variance contribution rate. The combination of the reconstructed components representing the polar motion period term and the trend term is determined according to the correlation analysis of the selected reconstructed components. After the above work, the principal component prediction sequence is obtained by fitting the period term and the trend term to convergence, respectively, and then, the SSA end effect is modified, and the residual term is predicted based on ARMA. The test results show that he prediction accuracy of SSA + ARMA at the front of the X and Y directions are improved by 96.90% and 97.53% compared with those of SSA, respectively,and the forecast accuracy of 365 days are improved by 37.93% and 19.53% in the X and Y directions compared with those of Bulletin A, respectively.

六自由度轮式并联机器人及其构型方法

车轮是人类历史上最伟大的发明之一,在交通运输领域得到了广泛的研究与应用。将车轮这种特殊的运动副引入到传统并联机器人构型设计中,可以有效拓展并联机器人的工作空间。基于全方位轮的关联矩阵描述,将含车轮的串联支链构型综合问题转化为含车轮的关联矩阵求解问题,综合出两类含车轮的无约束串联支链,并分析了两类串联支链的受力稳定性。提出了两种六自由度轮式并联机器人新构型,分析了基于全向轮的轮式并联机器人的自由度属性,并成功研制出了一台实验样机。六自由度轮式并联机器人融合了移动机器人和传统并联机器人的优势,不仅具有移动效率高、移动范围广的优点,且具备在局部小范围内进行高精度六自由度操作的能力,可广泛应用于大型精密设备制造过程中的加工、运输、调姿和装配等工业操作。