Abstract: Indoor positioning technologies have been evolved rapidly. They augment the Global Positioning System (GPS) which requires line-of-sight to the sky to track the location of people or objects. In this study, we developed an error correction method for an indoor real-time location system (RTLS) based on an ultra-wideband (UWB) sensor from Decawave. Multiple stationary nodes (anchor) were installed throughout the workspace. The distance between stationary and moving nodes (tag) can be measured using a two-way-ranging (TWR) scheme. The result has shown that the uncorrected ranging error from the sensor system can be as large as 1 m. To reduce ranging error and thus increase positioning accuracy, we present an online correction algorithm using the Kalman filter. The results from experiments have shown that the system can reduce ranging error down to 5 cm.
Abstract: With this contribution, we want to show how the AiRT system could change the future way of working of a part of the creative industry and what new economic opportunities could arise for them. Remotely Piloted Aircraft Systems (RPAS), also more commonly known as drones, are now essential tools used by many different companies for their creative outdoor work. However, using this very flexible applicable tool indoor is almost impossible, since safe navigation cannot be guaranteed by the operator due to the lack of a reliable and affordable indoor positioning system which ensures a stable flight, among other issues. Here we present our first results of a European project, which consists of developing an indoor drone for professional footage especially designed for the creative industries. One of the main achievements of this project is the successful implication of the end-users in the overall design process from the very beginning. To ensure safe flight in confined spaces, our drone incorporates a positioning system based on ultra-wide band technology, an RGB-D (depth) camera for 3D environment reconstruction and the possibility to fully pre-program automatic flights. Since we also want to offer this tool for inexperienced pilots, we have always focused on user-friendly handling of the whole system throughout the entire process.
Abstract: Visible Light Communication (VLC) provides wireless communication features in illumination systems. One of the key applications is to recognize the user location by indoor illuminators such as light emitting diodes. For localization of individual receivers in these systems, we usually assume that receivers and transmitters are placed in parallel. However, it is difficult to satisfy this assumption because the receivers move randomly in real case. It is necessary to analyze the case when transmitter is not placed perfectly parallel to receiver. It is also important to identify changes on optical gain by the tilted angles and distances of them against the illuminators. In this paper, we simulate optical gain for various cases where the tilt of the receiver and the distance change. Then, we identified changing patterns of optical gains according to tilted angles of a receiver and distance. These results can help many VLC applications understand the extent of the location errors with regard to optical gains of the receivers and identify the root cause.
Abstract: This paper discusses the design of an indoor mobile robot positioning system. The problem of indoor positioning is solved through Wi-Fi fingerprint positioning to implement a low cost deployment. A wireless fingerprint matching algorithm based on the similarity of unequal length sequences is presented. Candidate sequences selection is defined as a set of mappings, and detection errors caused by wireless hotspot stability and the change of interior pattern can be corrected by transforming the unequal length sequences into equal length sequences. The presented scheme was verified experimentally to achieve the accuracy requirements for an indoor positioning system with low deployment cost.
Abstract: This paper presents a route guidance system for car owners to find their cars in parking garages. The presents system comprises a positioning-assisting subsystem and a car-finding mobile app. The positioning-assisting subsystem mainly uses the iBeacon technology for indoor positioning. The car-finding mobile app guides car owners to their cars based on a non-map navigation strategy. This study also designs a virtual coordinate system to support identifying the locations of parking spaces and iBeacon devices. We use Arduino and Android as the platforms to implement the proposed positioning-assisting subsystem and car-finding mobile app, respectively. We have also deployed the system in a parking garage in our campus for testing. Experimental results verify that our system can efficiently and correctly guide car owners to the parking spaces of their cars.
Abstract: In this paper, we propose an optimization technique
that can be used to optimize the placements of reference nodes and
improve the location determination performance for the multi-floor
building. The proposed technique is based on Simulated Annealing
algorithm (SA) and is called MSMR-M. The performance study in
this work is based on simulation. We compare other node-placement
techniques found in the literature with the optimal node-placement
solutions obtained from our optimization. The results show that using
the optimal node-placement obtained by our proposed technique can
improve the positioning error distances up to 20% better than those of
the other techniques. The proposed technique can provide an average
error distance within 1.42 meters.
Abstract: In recent years, Radio Frequency Identification (RFID)
is followed with interest by many researches, especially for the
purpose of indoor positioning as the innate properties of RFID are
profitable for achieving it. A lot of algorithms or schemes are proposed
to be used in the RFID-based positioning system, but most of them are
lack of environmental consideration and it induces inaccuracy of
application. In this research, a lot of algorithms and schemes of RFID
indoor positioning are discussed to see whether effective or not on
application, and some rules are summarized for achieving accurate
positioning. On the other hand, a new term “Noise Factor" is involved
to describe the signal loss between the target and the obstacle. As a
result, experimental data can be obtained but not only simulation; and
the performance of the positioning system can be expressed
substantially.
Abstract: Ability of accurate and reliable location estimation in
indoor environment is the key issue in developing great number of
context aware applications and Location Based Services (LBS).
Today, the most viable solution for localization is the Received
Signal Strength (RSS) fingerprinting based approach using wireless
local area network (WLAN). This paper presents two RSS
fingerprinting based approaches – first we employ widely used
WLAN based positioning as a reference system and then investigate
the possibility of using GSM signals for positioning. To compare
them, we developed a positioning system in real world environment,
where realistic RSS measurements were collected. Multi-Layer
Perceptron (MLP) neural network was used as the approximation
function that maps RSS fingerprints and locations. Experimental
results indicate advantage of WLAN based approach in the sense of
lower localization error compared to GSM based approach, but GSM
signal coverage by far outreaches WLAN coverage and for some
LBS services requiring less precise accuracy our results indicate that
GSM positioning can also be a viable solution.
Abstract: This paper presents an analysis of the localization accuracy of indoor positioning systems using Cramer-s rule via IEEE 802.15.4 wireless sensor networks. The objective is to study the impact of the methods used to convert the received signal strength into the distance that is used to compute the object location in the wireless indoor positioning system. Various methods were tested and the localization accuracy was analyzed. The experimental results show that the method based on the empirical data measured in the non line-of-sight (NLOS) environment yield the highest localization accuracy; with the minimum error distance less than 3 m.
Abstract: In this paper, we present a method named Signal Level
Matrix (SLM) which can improve the accuracy and stability of active
RFID indoor positioning system. Considering the accuracy and cost,
we use uniform distribution mode to set up and separate the
overlapped signal covering areas, in order to achieve preliminary
location setting. Then, based on the proposed SLM concept and the
characteristic of the signal strength value that attenuates as the
distance increases, this system cross-examines the distribution of
adjacent signals to locate the users more accurately. The experimental
results indicate that the adaptive positioning method proposed in this
paper could improve the accuracy and stability of the positioning
system effectively and satisfyingly.
Abstract: This paper presents a studyof the impact of reference
node locations on the accuracy of the indoor positioning systems. In
particular, we analyze the localization accuracy of the RSSI database
mapping techniques, deploying on the IEEE 802.15.4 wireless
networks. The results show that the locations of the reference nodes
used in the positioning systems affect the signal propagation
characteristics in the service area. Thisin turn affects the accuracy of the wireless indoor positioning system. We found that suitable
location of reference nodes could reduce the positioning error upto 35 %.