An ultrasonic positioning algorithm based on maximum correntropy criterion extended Kalman filter weighted centroid

2018 ◽  
Vol 12 (6) ◽  
pp. 1207-1215 ◽  
Author(s):  
Fuqiang Ma ◽  
Fangjie Liu ◽  
Xiaotong Zhang ◽  
Peng Wang ◽  
Hongying Bai ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Maximum Correntropy Criterion ◽  
Positioning Algorithm

scholarly journals Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Xin Li ◽  
Yan Wang ◽  
Dawei Liu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Particle Filters ◽  
Range Data ◽  
Kinematic Equation ◽  
Positioning Method ◽  
Nonlinear Condition ◽  
Positioning Algorithm ◽  
Range Error

As UWB high-precision positioning in NLOS environment has become one of the hot topics in the research of indoor positioning, this paper firstly presents a method for the smoothing of original range data based on the Kalman filter by the analysis of the range error caused by UWB signals in LOS and NLOS environment. Then, it studies a UWB and foot-mounted IMU fusion positioning method with the integration of particle filter with extended Kalman filter. This method adopts EKF algorithm in the kinematic equation of particle filters algorithm to calculate the position of each particle, which is like the way of running N (number of particles) extended Kalman filters, and overcomes the disadvantages of the inconformity between kinematic equation and observation equation as well as the problem of sample degeneration under the nonlinear condition of the standard particle filters algorithm. The comparison with the foot-mounted IMU positioning algorithm, the optimization-based UWB positioning algorithm, the particle filter-based UWB positioning algorithm, and the particle filter-based IMU/UWB fusion positioning algorithm shows that our algorithm works very well in LOS and NLOS environment. Especially in an NLOS environment, our algorithm can better use the foot-mounted IMU positioning trajectory maintained by every particle to weaken the influence of range error caused by signal blockage. It outperforms the other four algorithms described as above in terms of the average and maximum positioning error.

scholarly journals UWB and MEMS IMU Integrated Positioning Algorithm for a Work-Tool Tracking System

2021 ◽  
Vol 11 (19) ◽  
pp. 8826
Author(s):  
Seong-Geun Kwon ◽  
Oh-Jun Kwon ◽  
Ki-Ryong Kwon ◽  
Suk-Hwan Lee
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Tracking System ◽  
Mobile Node ◽  
Automotive Production ◽  
Space Matrix ◽  
Positioning Algorithm ◽  
Mems Imu ◽  
Combined Sensor ◽  
Coordinate Data

In this paper, we address a system that can accurately locate and monitor work tools in a complex assembly process, such as automotive production. Our positioning monitoring system is positioned by a combined sensor of the UWB module and the MEMS IMU (inertial measuring unit) sensor based on the extended Kalman filter. The MEMS IMU sensor provides the positioning calibration information. The proposed method incorporates IMU and UWB positioning to compensate for errors that can only occur in UWB positioning through the extended Kalman filter (EKT). This EKT is improved by the error dynamic equation derived from the sparse state-space matrix. Also, the proposed method computes the transmission time and distance between the tag and anchor of the UWB module by the TWR (two-way range) system. The tag of a mobile node, which is attached to a moving tool, measures the position of the work tool and transmits the position coordinate data to the anchor. Here, the proposed method uses the trilateration localization method by the confidence distance compensation to prevent the distance error by obstacles and changes in the indoor environment. Experimental results verified that the proposed method confirms whether a specific tool is accurately used according to the prescribed regulations and has more positioning accuracy than the conventional methods.

Author response for "Improved speed and load torque estimations with adaptive fading extended Kalman filter"

2020 ◽  
Author(s):  
Emrah Zerdali ◽  
Recep Yildiz ◽  
Remzi Inan ◽  
Ridvan Demir ◽  
Murat Barut
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Author Response ◽  
Load Torque
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