scholarly journals Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 6972
Author(s):  
Harun Jamil ◽  
Faiza Qayyum ◽  
Faisal Jamil ◽  
Do-Hyeun Kim
Keyword(s):  
Kalman Filter ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Sensor Data ◽  
Compensation Mechanism ◽  
Localization Algorithm ◽  
High Position ◽  
Zero Velocity ◽  
Conflicting Information

This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization algorithm using Bluetooth low energy beacons (BLE) was applied and analyzed in an indoor environment. The experiments conducted in an indoor scenario shows the results of various activities performed by the object and achieves better orientation estimation, zero velocity measurements, and high position accuracy than other methods in the literature.

scholarly journals Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 24595-24614 ◽  
Author(s):  
Guoliang Chen ◽  
Xiaolin Meng ◽  
Yunjia Wang ◽  
Yanzhe Zhang ◽  
Peng Tian ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Kalman Filter Algorithm

scholarly journals Data Fusion of UWB and IMU Based on Unscented Kalman Filter for Indoor Localization of Quadrotor UAV

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 64971-64981 ◽  
Author(s):  
Weide You ◽  
Fanbiao Li ◽  
Liqing Liao ◽  
Meili Huang
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Quadrotor Uav

Fast and Precise Glass Handling Using Visual Servo With Unscented Kalman Filter Dual Estimation

2017 ◽  
Vol 140 (4) ◽  
Author(s):  
Xiaowen Yu ◽  
Thomas Baker ◽  
Yu Zhao ◽  
Masayoshi Tomizuka
Keyword(s):  
Kalman Filter ◽  
Manufacturing Industry ◽  
Unscented Kalman Filter ◽  
Industrial Robot ◽  
Frame Rate ◽  
Sensor Data ◽  
Visual Servo ◽  
Dual Estimation ◽  
Low Frame Rate ◽  
Glass Piece

In the protective glass manufacturing industry for cell phones, placing glass pieces into the slots of the grinder requires submillimeter accuracy which only can be achieved by human workers, leading to a bottle neck in the production line. To address such issue, industrial robot equipped with vision sensors is proposed to support human workers. The high placing performance is achieved by a two step approach. In the first step, an eye-to-hand camera is installed to detect the glass piece and slot with robust vision, which can put the glass piece close to the slot and ensures a primary precision. In the second step, a closed-loop controller based on visual servo is adopted to guide the glass piece into the slot with dual eye-in-hand cameras. However, vision sensor suffers from a very low frame rate and slow image processing speed resulting in a very slow placing performance. In addition, the placing performance is substantially limited by the system parameter uncertainty. To compensate for these limitations, a dual-rate unscented Kalman filter (UKF) with dual-estimation is adopted for sensor data filtering and online parameter identification without requiring any linear parameterization of the model. Experimental results are presented to confirm the effectiveness of the proposed approach.

scholarly journals DEVELOPMENT OF LOCATION ESTIMATION ALGORITHM UTILIZING RSSI FOR LORA POSITIONING SYSTEM

2021 ◽  
Vol 84 (1) ◽  
pp. 97-105
Author(s):  
S. Kavetha ◽  
A. S. Ja'afar ◽  
M. Z. A. Aziz ◽  
A. A. M. Isa ◽  
M. S. Johal ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Low Power ◽  
Indoor Environment ◽  
Multipath Fading ◽  
Location Estimation ◽  
Line Of Sight ◽  
Area Network ◽  
Positioning System ◽  
Localization Algorithm ◽  
Wide Area Network

LoRa is identified as Long-Range low power network technology for Low Power Wide Area Network (LPWAN) usage. Nowadays, Global Positioning System (GPS) is an important system which is used for location and navigation predominantly used in outdoor but less accurate in indoor environment. Most of LoRa technology have been used on the internet-of-things (ioT) but very few use it as localization system. In this project, a GPS-less solution is proposed where LoRa Positioning System was developed which consists of LoRa transmitter, LoRa transceiver and LoRa receiver. The system has been developed by collecting the RSSI which is then used for the distance estimation. Next, Kalman filter with certain model has been implemented to overcome the effect of multipath fading especially for indoor environment and the trilateration technique is applied to estimate the location of the user. Both distribution estimation results for Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) condition were analyzed. Then, the comparison RMSE achievement is analyzed between the trilateration and with the Kalman Filter. GPS position also were collected as comparison to the LoRa based positioning. Lastly, the Cumulative Density Function (CDF) shows 90% of the localization algorithm error for LOS is lower than 0.82 meters while for NLOS is 1.17 meters.

RFID Indoor Localization Algorithm Based on PSO

2012 ◽  
Vol 241-244 ◽  
pp. 972-975 ◽  
Author(s):  
Pei Zhi Wen ◽  
Ting Ting Su ◽  
Li Fang Li
Keyword(s):  
Indoor Environment ◽  
Lower Cost ◽  
Indoor Localization ◽  
Main Idea ◽  
Pso Algorithm ◽  
Position Estimation ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Smoothing Filter ◽  
The Impact

In order to improve the positioning accuracy and reduce the localization cost, a kind of PSO-based RFID indoor localization algorithm is proposed in this paper. The main idea of this algorithm contains the following two aspects. First, due to the influence of none line of sight and multipath transmission in indoor environment, we adopt Gaussian Smoothing Filter to process Received Signal Strength Indicator (RSSI) values, which can reduce the impact of environmental factors on the position estimation effectively. Second, Particle of Swarm Optimization (PSO) algorithm is introduced to obtain a better positioning result. By experimenting in different indoor environment, the results demonstrate that the proposed approach can not only improve the precision of indoor localization, but has a lower cost and better robustness when compared to VIRE approach.

scholarly journals A Robust Indoor Mobile Localization Algorithm for Wireless Sensor Network in Mixed LOS/NLOS Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ou Yong Kang ◽  
Cheng Long
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Unscented Kalman Filter ◽  
Wireless Sensor ◽  
Selection Strategy ◽  
Time Of Arrival ◽  
Multiple Model ◽  
Localization Algorithm ◽  
Extend Kalman Filter

Wireless sensor network (WSN) is a self-organizing network which is composed of a large number of cheap microsensor nodes deployed in the monitoring area and formed by wireless communication. Since it has the characteristics of rapid deployment and strong resistance to destruction, the WSN positioning technology has a wide application prospect. In WSN positioning, the nonline of sight (NLOS) is a very common phenomenon affecting accuracy. In this paper, we propose a NLOS correction method algorithm base on the time of arrival (TOA) to solve the NLOS problem. We firstly propose a tendency amendment algorithm in order to correct the NLOS error in geometry. Secondly, this paper propose a particle selection strategy to select the standard deviation of the particle swarm as the basis of evolution and combine the genetic evolution algorithm, the particle filter algorithm, and the unscented Kalman filter (UKF) algorithm. At the same time, we apply orthogon theory to the UKF to make it have the ability to deal with the target trajectory mutation. Finally we use maximum likelihood localization (ML) to determine the position of the mobile node (MN). The simulation and experimental results show that the proposed algorithm can perform better than the extend Kalman filter (EKF), Kalman filter (KF), and robust interactive multiple model (RIMM).

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