Indoor Localization and Identification

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Identification & Mitigation of NLOS Information for UWB based Indoor Localization

Identification & Mitigation of NLOS Information for UWB based Indoor Localization

Real time indoor positioning systems are a growing field due to the increase in use of mobile and portable devices. The need of a solution for indoor location based services is increasing [1]. When a person wishes to travel, getting the location and route to a destination has never been easier with the advent of cell phones and Global Positioning Systems (GPS). GPS works well outdoors but has poor performance indoors due signal attenuation from the walls of a building. To get a target’s position indoors, a different solution is required; that solution is indoor localization, also known as “indoor GPS”. There are a variety of proposed methods to get a user’s position indoors, with the most popular using Wireless Local Area Network (WLAN) and Ultra-Wideband (UWB) spectrum. WLAN based systems use finger printing algorithms; however, WLAN based positioning does not provide good accuracy [2]. There is currently no set standard in terms of wireless spectrum for indoor positioning due to all of the various types of complex indoor environments. The best candidate for high accuracy positioning is an UWB based Indoor Positioning System (IPS), especially since UWB does not interfere with other wireless spectrums and has accuracy on the order of centimeters [2]. The downfall of UWB based IPS’, particularly systems employing Time of Arrival (ToA), is that they suffer from location inaccuracies due to the multipath effect from physical obstructions found in an indoor environment [3]. These obstructions cause a scenario called No Line-of-Sight (NLOS) to occur between the transmitter and receiver. By identifying and mitigating the inaccuracies caused by NLOS, the biggest downfall of UWB based IPS’ could be solved, thus making the system more robust while maintaining accuracy.
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Indoor Localization Of Robot In Biofuel Lab With Web Of Things

Indoor Localization Of Robot In Biofuel Lab With Web Of Things

Drones have becomes popular in today’s scenario to perfrom various ardent task which human beings cannot afford to do.They are actively invloved in tasks like patrol,transportation and data collection which are readily developed in indoor environments ,like industrial premises ,factories and buildings.Through this paper a proposal is illustrated which is entirely dependent on a network of connected drones which communicates with a central server.Drones are ,managed bu users who have hands on expertise on the web application [1].The best design archetype in today’s scenario is the Internet of Things (IoT).WSN comes in handy when it is related for collection of data for a specific application.Depending upon the choice of wireless sensor network application it becomes really essential to have the exact location information[2]The BASs (Building Automation System) has changed the outlook of industrial systems as BASs are considered to be the integral part of the IoT technologies.Several problems do hamper the developmemt of BASs,especially the deployment which is considered to be highly difficult and time consuming[3]Several application domains are benefiting from the RFID (Radio Frequency Identification) technology.Indoor positioning technologies are also used by ware house goods positioning, item positioning in the production assembly lines and worker positioning in various construction sites.However Indoor positioning using RFID faces great issues in the dynamic environment thus degrading it to great extent, especially when tracking of random moving targets are carried out [4] The sensor node information and controlling of things are holded up to a great extent by the Internet of Things (IoT).The brilliance of IoT
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Ensemble learning particle swarm optimization for real time UWB indoor localization

Ensemble learning particle swarm optimization for real time UWB indoor localization

With the popularization of smart devices and the devel- opment of mobile Internet, there is an increasing de- mand for indoor positioning. Indoor localization-based services can support many application scenarios, such as public security and emergency response and positioning navigation. Diverse technologies have been developed for precise indoor localization. Localization technology based on Global Position System (GPS) and maps have been widely used. But GPS location signals are not able to penetrate buildings; they are unable to work indoors. In order to overcome the GPS positioning defects and realize the accurate positioning in the complex indoor en- vironment, many practical indoor localization schemes are introduced, such as infrared, WIFI, Bluetooth, ZigBee, ultrasound, radio frequency identification (RFID), and ultra-wideband (UWB). Infrared [1] is limited by its prop- erties and vulnerable to the external environment; the po- sitioning accuracy can only be up to 5 m. WIFI [2], Bluetooth [3], and ZigBee [4] can only locate the area of about a few tens of meters, and its positioning ac- curacy can only reach 3 m, unable to meet the indoor mobile positioning demand. Ultrasonic [5] indoor
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Enhancing RFID indoor localization with cellular technologies

Enhancing RFID indoor localization with cellular technologies

Indoor localization, this means solutions providing the position of mobile objects/persons in indoor environments (e.g., hospitals, malls, etc.), is one of the most cutting-edge services with growing demand in smart applications such as robotics for care, pedestrian navigations, etc. With the objective of providing indoor localization, this paper presents the experimental analysis of simultaneous radio frequency measurements from two different radio frequency systems: Ultra High Frequency Radio Frequency IDentification (UHF RFID) and macrocellular networks. Extensively deployed cellular technologies (Global System for Mobile communications (GSM) and Universal Mobile Telecommunications System (UMTS)) are here evaluated with the purpose of enhancing pre-existent RFID-based localization systems at reduced costs. Temporal and statistical analysis of the measurements gathered from each technology is performed, and its applicability for localization is assessed. Based on this analysis, a RFID localization mechanism that is able to integrate macrocellular technologies information is proposed, showing improved results in terms of accuracy.
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Identification and Mitigation of NLOS based on Channel Information Rules for Indoor UWB Localization

Identification and Mitigation of NLOS based on Channel Information Rules for Indoor UWB Localization

Position and tracking systems working on Wireless Local Area Network (WLAN), Blue- tooth, ZigBee or Radio Frequency Identification (RFID) are usually based on Received Signal Strength Indicator (RSSI) estimation, and their accuracy is low [7]. One of the most promising technologies addressed to position and tracking systems is Ultra Wide Band (UWB) [8]. UWB combines remarkable features concerning size and power consump- tion, providing high accuracy on distance estimation and allowing simultaneous location and data transmission with high data rates. Impulse Radio (IR) UWB communication sys- tems are based on the transmission of very short duration pulses, which originates very high bandwidth signals. The downfall of UWB based systems employing Time of Ar- rival (TOA), is that they suffer from location inaccuracies due to the multipath effect from physical obstacles found in an indoor environment [9]. These obstacles cause a scenario called Non Line-of-Sight (NLOS) to occur between the transmitter and receiver. Because of NLOS, the calculated distance between the transmitter and receiver will be biased, and the end application of those systems will be affected. Also, NLOS inaccuracy varies and depends on the size of the obstacle and the material of the obstacle. Therefore, it is nec- essary that NLOS identification and mitigation techniques are introduced to improve the accuracy of indoor localization. By identifying and mitigating the inaccuracies caused by NLOS, positioning system based on UWB or any other system which suffer from this phe- nomenon, can be made more robust while maintaining accuracy.
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Robust indoor localization and tracking using GSM fingerprints

Robust indoor localization and tracking using GSM fingerprints

This paper presents a room-level indoor localization method that uses support vector machines (SVMs) to classify RSS vectors containing very large numbers of GSM channels. Using a “space sampling” scheme, train- ing and test data was recorded while randomly walking inside the rooms, which enables localizing a mobile terminal in arbitrary positions, not only in some repre- sentative points. The robustness problem of the received signal strength fingerprinting approach was investigated with experiments over several months. The evolution of received signal strengths and the localization perform- ance are examined. In order to combat the severe, performance-degrading fluctuations to which radiotele- phone RSS values are susceptible [15], a “time sampling” scheme is introduced to incorporate as much as possible of the RSS fluctuations. A Bayesian filter is furthermore applied in order to employ a priori information about
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Indoor topological localization using a visual landmark sequence

Indoor topological localization using a visual landmark sequence

Several tricks were applied to train AlexNet for our indoor object detection. Firstly, the output layer was modified to recognize the target indoor objects. AlexNet was originally designed for ImageNet competition, which aims to recognize 1000 types of objects. However, not all indoor objects of our interest were included. We replaced the output layer with new one, in which the number of neurons equals the number of our interesting indoor objects. The softmax function was chosen as the activation function of output layer neurons. Secondly, we retrained AlexNet with a finetuning technique. Only the newly added layer was allowed to retrain, while the weights of the rest of the layers were fixed when fine-tuning. Finally, to eliminate the object variations caused by illuminations, rotations, and movement, we conducted data augmentation by pre-processing the original images. For each original image, we change its brightness by adding 10, 30, − 10, and − 30 to produce new images. We rotated the original image by 5 ◦ , 10 ◦ , − 5 ◦ , and − 10 ◦ . The movement of pedestrians led to the partial occlusion of targets of interest. We also generated new images by randomly cropping original images to sizes of 224 × 224. The brightness and rotating images were altered with the original images, and cropping was done in the training stage. In this way, we enlarged the training dataset, and the trained network was robust to those variations.
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Robust Techniques for Accurate Indoor Localization in Hazardous Environments

Robust Techniques for Accurate Indoor Localization in Hazardous Environments

It is important to notice that our work focused on cre- ating new theoretical methods, for accurate estimation of time delay parameters, which can cater for various ap- plication scenarios [11]. Therefore one need not limit the application possibilities to just UWB based systems. The high noise immunity and versatility under low bandwidth conditions make these methods prime candidates for use in economical wireless sensor networks. Systems intro- duced in [12,13] utilize a simple sensor network using audible sound for positioning, a wireless LAN for syn- chronization and an ultra sound based distributed posi- tioning system with signaling and synchronization done by a RF pulse respectively. Both these systems use sim- ple correlation or counter based peak detection algo- rithms for the TOA estimation process. Even with such simple methods, for example the audible sound based system yields accuracy to within two feet in 97% cases for 2-D case and a corresponding accuracy to within three feet in 95% cases for 3-D case. This can be easily improved upon by using the super resolution techniques suggested in this paper. Ultra sound and audible sound sys- tems display poor results under noisy conditions and such occurrences are frequent in indoor environments [14].
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Sensor Fusion and Deep Learning for Indoor Agent Localization

Sensor Fusion and Deep Learning for Indoor Agent Localization

The earliest, and most crude form of localization is called dead-reckoning. Dead- reckoning involves using odometry information, trigonometry, and robot kinematics to determine how far the agent travels from its initial position. Dead reckoning is plagued by two major issues. The first is the algorithm requires knowledge of the agent’s initial position, and the second is over time measurement errors cause the accuracy to decrease to an unacceptable level. Ojeda et al. [22] presented a localiza- tion system for a humans that utilized an IMU to perform dead reckoning. While the system was fairly accurate at detecting linear displacement over short distances, it struggled to remain accurate with longer walks. To improve this form of localization, error correction techniques were added to account for the accumulating sensor error. Among the most popular techniques, Thrun et al. [23] used a probabilistic approach known as particle filtering, and Kwon et al. [15] utilized extended Kalman Filters for error correction. Others tried to improve dead reckoning techniques by including ad- ditional sensor data with the odometry information. Kim et al. [16] combined dead reckoning with data from ultrasonic sensors and used a Kalman Filter to improve localization performance.
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Towards the Next Generation of Location-Aware Communications

Towards the Next Generation of Location-Aware Communications

In this thesis, we contribute to the understanding of 5G mmWave localiza- tion by focusing on challenges pertaining to this emerging technology. Towards that, we start by considering a conventional cellular system and propose a posi- tioning method under outdoor LOS/NLOS conditions that, although approaches the Cram´er-Rao lower bound (CRLB), provides accuracy in the order of meters. This shows that conventional systems have limited range of location-aware appli- cations. Next, we focus on mmWave localization in three stages. Firstly, we tackle the initial access (IA) problem, whereby user equipment (UE) attempts to estab- lish a link with a base station (BS). The challenge in this problem stems from the high directivity of mmWave. We investigate two beamforming schemes: directional and random. Subsequently, we address 3D localization beyond IA phase. Devices nowadays have higher computational capabilities and may perform localization in the downlink. However, beamforming on the UE side is sensitive to the device orientation. Thus, we study localization in both the uplink and downlink under
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An Indoor Localization Algorithm Based on RBF Neural Network Optimized by the Improved PSO

An Indoor Localization Algorithm Based on RBF Neural Network Optimized by the Improved PSO

Aimed at the problem of large localization error based on indoor received signal strength indication (RSSI), a RBF neural network (RBFNN) localization algorithm is proposed optimized by improved particle swarm optimization (PSO). Combined with resource allocation network (RAN), the number of nodes in hidden layer increase dynamically to determine the center of RBFNN, the number of nodes in hidden layer and spread constant. The inertia weight of PSO is improved to advance the global search ability of PSO and optimize the output weight of RBFNN. Finally, the optimized RBFNN is used for indoor RSSI positioning. Simulation and experimental results show that the improved localization algorithm has higher positioning accuracy.
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Indoor Localization System Using WiFi RSSI

Indoor Localization System Using WiFi RSSI

In this project is presented UCMA, an unsupervised calibrationmethod that can build an indoor localization systemusing unlabeled RSSI measurements. Simple modeling andoptimization techniques were employed in unsupervisedlearning on the unlabeledmeasurements.The evaluation onthe two office buildings confirmed that, under various conditions,the proposed method can build a precise localizationmodel without any location reference.An indoor map and online RSSI measurements are twoessential requirements in the service phase of fingerprinting-based localization systems. UCMA uses only the twoinputs for the calibration,whereas conventional approachesrequire extra inputs or extensive efforts. This indicates thata localization system can be implemented by UCMA withoutadditional cost except computational cost on the serverside. In that sense, the technique has the potential to makesignificant progress in indoor localization, especially in realizinga global indoor positioning system
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Enhanced wireless fingerprinting accuracy for indoor localization

Enhanced wireless fingerprinting accuracy for indoor localization

Indoor localization accuracy varies according to target environment and algorithm, which may impede certain techniques due to some limitations. Adapting AI approach for indoor localization is acceptable, brings accuracy, and shows a good response while increasing fingerprinting map resolution. Sudden changes could affect signal strength measurements temporarily, there are several methods handling this such as mixing with other localization techniques. Increasing finger resolution produces enhanced GRNN results. Literature stated that K mostly odd number, but in this experiment, it was K = 2 within 15 cm resolution. In addition, black line (Figure 5) brings large error in one point representing a sudden environmental change while taking experiment. This avoided either by taking several averaged measurements or GRNN fused with another technique. The overall results show GRNN brings acceptable positioning accuracy.
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Object Search and Localization for an Indoor Mobile Robot

Object Search and Localization for an Indoor Mobile Robot

In this paper we present a method for search and localization of objects with a mobile robot using a monocular camera with zoom capabilities. We show how to overcome the limitations of low resolution images in object recognition by utilizing a combination of an attention mechanism and zooming as the first steps in the recognition process. The attention mechanism is based on receptive field cooccurrence histograms and the object recognition on SIFT feature matching. We present two methods for estimating the distance to the objects which serve both as the input to the control of the zoom and the final object localization. Through extensive experiments in a realistic environment, we highlight the strengths and weaknesses of both methods. To evaluate the usefulness of the method we also present results from experiments with an integrated system where a global sensing plan is generated based on view planning to let the camera cover the space on a per room basis.
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Differential radio map based robust indoor localization

Differential radio map based robust indoor localization

Ubiquitous computing and communication have become popular with the development of wireless communica- tion technology over the last decade. The need for loca- tion information to capture contexts and configure them into the computing and communication processes, coupled with the unavailability of global positioning sys- tem (GPS) in indoor environment, has triggered increased research interest in indoor localization. Recently, numerous localization systems have been developed based on the received signal strength (RSS) of the wireless local area networks (WLANs). The advan- tage of these systems is that the cost of deploying a spe- cialized infrastructure is avoided. However, building an indoor localization system based on WLAN is a challen- ging problem due to the complex indoor signal propaga- tion character and different hardware solutions of different mobile devices.
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RSS Distance Rationalization Procedure for Localization in an Indoor Environment

RSS Distance Rationalization Procedure for Localization in an Indoor Environment

The computational capabilities of off-the-shelf wireless sensors networks presents a limitation when more complex forms of localization algorithms are employed for location estimation purposes, particularly in an indoor envi- ronment. Range-free algorithms rely on Received Signal Strength (RSS) from sensors that are location aware (anchor nodes) as the major means of distance estimation. This paper presents a non-site specific algorithm for better esti- mating RSS relationship with distance. By employing a unique form of ra- tionalization of raw RSS with respect to distance using the proposed algo- rithm, it is possible to enhance the reliability of RSS when employed in in- door Localization Algorithms. Consequently, this paper presents an innovative RSS-Distance rationalization algorithm for localization of objects in an indoor environment. The paper compared the proposed algorithm with Simple Moving Average (SMA) algorithm due to the wide applicability and ease of manipulation of SMA. The analysis of the proposed algorithm and SMA shows that the proposed algorithm better modifies RSS for more accurate po- sition estimation in an indoor environment.
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Enhanced Indoor Localization System based on Inertial Navigation

Enhanced Indoor Localization System based on Inertial Navigation

An improved INS based dead reckoning approach for pedestrian localization is proposed in this thesis. Due to higher levels of errors in the low cost sensors available in smartphones, traditional INS results in a large amount of drift from actual location within few seconds. Cor- rections to walking velocity from a velocity model derived using actual velocity measurements and walking direction corrections from domain specific knowledge are employed to control the drift to improve upon current methods. A prototype system was developed to investigate the performance of proposed method. Results from several experiments on walks demonstrate the increased accuracy and robustness of this method over SHS which is currently popular in this application area.
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Asymmetry Regional Boundary Localization for Indoor Robot Navigation

Asymmetry Regional Boundary Localization for Indoor Robot Navigation

Although, this technique required preparation phase that takes long time for initialization as well as large data storage to keep the record, its advantage is robustness against error from signal noise which mostly occurred to indoor environment. Due to the uncertainty of positioning result, some work doesn’t require the specific output. The concept of geometry regional area is applied. For example, Voronoi diagram [5], [7], [8], Multi-granularity [9], Approximate Point-In-Triangle (APIT). These approach seems to be difficult in mathematical terms and algorithmic complexity is high even though some method can provide the error less than 1 meter approximately. Prior knowledge in signal processing may require in order to manage pre-processed data in proper way. It can be noticed that, machine learning is applied in [2], [4], [6], [14], and [16]. Neural Network is preferable technique in non-linear classification problem comparing to others. Furthermore, Deep Neural Network is used in [14] for function approximation, solving RSSI fingerprints in specific environment. The research is [15] predicts the future trend of localization method that hybrid technology and sensor fusion may provide the efficient result using the combination between WSN, Wi-Fi, and Bluetooth Low Energy (BLE). [2] and [13] prove this idea, reducing positioning error to 1.05 m. Additionally, [1] mentions the hardware modification in order to increase signal quality. Since this work claims that signal cannot be approximated consistently, the absorbing plate is installed underneath antenna in order to avoid interference with signal reflected by ground that caused multi-path effect.
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Comparison of Indoor Localization Systems Based on Wireless Communications

Comparison of Indoor Localization Systems Based on Wireless Communications

Localization using a Wireless Sensor Network (WSN) has become a field of interest for researchers in the past years. This information is expected to aid in routing, systems maintenance and health monitoring. For example, many projects aiming to monitor the elderly at home include a personal area network (PAN) which can provide current location of the patient to the medical staff. This article presents an overview of the current trends in this domain. We introduce the mathematical tools used to determine position then we introduce a selection of range-free and range-based proposals. Finally, we provide a comparison of these techniques and suggest possible areas of improvement.
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A Comprehensive Method to combine RFID Indoor Targets Positioning with Real Geographic Environment

A Comprehensive Method to combine RFID Indoor Targets Positioning with Real Geographic Environment

During the real operations of system architecture, the collection data in the sensing layer has problems in asynchronous. The data in the network layer has problem with transmission time delay. These problems lead that the application layer’s integrity of standard data cannot be guaranteed. Hence we need to make rules on the application layer and filter the data to ensure that the localization algorithm evaluates correctly. The data filtering method is: after the application layer obtains data from the message middleware at a certain time, the system arranges the data into two-dimensional array of L*4. L represents the product of the reader number and the number of the tags in the indoor environment. Because there are three readers, four reference tags and a positioning tag, in the real test L is 15. Each row in the array represents a complete four- tuple data. Each column in array represents each element in the quaternion group. The system uses the active RFID readers whose frequency band are 2.4GHz for real deployment and testing. The range of RSSI values in the collection data is from 0- 65535, the whole data matrix form is shown in Figure 8.
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