Reaaliaikainen sis¨atilapaikannus rakennusty¨omaalla k¨aytt¨aen BLE-majakoiden trilateraatiota

A real-time indoor location tracking system prototype for construction site resource tracking was developed in this Master's Thesis. The positioning technology used was Bluetooth Low Energy Beacons and the method was trilateration. The prototype developed in this work is built upon a simpler version of a location tracking system prototype developed in iCONS research project in Aalto University. The contextual purpose of this work was to investigate in which ways a coordinate level indoor positioning system could enhance production control in construction. The lean construction philosophy is the theoretical background of this research topic. The Design Science research method was followed. The process of implementation was documented in detail. The prototype was tested in a construction site to determine the location of a person carrying a BLE beacon. The accuracy turned out to be around 10 meters at best when there was least movement. Various aspects other than accuracy have also been evaluated, and ideas for improvement are presented. The value and the applications of an ideally working coordinate level real-time location tracking system for construction production control was assessed in light of the research literature and the experience gained from creating and testing the prototype. Such a system would have a significantly positive impact on the productivity, transparency, and safety in construction.Tässä diplomityössä kehitettiin reaaliaikainen sisätilapaikannusjärjestelmä rakennustyömaan resurssien seurantaan. Paikannustekniikkana toimi Bluetooth Low Energy (BLE) -majakat ja niiden paikantaminen trilateraation avulla. Työssä kehitetty prototyyppi rakentui Aalto-yliopiston iCONS-tutkimusprojektissa kehitetyn yksinkertaisemman paikannusjärjestelmän päälle. Tässä työssä tutkittiin, millä tavoin koordinaattitason sisätilapaikannusjärjestelmä voisi parantaa tuotannonohjausta rakentamisessa. Lean-rakentaminen on tämän tutkimusaiheen teoreettinen tausta. Design Science -tutkimusmenetelmää sovellettiin tässä työssä. Menetelmän mukaisen artifaktin toteutusprosessi dokumentoitiin yksityiskohtaisesti. Prototyyppiä testattiin oikealla rakennustyömaalla BLE-majakkaa kantavan henkilön sijainnin määrittämiseksi. Tarkkuus ylsi parhaimmillaan noin 10 metriin, kun liikettä oli vähiten. Tarkkuuden lisäksi järjestelmän muita aspekteja on myös arvioitu ja parannusideoita esitetty. Ideaalin reaaliaikaisen paikannusjärjestelmän arvoa ja sovelluksia rakennusalan tuotannonohjauksessa arvioitiin sekä tutkimuskirjallisuuden että prototyypistä saadun tiedon valossa. Tällaisella järjestelmällä olisi merkittävä vaikutus rakentamisen tuottavuuteen, läpinäkyvyyteen ja turvallisuuteen

Accurate indoor positioning system based on modify nearest point technique

Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m)

Improvement of a positioning system for assisted and autonomous driving

In the recent years, the automotive industry has oriented its research and development efforts towards autonomous and assisted driving. These are critical systems as they must guarantee their robustness and reliability for human life, and therefore they have complex designs and involve the most innovative technologies. Location and positioning system is one of its more challenging mechanisms that requires high availability and precision. For this problem there are many technological approaches, but most common ones are GNSS-like and image recognition solutions. They both have accurate and reliable performance under favourable conditions. However, due to the variability of the environment they also have some points of failure that can result in an accident. In certain adverse conditions, to obtain a precise and reliable localization, further information is needed. Here is where Ultra Wide Band technology (UWB) with support of additional sensing mechanisms such as Inertial Navigation System (INS), can provide additional sources of information. This data fusion is done by means of Kalman Filtering, more concretely the Extended Kalman Filter algorithm, which applies for non-linear systems like the one under study. In this document, the whole positioning system's performance will be analysed and evaluated with some simulations and real scenario tests, comparing the main approaches of the Kalman Filter (linear and non-linear) and obtaining an optimized solution for the positioning mechanism. Also, some conclusions and future work tips will be provided in order to contribute the knowledge in similar areas.En los últimos años, la industria del automóvil ha orientado sus esfuerzos de investigación y desarrollo hacia la conducción autónoma y asistida. Estos son sistemas críticos ya que deben garantizar su robustez y fiabilidad para la vida humana, por lo que adquieren diseños complejos e involucran las tecnologías más innovadoras. El sistema de ubicación y posicionamiento es uno de sus mecanismos más desafiantes que requiere una alta disponibilidad y precisión. Para este problema existen muchos enfoques tecnológicos, pero los más comunes son las soluciones de reconocimiento de imágenes y sistemas GNSS. Ambos tienen un rendimiento preciso y confiable en condiciones favorables. Sin embargo, debido a la variabilidad del entorno también tienen algunos puntos de fallo que pueden resultar en un accidente. En determinadas condiciones adversas, para obtener una localización precisa y fiable, se necesita más información. Aquí es donde la tecnología de banda ultra ancha (UWB) con soporte de mecanismos de detección adicionales como el sistema de navegación inercial (INS), puede proporcionar fuentes adicionales de información. Esta fusión de datos se realiza mediante el filtrado de Kalman, más concretamente el algoritmo de filtro de Kalman extendido, que se aplica a sistemas no lineales como el que se está estudiando. En este documento se analizará y evaluará el desempeño de todo el sistema de posicionamiento con algunas simulaciones y pruebas de escenarios reales, comparando los principales enfoques del Filtro de Kalman (lineal y no lineal) y obteniendo una solución optimizada para el mecanismo de posicionamiento. Asimismo, se brindarán algunas conclusiones y consejos de trabajo futuro con el fin de aportar el conocimiento en áreas similares.En els darrers anys, la indústria de l'automòbil ha orientat els seus esforços de recerca i desenvolupament cap a la conducció autònoma i assistida. Es tracta de sistemes crítics, ja que han de garantir la seva robustesa i fiabilitat per a la vida humana i, per tant, tenen dissenys complexos i impliquen les tecnologies més innovadores. El sistema de localització i posicionament és un dels seus mecanismes més difícils que requereix una alta disponibilitat i precisió. Per a aquest problema hi ha molts enfocaments tecnològics, però els més comuns són solucions similars al GNSS i de reconeixement d'imatges. Tots dos tenen un rendiment precís i fiable en condicions favorables. Tot i això, a causa de la variabilitat de l'entorn, també presenten alguns punts de fracàs que poden provocar un accident. En certes condicions adverses, per obtenir una localització precisa i fiable, es necessita més informació. Aquí és on la tecnologia Ultra Wide Band (UWB) amb suport de mecanismes de detecció addicionals com el sistema de navegació inercial (INS), pot proporcionar fonts d'informació addicionals. Aquesta fusió de dades es fa mitjançant el filtratge de Kalman, més concretament l'algorisme del filtre Kalman estès, que s'aplica a sistemes no lineals com el que s'està estudiant. En aquest document, s'analitzarà i avaluarà el rendiment de tot el sistema de posicionament amb algunes simulacions i proves d'escenaris reals, comparant els enfocaments principals del filtre Kalman (lineal i no lineal) i obtenint una solució optimitzada per al mecanisme de posicionament. A més, es proporcionaran algunes conclusions i futurs consells de treball per tal de contribuir al coneixement en àrees similars

Practical implementation of a hybrid indoor localization system

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndoor localization systems occupy a significant role to track objects during their life cycle, e.g., related to retail, logistics and mobile robotics. These positioning systems use several techniques and technologies to estimate the position of each object, and face several requirements such as position accuracy, security, coverage range, energy consumption and cost. This master thesis describes a real-world scenario implementation, based on Bluetooth Low Energy (BLE) beacons, evaluating a Hybrid Indoor Positioning System (H-IPS) that combines two RSSI-based approaches: Multilateration (MLT) and Fingerprinting (FP). The objective is to track a target node, assuming that the object follows a linear motion model. It was employed Kalman Filter (KF) to decrease the positioning errors of the MLT and FP techniques. Furthermore a Track-to-Track Fusion (TTF) is performed on the two KF outputs in order to maximize the performance. The results show that the accuracy of H-IPS overcomes the standalone FP in 21%, while the original MLT is outperformed in 52%. Finally, the proposed solution demonstrated a probability of error < 2 m of 80%, while the same probability for the FP and MLT are 56% and 20%, respectively.Os sistemas de localização de ambientes internos desempenham um papel importante na localização de objectos durante o seu ciclo de vida, como por exemplo os relacionados com o varejo, a logística e a robótica móvel. Estes sistemas de localização utilizam várias técnicas e tecnologias para estimar a posição de cada objecto, e possuem alguns critérios tais como precisão, segurança, alcance, consumo de energia e custo. Esta dissertação de mestrado descreve uma implementação num cenário real, baseada em Bluetooth Low Energy (BLE) beacons, avaliando um Sistema Híbrido de Posicionamento para Ambientes Internos (H-IPS, do inglês Hybrid Indoor Positioning System) que combina duas abordagens baseadas no Indicador de Intensidade do Sinal Recebido (RSSI, do inglês Received Signal Strength Indicator): Multilateração (MLT) e Fingerprinting (FP). O objectivo é localizar um nó alvo, assumindo que o objecto segue um modelo de movimento linear. Foi utilizado Filtro de Kalman (FK) para diminuir os erros de posicionamento do MLT e FP, além de aplicar uma fusão de vetores de estado nas duas saídas FK, a fim de maximizar o desempenho. Os resultados mostram que a precisão do H-IPS supera o FP original em 21%, enquanto que o MLT original tem um desempenho superior a 52%. Finalmente, a solução proposta apresentou uma probabilidade de erro de < 2 m de 80%, enquanto a mesma probabilidade para FP e MLT foi de 56% e 20%, respectivamente

Indoor localization based on hybrid Wi-Fi hotspots

Most existing indoor localization algorithms basedon Wi-Fi signals mainly rely on wireless access points (APs), i.e. hotspots, with fixed deployment, which are easily affected by the non-line of sight (NLOS) factors and the multipath effect. There also exist many other problems, such as positioning stability and blind spots, which can cause decline in positioning accuracy at certain positions, or even failure of positioning. However, it will increase the hardware cost by adding more static APs; if the localization mechanism integrates different wireless signals is adopted, it tends to cause high cost of positioning and long complex positioning process, etc. In this paper, we proposed a novel hybrid Wi-Fi access point-based localization algorithm (HAPLA), which utilizes the received signal strength indications(RSSI) from static APs and dynamic APs to determine location scenes. It flexibly selects available AP signals and dynamically switches the positioning methods, thus to achieve efficient positioning. HAPLA only relies on the Wi-Fi signal strength values, which can reduce the cost of hardware and the complexity of localization system. The proposed method can also be able to effectively prevent interference from different signal sources. Inour test scenario, we deployed typical indoor scenes with the NLOS factors and the multipath effect for experiments. The experiments demonstrate the effectiveness of proposed method and the results show that, compared with the classic K nearest neighbor-based location algorithm (KNN) and the variance-based fingerprint distance adjustment algorithm (VFDA), HAPLA has better adaptability and higher positioning accuracy, and can effectively solve the problem of positioning blind spots

INDOOR LOCATION TRACKING AND ORIENTATION ESTIMATION USING A PARTICLE FILTER, INS, AND RSSI

With the advent of wireless sensor technologies becoming more and more common-place in wearable devices and smartphones, indoor localization is becoming a heavily researched topic. One such application for this topic is in the medical field where wireless sensor devices that are capable of monitoring patient vitals and giving accurate location estimations allow for a less intrusive environment for nursing home patients. This project explores the usage of using received signal strength indication (RSSI) in conjunction with an inertial navigation system (INS) to provide location estimations without the use of GPS in a Particle Filter with a small development microcontroller and base station. The paper goes over the topics used in this thesis and the results

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods