scholarly journals An Indoor Robust Localization Algorithm Based on Data Association Technique

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6598
Author(s):  
Long Cheng ◽  
Yong Wang ◽  
Mingkun Xue ◽  
Yangyang Bi
Keyword(s):  
Hypothesis Testing ◽  
Indoor Localization ◽  
Likelihood Estimation ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Position Information ◽  
Position Estimate

As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms.

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 Indoor Localization System Based on Bluetooth Low Energy for Museum Applications

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1055 ◽  
Author(s):  
Romeo Giuliano ◽  
Gian Carlo Cardarilli ◽  
Carlo Cesarini ◽  
Luca Di Nunzio ◽  
Francesca Fallucchi ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Position Estimation ◽  
Low Energy ◽  
Least Square ◽  
Feed Forward Neural Network ◽  
Position Information ◽  
Bluetooth Low Energy ◽  
Performance Results ◽  
Linear Least Square ◽  
Position Estimate

In the last few years, indoor localization has attracted researchers and commercial developers. Indeed, the availability of systems, techniques and algorithms for localization allows the improvement of existing communication applications and services by adding position information. Some examples can be found in the managing of people and/or robots for internal logistics in very large warehouses (e.g., Amazon warehouses, etc.). In this paper, we study and develop a system allowing the accurate indoor localization of people visiting a museum or any other cultural institution. We assume visitors are equipped with a Bluetooth Low Energy (BLE) device (commonly found in modern smartphones or in a small chipset), periodically transmitting packets, which are received by geolocalized BLE receivers inside the museum area. Collected packets are provided to the locator server to estimate the positions of the visitors inside the museum. The position estimation is based on a feed-forward neural network trained by a measurement campaign in the considered environment and on a non-linear least square algorithm. We also provide a strategy for deploying the BLE receivers in a given area. The performance results obtained from measurements show an achievable position estimate accuracy below 1 m.

scholarly journals Indoor Localization Based on VIO System and Three-Dimensional Map Matching

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2790 ◽  
Author(s):  
Jitong Zhang ◽  
Mingrong Ren ◽  
Pu Wang ◽  
Juan Meng ◽  
Yuman Mu
Keyword(s):  
Random Field ◽  
Indoor Localization ◽  
Conditional Random Field ◽  
A Priori ◽  
Three Dimensional ◽  
Map Matching ◽  
Localization Algorithm ◽  
Position Information ◽  
Feedback Information ◽  
Poor Lighting

High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we propose an indoor localization algorithm based on VIO system and three-dimensional (3D) map matching. The 3D map matching is to add height matching on the basis of previous two-dimensional (2D) matching so that the algorithm has more universal applicability. Firstly, the conditional random field model is established. Secondly, an indoor three-dimensional digital map is used as a priori information. Thirdly, the pose and position information output by the VIO system are used as the observation information of the conditional random field (CRF). Finally, the optimal states sequence is obtained and employed as the feedback information to correct the trajectory of VIO system. Experimental results show that our algorithm can effectively improve the positioning accuracy of VIO system in the indoor area of poor lighting and featureless.

scholarly journals Mobile Robot Indoor Positioning System Based on K-ELM

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Haixia Wang ◽  
Junliang Li ◽  
Wei Cui ◽  
Xiao Lu ◽  
Zhiguo Zhang ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Positioning Algorithm ◽  
Indoor Positioning System

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

scholarly journals A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

2020 ◽  
Vol 10 (3) ◽  
pp. 956 ◽  
Author(s):  
Minghao Si ◽  
Yunjia Wang ◽  
Shenglei Xu ◽  
Meng Sun ◽  
Hongji Cao
Keyword(s):  
Numerical Experiments ◽  
New Technologies ◽  
Gaussian Model ◽  
Indoor Positioning ◽  
Least Square ◽  
Line Of Sight ◽  
Positioning Accuracy ◽  
Timing Measurement ◽  
Positioning Method ◽  
Non Line Of Sight

In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

scholarly journals THE DESIGN AND IMPLEMENTATION OF INDOOR LOCALIZATION SYSTEM USING MAGNETIC FIELD BASED ON SMARTPHONE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 379-383
Author(s):  
J. Liu ◽  
C. Jiang ◽  
Z. Shi
Keyword(s):  
Magnetic Field ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Localization Algorithm ◽  
External Device ◽  
Localization System ◽  
Filtering Algorithm ◽  
Coordinate Conversion ◽  
Indoor Positioning System

Sufficient signal nodes are mostly required to implement indoor localization in mainstream research. Magnetic field take advantage of high precision, stable and reliability, and the reception of magnetic field signals is reliable and uncomplicated, it could be realized by geomagnetic sensor on smartphone, without external device. After the study of indoor positioning technologies, choose the geomagnetic field data as fingerprints to design an indoor localization system based on smartphone. A localization algorithm that appropriate geomagnetic matching is designed, and present filtering algorithm and algorithm for coordinate conversion. With the implement of plot geomagnetic fingerprints, the indoor positioning of smartphone without depending on external devices can be achieved. Finally, an indoor positioning system which is based on Android platform is successfully designed, through the experiments, proved the capability and effectiveness of indoor localization algorithm.

scholarly journals A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 193 ◽  
Author(s):  
Haibin Tong ◽  
Ning Xin ◽  
Xianli Su ◽  
Tengfeng Chen ◽  
Jingjing Wu
Keyword(s):  
High Precision ◽  
Indoor Localization ◽  
Poor Performance ◽  
Harsh Environments ◽  
Line Of Sight ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Advantages And Disadvantages ◽  
Hybrid Localization ◽  
Localization Approach

Wireless sensor networks (WSNs) and the Internet of Things (IoT) have been widely used in industrial, construction, and other fields. In recent years, demands for pedestrian localization have been increasing rapidly. In most cases, these applications work in harsh indoor environments, which have posed many challenges in achieving high-precision localization. Ultra-wide band (UWB)-based localization systems and pedestrian dead reckoning (PDR) algorithms are popular. However, both have their own advantages and disadvantages, and both exhibit a poor performance in harsh environments. UWB-based localization algorithms can be seriously interfered by non-line-of-sight (NLoS) propagation, and PDR algorithms display a cumulative error. For ensuring the accuracy of indoor localization in harsh environments, a hybrid localization approach is proposed in this paper. Firstly, UWB signals cannot penetrate obstacles in most cases, and traditional algorithms for improving the accuracy by NLoS identification and mitigation cannot work in this situation. Therefore, in this study, we focus on integrating a PDR and UWB-based localization algorithm according to the UWB communication status. Secondly, we propose an adaptive PDR algorithm. UWB technology can provide high-precision location results in line-of-sight (LoS) propagation. Based on these, we can train the parameters of the PDR algorithm for every pedestrian, to improve the accuracy. Finally, we implement this hybrid localization approach in a hardware platform and experiment with it in an environment similar to industry or construction. The experimental results show a better accuracy than traditional UWB and PDR approaches in harsh environments.

Modelling the Effect of Human Body around User on Signal Strength and Accuracy of Indoor Positioning

2020 ◽  
Vol 12 (7) ◽  
Author(s):  
Firdaus Firdaus ◽  
◽  
Noor Azurati Ahmad ◽  
Shamsul Sahibuddin ◽  
Rudzidatul Akmam Dziyauddin ◽  
...  
Keyword(s):  
Human Body ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Line Of Sight ◽  
Minimal Cost ◽  
The People ◽  
Indoor Positioning System ◽  
Presence Of People ◽  
Non Line Of Sight

WLAN indoor positioning system (IPS) has high accurate of position estimation and minimal cost. However, environmental conditions such as the people presence effect (PPE) greatly influence WLAN signal and it will decrease the accuracy. This research modelled the effect of people around user on signal strength and the accuracy. We have modelled the human body around user effects by proposed a general equation of decrease in signal strength as function of position, distance, and number of people. Signal strength decreased from 5 dBm to 1 dBm when people in line of sight (LOS) position, and start from 0.5 dBm to 0.3 dBm when people in non-line of sight (NLOS) position. The system accuracy decreases due to the presence of people. When the system is in NLOS case, the presence of people causes a decrease in accuracy from 33% to 57%. Then the accuracy decrease from 273% to 334% in LOS case.

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 Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints

2020 ◽  
Vol 9 (12) ◽  
pp. 714
Author(s):  
Yankun Wang ◽  
Renzhong Guo ◽  
Weixi Wang ◽  
Xiaoming Li ◽  
Shengjun Tang ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Indoor Positioning ◽  
Kriging Interpolation ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Position Information ◽  
Kriging Interpolation Method ◽  
Additional Equipment ◽  
Positioning Method ◽  
Fingerprint Database

Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint collection. To overcome these drawbacks, we provide a near relation-based indoor positioning method under a sparse Wi-Fi fingerprint. To effectively obtain the fingerprint database, certain interpolation methods are used to enrich sparse Wi-Fi fingerprints. A near relation boundary is provided, and Wi-Fi fingerprints are constrained to this region to reduce fingerprint ambiguity, which can also improve the efficiency of fingerprint matching. Extensive experiments show that the kriging interpolation method performs well, and a positioning accuracy of 2.86 m can be achieved with a near relation under a 1 m interpolation density.

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