scholarly journals Study on the Improved Unscented Kalman Filter Ultra-Wideband Indoor Location Algorithm based on Two-Way Time-of-Flight

2018 ◽  
Vol 11 (5) ◽  
pp. 93-99
Author(s):  
Feng Tian ◽  
◽  
Hanqing Li ◽  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Time Of Flight ◽  
Ultra Wideband ◽  
Indoor Location ◽  
Location Algorithm

Ultrasonic Time-of-Flight Estimation Through Unscented Kalman Filter

2006 ◽  
Vol 55 (4) ◽  
pp. 1077-1084 ◽  
Author(s):  
L. Angrisani ◽  
A. Baccigalupi ◽  
R. Schiano Lo Moriello
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Time Of Flight ◽  
Ultrasonic Time

scholarly journals An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

2019 ◽  
Vol 11 (22) ◽  
pp. 2628 ◽  
Author(s):  
Liu ◽  
Li ◽  
Wang ◽  
Zhang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Inertial Measurement Unit ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Indoor Location ◽  
Magnetometer Data ◽  
Noise Covariance ◽  
Mems Imu ◽  
Non Line Of Sight

High precision positioning of UWB (ultra-wideband) in NLOS (non-line-of-sight) environment is one of the hot issues in the direction of indoor positioning. In this paper, a method of using a complementary Kalman filter (CKF) to fuse and filter UWB and IMU (inertial measurement unit) data and track the errors of variables such as position, speed, and direction is presented. Based on the uncertainty of magnetometer and acceleration, the noise covariance matrix of magnetometer and accelerometer is calculated dynamically, and then the weight of magnetometer data is set adaptively to correct the directional error of gyroscope. Based on the uncertainty of UWB distance observations, the covariance matrix of UWB measurement noise is calculated dynamically, and then the weight of UWB data observations is set adaptively to correct the position error. The position, velocity and direction errors are corrected by the fusion of UWB and IMU. The experimental results show that the algorithm can reduce the gyroscope deviation with magnetic noise and motion noise, so that the orientation estimates can be improved, as well as the positioning accuracy can be increased with UWB ranging noise.

Indoor Location Algorithm Based on Kalman Filter and Multi-Source Data Integration

2016 ◽  
Vol 9 (4) ◽  
pp. 45-54
Author(s):  
Zhang Ya-qiong ◽  
Li Zhao-xing ◽  
Li Xin ◽  
Lv Zhihan-han
Keyword(s):  
Kalman Filter ◽  
Data Integration ◽  
Indoor Location ◽  
Source Data ◽  
Location Algorithm

A Robust Unscented Kalman Filter applied to Ultra-wideband Positioning

2020 ◽  
Vol 11 (4) ◽  
pp. 308-330
Author(s):  
Chuanyang Wang ◽  
Yipeng Ning ◽  
Xin Li ◽  
Haobo Li
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Ultra Wideband

Indoor Location Algorithm Based on Kalman Filter

2016 ◽  
Author(s):  
Ya-qiong Zhang ◽  
Zhao-xing Li ◽  
Xin Li ◽  
Zhihan-han Lv
Keyword(s):  
Kalman Filter ◽  
Indoor Location ◽  
Location Algorithm

scholarly journals Indoor Tracking by Adding IMU and UWB using Unscented Kalman Filter

2021 ◽  
Author(s):  
Venkata Krishnaveni B ◽  
Suresh Reddy K ◽  
Ramana Reddy P
Keyword(s):  
Kalman Filter ◽  
High Precision ◽  
Unscented Kalman Filter ◽  
Tracking System ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Smart Manufacturing ◽  
Indoor Tracking ◽  
Indoor Conditions ◽  
Localization And Tracking

Abstract In recent days Internet of Things (IoT) applications becoming prominent, like smart home, connected health, smart farming, smart retail and smart manufacturing, will lead to a challenging task in providing low cost, high precision localization and tracking in indoor environments. Positioning in indoor is yet an open issue mostly because of not receiving the signals of GPS in the context of indoor. Inertial Measurement Unit (IMU) can give an exact indoor tracking, however, they regularly experience the cumulated error as the speed and position are gotten by incorporating the increasing acceleration constantly as for time. At the same time Ultra Wideband (UWB) localization and tracking will be influenced by the real time indoor conditions. It is difficult to utilize an independent localization and tracking system to accomplish high precision in indoor conditions. In this paper, we come up with an incorporated positioning system in indoor by joining IMU and the UWB over the Unscented Kalman Filter (UKF) and the Extended Kalman Filter (EKF) to enhance the precision. All these algorithms are analyzed and assessed dependent on their exhibition.

scholarly journals A Clustering-Based Approach for Improving the Accuracy of UWB Sensor-Based Indoor Positioning System

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Taner Arsan ◽  
Mohammed Muwafaq Noori Hameez
Keyword(s):  
Kalman Filter ◽  
Mean Shift ◽  
Indoor Positioning ◽  
Optimal Number ◽  
Ultra Wideband ◽  
Positioning System ◽  
Indoor Location ◽  
Test Points ◽  
The Impact ◽  
Indoor Positioning System

There are several methods which can be used to locate an object or people in an indoor location. Ultra-wideband (UWB) is a specifically promising indoor positioning technology because of its high accuracy, resistance to interference, and better penetration. This study aims to improve the accuracy of the UWB sensor-based indoor positioning system. To achieve that, the proposed system is trained by using the K-means algorithm with an additional average silhouette method. This helps us to define the optimal number of clusters to be used by the K-means algorithm based on the value of the silhouette coefficient. Fuzzy c-means and mean shift algorithms are added for comparison purposes. This paper also introduces the impact of the Kalman filter while using the measured UWB test points as an input for the Kalman filter in order to obtain a better estimation of the position. As a result, the average localization error is reduced by 43.26% (from 16.3442 cm to 9.2745 cm) when combining the K-means algorithm with the Kalman filter in which the Kalman-filtered UWB-measured test points are used as an input for the proposed system.

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