scholarly journals Distance Estimation based on 802.11 RTS/CTS Mechanism for Indoor Localization

10.5772/15670 ◽  
2011 ◽  
Author(s):  
Alfonso Bahillo ◽  
Patricia Fernndez ◽  
Javier Prieto ◽  
Santiago Mazuelas ◽  
Rubn M. ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Distance Estimation

Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

2022 ◽  
pp. 123-145
Author(s):  
Pelin Yildirim Taser ◽  
Vahid Khalilpour Akram
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Learning Algorithms ◽  
Distance Estimation ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Traditional Methods ◽  
Learning Techniques ◽  
Localization Systems

The GPS signals are not available inside the buildings; hence, indoor localization systems rely on indoor technologies such as Bluetooth, WiFi, and RFID. These signals are used for estimating the distance between a target and available reference points. By combining the estimated distances, the location of the target nodes is determined. The wide spreading of the internet and the exponential increase in small hardware diversity allow the creation of the internet of things (IoT)-based indoor localization systems. This chapter reviews the traditional and machine learning-based methods for IoT-based positioning systems. The traditional methods include various distance estimation and localization approaches; however, these approaches have some limitations. Because of the high prediction performance, machine learning algorithms are used for indoor localization problems in recent years. The chapter focuses on presenting an overview of the application of machine learning algorithms in indoor localization problems where the traditional methods remain incapable.

scholarly journals Photography Trilateration Indoor Localization with Image Sensor Communication

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3290 ◽  
Author(s):  
Nam Tuan Le ◽  
Yeong Min Jang
Keyword(s):  
Indoor Localization ◽  
Distance Estimation ◽  
Channel Modeling ◽  
Image Sensor ◽  
Error Rates ◽  
Localization Scheme ◽  
Wide Range ◽  
Successful Approach ◽  
Smart Mobile ◽  
The Internet Of Things

Localization has become an important aspect in a wide range of mobile services with the integration of the Internet of things and service on demand. Numerous mechanisms have been proposed for localization, most of which are based on the estimation of distances. Depending on the channel modeling, each mechanism has its advantages and limitations on deployment, exhibiting different performances in terms of error rates and implementation. With the development of technology, these limitations are rapidly overcome with hybrid systems and enhancement schemes. The successful approach depends on the achievement of a low error rate and its controllability by the integration of deployed products. In this study, we propose and analyze a new distance estimation technique employing photography and image sensor communications, also named optical camera communications (OCC). It represents one of the most important steps in the implemented trilateration localization scheme with real architectures and conditions of deployment which is the second our contribution for this article. With the advantages of the image sensor hardware integration in smart mobile devices, this technology has great potential in localization-based optical wireless communication

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 IMPROVED INDOOR LOCALIZATION WITH DIVERSITY AND FILTERING BASED ON RECEIVED SIGNAL STRENGTH MEASUREMENTS

2010 ◽  
pp. 9-15
Author(s):  
Andreas Fink ◽  
Helmut Beikirch ◽  
Matthias Voss
Keyword(s):  
Data Transmission ◽  
Indoor Localization ◽  
Estimation Error ◽  
Path Loss ◽  
Distance Estimation ◽  
Signal Strength ◽  
Location Estimation ◽  
Indoor Environments ◽  
Redundant Data ◽  
Unknown Node

Distance estimation by the evaluation of RSSI measurements is a simple and well-known technique to predict the position of an unknown node. Therefore the infrastructure does not have to be extended by expensive hardware for synchronization or direction approximation. However, with the localization based on RSSI measurements common and proven systems can be used for the infrastructure. For indoor environments the distance-pending path loss is affected by strong variations, especially appearing as frequency specific signal dropouts. A diversity concept with redundant data transmission in different frequency bands can reduce the dropout probability. If also space diversity and plausibility filtering are used, the Location Estimation Error can be reduced significantly. The investigations show that a good performance for precision and availability can also be reached with low infrastructural costs.

scholarly journals A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Sharly Joana Halder ◽  
Wooju Kim
Keyword(s):  
Indoor Localization ◽  
Distance Estimation ◽  
Link Quality ◽  
Average Error ◽  
Indoor Environments ◽  
Interference Avoidance ◽  
Localization System ◽  
Context Aware Computing ◽  
Link Quality Indicator ◽  
Unknown Node

Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator- (RSSI-) based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI), which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.

scholarly journals Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3149 ◽  
Author(s):  
Jiheon Kang ◽  
Joonbeom Lee ◽  
Doo-Seop Eom
Keyword(s):  
Indoor Localization ◽  
Inertial Sensor ◽  
Distance Estimation ◽  
Dead Reckoning ◽  
Android Application ◽  
Sensory Signal ◽  
Novel Method ◽  
The Cost ◽  
Deep Learning Model ◽  
Pedestrian Dead Reckoning

We introduce a novel method for indoor localization with the user’s own smartphone by learning personalized walking patterns outdoors. Most smartphone and pedestrian dead reckoning (PDR)-based indoor localization studies have used an operation between step count and stride length to estimate the distance traveled via generalized formulas based on the manually designed features of the measured sensory signal. In contrast, we have applied a different approach to learn the velocity of the pedestrian by using a segmented signal frame with our proposed hybrid multiscale convolutional and recurrent neural network model, and we estimate the distance traveled by computing the velocity and the moved time. We measured the inertial sensor and global position service (GPS) position at a synchronized time while walking outdoors with a reliable GPS fix, and we assigned the velocity as a label obtained from the displacement between the current position and a prior position to the corresponding signal frame. Our proposed real-time and automatic dataset construction method dramatically reduces the cost and significantly increases the efficiency of constructing a dataset. Moreover, our proposed deep learning model can be naturally applied to all kinds of time-series sensory signal processing. The performance was evaluated on an Android application (app) that exported the trained model and parameters. Our proposed method achieved a distance error of <2.4% and >1.5% on indoor experiments.

scholarly journals UWB Indoor Localization Using Deep Learning LSTM Networks

2020 ◽  
Vol 10 (18) ◽  
pp. 6290 ◽  
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Indoor Localization ◽  
Distance Estimation ◽  
Wide Band ◽  
Batch Size ◽  
Estimation Accuracy ◽  
Time Of Arrival ◽  
Localization Error ◽  
Localization Accuracy

Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches.

scholarly journals Review of Indoor Positioning: Radio Wave Technology

2020 ◽  
Vol 11 (1) ◽  
pp. 279
Author(s):  
Tan Kim Geok ◽  
Khaing Zar Aung ◽  
Moe Sandar Aung ◽  
Min Thu Soe ◽  
Azlan Abdaziz ◽  
...  
Keyword(s):  
Radio Wave ◽  
Communication Networks ◽  
Indoor Localization ◽  
Reference Signal ◽  
Distance Estimation ◽  
Indoor Positioning ◽  
Positioning System ◽  
Micro Electro Mechanical Systems ◽  
Positioning Errors ◽  
Received Power

The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.

scholarly journals Traveled Distance Estimation Algorithm for Indoor Localization

2014 ◽  
Vol 17 ◽  
pp. 248-255 ◽  
Author(s):  
Rui Terra ◽  
Lino Figueiredo ◽  
Ramiro Barbosa ◽  
Ricardo Anacleto
Keyword(s):  
Indoor Localization ◽  
Distance Estimation ◽  
Estimation Algorithm
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