Research of DV-Hop localization algorithm in indoor environment

2014 ◽  
Author(s):  
Yang Li ◽  
Shu Xiang ◽  
Guang Yang
Keyword(s):  
Indoor Environment ◽  
Localization Algorithm

A Localization Algorithm Research to Eliminate the Indoor NLOS Error

2014 ◽  
Vol 989-994 ◽  
pp. 2232-2236 ◽  
Author(s):  
Jia Zhi Dong ◽  
Yu Wen Wang ◽  
Feng Wei ◽  
Jiang Yu
Keyword(s):  
Indoor Environment ◽  
Arrival Time ◽  
Channel Model ◽  
Indoor Positioning ◽  
Superior Performance ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Software Simulation ◽  
Positioning Algorithm

Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.

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 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.

Egocentric Landmark-Based Indoor Guidance System for the Visually Impaired

2017 ◽  
Vol 8 (3) ◽  
pp. 55-69 ◽  
Author(s):  
Zhuorui Yang ◽  
Aura Ganz
Keyword(s):  
Visually Impaired ◽  
Indoor Environment ◽  
Field Of View ◽  
Guidance System ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Algorithm Performance ◽  
Illumination Changes ◽  
Low Illumination ◽  
Vision Based Localization

In this paper, we introduce an egocentric landmark-based guidance system that enables visually impaired users to interact with indoor environments. The user who wears Google Glasses will capture his surroundings within his field of view. Using this information, we provide the user an accurate landmark-based description of the environment including his relative distance and orientation to each landmark. To achieve this functionality, we developed a near real time accurate vision based localization algorithm. Since the users are visually impaired our algorithm accounts for captured images using Google Glasses that have severe blurriness, motion blurriness, low illumination intensity and crowd obstruction. We tested the algorithm performance in a 12,000 ft2 open indoor environment. When we have mint query images our algorithm obtains mean location accuracy within 5ft., mean orientation accuracy less than 2 degrees and reliability above 88%. After applying deformation effects to the query images such blurriness, motion blurriness and illumination changes, we observe that the reliability is above 75%.

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.

Improved Weighted Centroid Localization with Quadratic Means for Indoor Mobile Robot Tracking

2015 ◽  
Vol 781 ◽  
pp. 441-444
Author(s):  
Jirapat Sangthong ◽  
Kantaphon Torat ◽  
Sathaporn Promwong
Keyword(s):  
Mobile Robot ◽  
Indoor Environment ◽  
Signal Strength ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Indoor Mobile Robot ◽  
Robot Tracking ◽  
Weighted Centroid Localization ◽  
The One ◽  
Range Free

Nowadays, mobile robot tracking is the one of application in wireless sensor networks (WSNs). A one of promising localization algorithm in range-free algorithm is the weighted centroid localization (WCL) algorithm. This work proposed the improved WCL algorithm with quadratic means based on received signal strength (RSS). The ZigBee operated at 2.4 GHz frequency is used to measure in the indoor environment. The final results will be discuss about the comparison of the typically WCL algorithm and the improved WCL algorithm. From the results, the 1-meter-precision of improved WCL is 91.67% and WCL is 83.33%. Moreover, the improved WCL obtained the 16.67% no error of estimated position.

Simultaneous localization and mapping: swarm robot mutual localization and sonar arc bidirectional carving mapping

2010 ◽  
Vol 225 (3) ◽  
pp. 733-744 ◽  
Author(s):  
S Xu ◽  
Z Ji ◽  
D T Pham ◽  
F Yu
Keyword(s):  
Indoor Environment ◽  
Simultaneous Localization And Mapping ◽  
Localization Algorithm ◽  
Mapping Algorithm ◽  
Robot Swarm ◽  
Localization And Mapping ◽  
Global Mapping ◽  
Swarm Robot ◽  
Azimuth Resolution ◽  
Robot Group

This work primarily aims to study robot swarm global mapping in a static indoor environment. Due to the prerequisite estimation of the robots' own poses, it is upgraded to a simultaneous localization and mapping (SLAM) problem. Five techniques are proposed to solve the SLAM problem, including the extended Kalman filter (EKF)-based mutual localization, sonar arc bidirectional carving mapping, grid-oriented correlation, working robot group substitution, and termination rule. The EKF mutual localization algorithm updates the pose estimates of not only the current robot, but also the landmark-functioned robots. The arc-carving mapping algorithm is to increase the azimuth resolution of sonar readings by using their freespace regions to shrink the possible regions. It is further improved in both accuracy and efficiency by the creative ideas of bidirectional carving, grid-orientedly correlated-arc carving, working robot group substitution, and termination rule. Software simulation and hardware experiment have verified the feasibility of the proposed SLAM philosophy when implemented in a typical medium-cluttered office by a team of three robots. Besides the combined effect, individual algorithm components have also been investigated.

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