scholarly journals Accurate Localization Scheme using Lateration in Indoor Environments

2010 ◽  
Vol 17C (3) ◽  
pp. 251-258
Author(s):  
Yu-Jin Lim ◽  
Jae-Sung Park
Keyword(s):  
Indoor Environments ◽  
Accurate Localization ◽  
Localization Scheme

scholarly journals Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Zahid Farid ◽  
Rosdiadee Nordin ◽  
Mahamod Ismail ◽  
Nor Fadzilah Abdullah
Keyword(s):  
Indoor Localization ◽  
Average Distance ◽  
Optimization Technique ◽  
Computational Time ◽  
Hybrid Technique ◽  
Indoor Environments ◽  
Positioning Systems ◽  
Accurate Localization ◽  
Hybrid Positioning ◽  
Localization Scheme

In indoor environments, WiFi (RSS) based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies) in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs). This model exploits machine learning, in particular Artificial Natural Network (ANN) techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA) based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.

Reducing Tracking Error in RFID Real-Time Localization Systems Using Kalman Filters

2014 ◽  
Vol 5 (3) ◽  
pp. 1-24
Author(s):  
Benjamin Sanda ◽  
Ikhlas Abdel-Qader ◽  
Abiola Akanmu
Keyword(s):  
Real Time ◽  
Radio Frequency Identification ◽  
Kalman Filters ◽  
Tracking Error ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Real World Data ◽  
Localization Accuracy ◽  
Accurate Localization ◽  
Localization Systems

The use of Radio Frequency Identification (RFID) has become widespread in industry as a means to quickly and wirelessly identify and track packages and equipment. Now there is a commercial interest in using RFID to provide real-time localization. Efforts to use RFID technology in this way experience localization errors due to noise and multipath effects inherent to these environments. This paper presents the use of both linear Kalman filters and non-linear Unscented Kalman filters to reduce the error rate inherent to real-time RFID localization systems and provide more accurate localization results in indoor environments. A commercial RFID localization system designed for use by the construction industry is used in this work, and a filtering model based on 3rd order motion is developed. The filtering model is tested with real-world data and shown to provide an increase in localization accuracy when applied to both raw time of arrival measurements as well as final localization results.

scholarly journals Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3907 ◽  
Author(s):  
Kwangjae Sung ◽  
Hyung Kyu Lee ◽  
Hwangnam Kim
Keyword(s):  
Particle Filter ◽  
Microelectromechanical Systems ◽  
Computational Cost ◽  
Dead Reckoning ◽  
Computation Time ◽  
Multipath Fading ◽  
Indoor Environments ◽  
Mems Devices ◽  
Proposal Distribution ◽  
Localization Scheme

The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the motion of the user inferred by dead reckoning (DR) approach via Bayes filters have been suggested to accomplish more accurate localization results indoors. Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high positioning accuracy. This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle filter (DSPF) in indoor environments. As a key element of our system, the DSPF algorithm is employed to correct the position of the user by fusing noisy location data gained by the RSS fingerprinting and DR schemes. By estimating the position of the user through the proposal distribution and target distribution obtained from multiple measurements, the DSPF method can offer better localization results compared to the Kalman filtering-based methods, and it can achieve competitive localization accuracy compared with PF while offering higher computational efficiency than PF. Experimental results demonstrate that the DSPF algorithm can achieve accurate and reliable localization with higher efficiency in computational cost compared with PF in indoor environments.

scholarly journals Support Vector Regression for Mobile Target Localization in Indoor Environments

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 358
Author(s):  
Satish R. Jondhale ◽  
Vijay Mohan ◽  
Bharat Bhushan Sharma ◽  
Jaime Lloret ◽  
Shashikant V. Athawale
Keyword(s):  
Support Vector Regression ◽  
Target Location ◽  
Indoor Localization ◽  
Target Localization ◽  
Superior Performance ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Indoor Environments ◽  
Localization Scheme ◽  
Mobile Target

Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.

scholarly journals Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2692 ◽  
Author(s):  
Yujin Chen ◽  
Ruizhi Chen ◽  
Mengyun Liu ◽  
Aoran Xiao ◽  
Dewen Wu ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Pose Estimation ◽  
Indoor Localization ◽  
Location Based Services ◽  
Visual Localization ◽  
Indoor Environments ◽  
Deep Convolutional Neural Networks ◽  
Accurate Localization ◽  
Visual Odometer ◽  
Similar Solutions

Indoor localization is one of the fundamentals of location-based services (LBS) such as seamless indoor and outdoor navigation, location-based precision marketing, spatial cognition of robotics, etc. Visual features take up a dominant part of the information that helps human and robotics understand the environment, and many visual localization systems have been proposed. However, the problem of indoor visual localization has not been well settled due to the tough trade-off of accuracy and cost. To better address this problem, a localization method based on image retrieval is proposed in this paper, which mainly consists of two parts. The first one is CNN-based image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks (DCNNs) from images are utilized to compare the similarity, and the output of this part are the matched images of the target image. The second one is pose estimation phase that computes accurate localization result. Owing to the robust CNN feature extractor, our scheme is applicable to complex indoor environments and easily transplanted to outdoor environments. The pose estimation scheme was inspired by monocular visual odometer, therefore, only RGB images and poses of reference images are needed for accurate image geo-localization. Furthermore, our method attempts to use lightweight datum to present the scene. To evaluate the performance, experiments are conducted, and the result demonstrates that our scheme can efficiently result in high location accuracy as well as orientation estimation. Currently the positioning accuracy and usability enhanced compared with similar solutions. Furthermore, our idea has a good application foreground, because the algorithms of data acquisition and pose estimation are compatible with the current state of data expansion.

scholarly journals Sensitivity maps for time-reverse imaging: an accuracy study for the Los Humeros Geothermal Field (Mexico)

2020 ◽  
Vol 222 (1) ◽  
pp. 231-246
Author(s):  
C Finger ◽  
E H Saenger
Keyword(s):  
A Priori ◽  
Velocity Model ◽  
Source Location ◽  
Geothermal Field ◽  
Location Accuracy ◽  
Accurate Localization ◽  
Localization Scheme ◽  
Priori Information ◽  
Initial Source ◽  
Sensitivity Maps

SUMMARY The estimation of the source–location accuracy of microseismic events in reservoirs is of significant importance. Time-reverse imaging (TRI) provides a highly accurate localization scheme to locate events by time-reversing the recorded full wavefield and back propagating it through a velocity model. So far, the influence of the station geometry and the velocity model on the source–location accuracy is not well known. Therefore, sensitivity maps are developed using the geothermal site of Los Humeros in Mexico to evaluate the spatial variability of the source–location accuracy. Sensitivity maps are created with an assumed gradient velocity model with a constant vp–vs ratio and with a realistic velocity model for the region of Los Humeros. The positions of 27 stations deployed in Los Humeros from September 2017 to September 2018 are used as surface receivers. An automatic localization scheme is proposed that does not rely on any a priori information about the sources and thus negates any user bias in the source locations. The sensitivity maps are created by simulating numerous uniformly distributed sources simultaneously and locating these sources using TRI. The found source locations are compared to the initial source locations to estimate the achieved accuracy. The resulting sensitivity maps show that the station geometry introduces complex patterns in the spatial variation of accuracy. Furthermore, the influence of the station geometry on the source–location accuracy is larger than the influence of the velocity model. Finally, a microearthquake recorded at the geothermal site of Los Humeros is located to demonstrate the usability of the derived sensitivity maps. This study stresses the importance of optimizing station networks to enhance the accuracy when locating seismic events using TRI.

Beacon selection and calibration for the efficient localization of a mobile robot

Robotica ◽  
2013 ◽  
Vol 32 (1) ◽  
pp. 115-131 ◽  
Author(s):  
Jaehyun Park ◽  
Jangmyung Lee
Keyword(s):  
Mobile Robot ◽  
Indoor Localization ◽  
Setup Time ◽  
Ultrasonic Waves ◽  
Mobile Robot Navigation ◽  
Indoor Environments ◽  
Ultrasonic Sensors ◽  
Indoor Space ◽  
Localization Scheme ◽  
Set Up

SUMMARYThis paper proposes a localization scheme using ultrasonic beacons in an unstructured multi-block workspace. Indoor localization schemes using ultrasonic sensors have widely been studied due to their low costs and high accuracies. However, ultrasonic sensors are susceptible to environmental noise due to the propagation characteristics of ultrasonic waves. In addition, the decay of ultrasonic signals over long distances implies that ultrasonic sensors are unsuitable for use in large indoor environments. To overcome these shortcomings of ultrasonic sensors, while retaining their advantages, a multi-block approach was devised by dividing an indoor space into several blocks with multiple beacons in each block. However, it is difficult to divide an indoor space into several blocks when beacons cannot be installed in a regular manner or when some new beacons are installed. To resolve this difficulty, a dynamic algorithm is needed to divide an indoor space into multiple blocks and to select suitable beacons. Therefore, this paper proposes a real-time localization scheme to estimate the position of a mobile robot independent of beacon locations and to estimate the position of a new beacon installed at an unknown position. A beacon selection algorithm was developed to select optimal beacons according to robot position and to set up sets of beacons for mobile robot navigation. By using the new beacon searching and calibration algorithm, a mobile robot is able to navigate in an unknown space without requiring the additional setup time needed to install new beacons. The performance of the proposed localization system was verified using real experiments.

scholarly journals Indoor Smartphone Localization Based on LOS and NLOS Identification

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3987 ◽  
Author(s):  
Hyeon Jo ◽  
Seungku Kim
Keyword(s):  
Indoor Localization ◽  
Estimation Error ◽  
Distance Estimation ◽  
Location Based Services ◽  
Propagation Path ◽  
Line Of Sight ◽  
Arrival Angle ◽  
Accurate Localization ◽  
Communication Signal ◽  
Localization Scheme

Accurate localization technology is essential for providing location-based services. Global positioning system (GPS) is a typical localization technology that has been used in various fields. However, various indoor localization techniques are required because GPS signals cannot be received in indoor environments. Typical indoor localization methods use the time of arrival, angle of arrival, or the strength of the wireless communication signal to determine the location. In this paper, we propose an indoor localization scheme using signal strength that can be easily implemented in a smartphone. The proposed algorithm uses a trilateration method to estimate the position of the smartphone. The accuracy of the trilateration method depends on the distance estimation error. We first determine whether the propagation path is line-of-sight (LOS) or non-line-of-sight (NLOS), and distance estimation is performed accordingly. This LOS and NLOS identification method decreases the distance estimation error. The proposed algorithm is implemented as a smartphone application. The experimental results show that distance estimation error is significantly reduced, resulting in accurate localization.

scholarly journals A novel indoor localization scheme based on refined fingerprint-based autoencoder network

2018 ◽  
Vol 189 ◽  
pp. 03017
Author(s):  
Junhui Mei ◽  
Juntong Xi
Keyword(s):  
Indoor Localization ◽  
Low Cost ◽  
Access Point ◽  
Indoor Environments ◽  
Location Based Service ◽  
Accuracy Improvement ◽  
Positioning Systems ◽  
Localization Scheme ◽  
Environmental Properties ◽  
Indoor Scenes

Indoor positioning systems have attracted increasing interests for the emergency of location based service in indoor environments. Wi-Fi fingerprint-based localization scheme has become a promising indoor localization technique due to the availability of access point (AP) and its low cost. However, the received signal strength (RSS) values are easily fluctuated by the interference of multi-path effects, which introduce propagation errors into localization results. In order to address the issue, a fingerprint-based autoencoder network scheme is proposed to learn the essential features from the measured coarse RSS values and extract the trained weight parameters of autoencoder network as refined fingerprints. The extracted fingerprints are able to represent the environmental properties and display strong robustness with fluctuated signals. The proposed scheme is further implemented in complex indoor scenes, which substantiate the effectiveness and accuracy improvement compared with other RSS-based schemes.

Real-Time Indoor Mobile Robot Localization Based on the Recognition of Omni-Directional Artificial Landmarks

2014 ◽  
Author(s):  
Xu Zhong ◽  
Yu Zhou ◽  
Hanyu Liu
Keyword(s):  
Real Time ◽  
Region Of Interest ◽  
Recognition Algorithm ◽  
Image Clustering ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Lighting Condition ◽  
Black And White ◽  
Localization Scheme ◽  
Geometric Mapping

This paper proposes a real-time robust localization scheme for mobile robots in indoor environments, based on the recognition of omnidirectional artificial landmarks captured by a single onboard camera. Considering the need of omnidirectional recognition and the disturbance of lighting condition, we encode the landmark identity with nested circles in black and white. The recognition algorithm consists of a global and a local recognition layers. The global recognition is a fast overall recognition process, including light detection, image clustering, region of interest (ROI) extraction, and ROI identification. If the number of identified ROIs does not meet the requirement of the localization algorithm, the local recognition will process those unidentified ROIs through adaptive ROI expansion and template cutting. Based on the landmark recognition, the absolute position and orientation of the camera in the environment are estimated using the geometric mapping between the image and global frames. The proposed approach is tested via experiments in a real indoor environment, and the result reveals high localization robustness and consistency to the lighting condition.

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