6 research outputs found
Visible light communications-based indoor positioning via compressed sensing
This paper presents an approach for visible light communication-based indoor
positioning using compressed sensing. We consider a large number of light
emitting diodes (LEDs) simultaneously transmitting their positional information
and a user device equipped with a photo-diode. By casting the LED signal
separation problem into an equivalent compressed sensing framework, the user
device is able to detect the set of nearby LEDs using sparse signal recovery
algorithms. From this set, and using proximity method, position estimation is
proposed based on the concept that if signal separation is possible, then
overlapping light beam regions lead to decrease in positioning error due to
increase in the number of reference points. The proposed method is evaluated in
a LED-illuminated large-scale indoor open-plan office space scenario. The
positioning accuracy is compared against the positioning error lower bound of
the proximity method, for various system parameters.Comment: to appear in IEEE Communication Letter
A Novel Received Signal Strength Assisted Perspective-three-Point Algorithm for Indoor Visible Light Positioning
In this paper, a received signal strength assisted Perspective-three-Point
positioning algorithm (R-P3P) is proposed for visible light positioning (VLP)
systems. The basic idea of R-P3P is to joint visual and strength information to
estimate the receiver position using 3 LEDs regardless of the LEDs'
orientations. R-P3P first utilizes visual information captured by the camera to
estimate the incidence angles of visible lights. Then, R-P3P calculates the
candidate distances between the LEDs and the receiver based on the law of
cosines and the Wu-Ritt's zero decomposition method. Based on the incidence
angles, the candidate distances and the physical characteristics of the LEDs,
R-P3P can select the exact distances from all the candidate distances. Finally,
the linear least square (LLS) method is employed to estimate the position of
the receiver. Due to the combination of visual and strength information of
visible light signals, R-P3P can achieve high accuracy using 3 LEDs regardless
of the LEDs' orientations. Simulation results show that R-P3P can achieve
positioning accuracy within 10 cm over 70% indoor area with low complexity
regardless of LEDs orientations.Comment: arXiv admin note: substantial text overlap with arXiv:2004.0629
Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements
While a vast number of location-based services appeared lately, indoor
positioning solutions are developed to provide reliable position information in
environments where traditionally used satellite-based positioning systems
cannot provide access to accurate position estimates. Indoor positioning
systems can be based on many technologies; however, radio networks and more
precisely Wi-Fi networks seem to attract the attention of a majority of the
research teams. The most widely used localization approach used in Wi-Fi-based
systems is based on fingerprinting framework. Fingerprinting algorithms,
however, require a radio map for position estimation. This paper will describe
a solution for dynamic radio map creation, which is aimed to reduce the time
required to build a radio map. The proposed solution is using measurements from
IMUs (Inertial Measurement Units), which are processed with a particle filter
dead reckoning algorithm. Reference points (RPs) generated by the implemented
dead reckoning algorithm are then processed by the proposed reference point
merging algorithm, in order to optimize the radio map size and merge similar
RPs. The proposed solution was tested in a real-world environment and evaluated
by the implementation of deterministic fingerprinting positioning algorithms,
and the achieved results were compared with results achieved with a static
radio map. The achieved results presented in the paper show that positioning
algorithms achieved similar accuracy even with a dynamic map with a low density
of reference points
Indoor Visible Light Communication:A Tutorial and Survey
Abstract
With the advancement of solid-state devices for lighting, illumination is on the verge of being completely restructured. This revolution comes with numerous advantages and viable opportunities that can transform the world of wireless communications for the better. Solid-state LEDs are rapidly replacing the contemporary incandescent and fluorescent lamps. In addition to their high energy efficiency, LEDs are desirable for their low heat generation, long lifespan, and their capability to switch on and off at an extremely high rate. The ability of switching between different levels of luminous intensity at such a rate has enabled the inception of a new communication technology referred to as visible light communication (VLC). With this technology, the LED lamps are additionally being used for data transmission. This paper provides a tutorial and a survey of VLC in terms of the design, development, and evaluation techniques as well as current challenges and their envisioned solutions. The focus of this paper is mainly directed towards an indoor setup. An overview of VLC, theory of illumination, system receivers, system architecture, and ongoing developments are provided. We further provide some baseline simulation results to give a technical background on the performance of VLC systems. Moreover, we provide the potential of incorporating VLC techniques in the current and upcoming technologies such as fifth-generation (5G), beyond fifth-generation (B5G) wireless communication trends including sixth-generation (6G), and intelligent reflective surfaces (IRSs) among others
Dynamic spatial segmentation strategy based magnetic field indoor positioning system
In this day and age, it is imperative for anyone who relies on a mobile device to
track and navigate themselves using the Global Positioning System (GPS). Such
satellite-based positioning works as intended when in the outdoors, or when the
device is able to have unobstructed communication with GPS satellites.
Nevertheless, at the same time, GPS signal fades away in indoor environments due
to the effects of multi-path components and obstructed line-of-sight to the
satellite. Therefore, numerous indoor localisation applications have emerged in
the market, geared towards finding a practical solution to satisfy the need for
accuracy and efficiency.
The case of Indoor Positioning System (IPS) is promoted by recent smart devices,
which have evolved into a multimedia device with various sensors and optimised
connectivity. By sensing the device’s surroundings and inferring its context,
current IPS technology has proven its ability to provide stable and reliable indoor
localisation information. However, such a system is usually dependent on a high-density of infrastructure that requires expensive installations (e.g. Wi-Fi-based
IPS). To make a trade-off between accuracy and cost, considerable attention from
many researchers has been paid to the range of infrastructure-free technologies,
particularly exploiting the earth’s magnetic field (EMF).
EMF is a promising signal type that features ubiquitous availability, location
specificity and long-term stability. When considering the practicality of this
typical signal in IPS, such a system only consists of mobile device and the EMF
signal. To fully comprehend the conventional EMF-based IPS reported in the
literature, a preliminary experimental study on indoor EMF characteristics was
carried out at the beginning of this research. The results revealed that the positioning performance decreased when the presence of magnetic disturbance
sources was lowered to a minimum. In response to this finding, a new concept of
spatial segmentation is devised in this research based on magnetic anomaly (MA).
Therefore, this study focuses on developing innovative techniques based on spatial
segmentation strategy and machine learning algorithms for effective indoor
localisation using EMF.
In this thesis, four closely correlated components in the proposed system are
included: (i) Kriging interpolation-based fingerprinting map; (ii) magnetic
intensity-based spatial segmentation; (iii) weighted Naïve Bayes classification
(WNBC); (iv) fused features-based k-Nearest-Neighbours (kNN) algorithm.
Kriging interpolation-based fingerprinting map reconstructs the original observed
EMF positioning database in the calibration phase by interpolating predicted
points. The magnetic intensity-based spatial segmentation component then
investigates the variation tendency of ambient EMF signals in the new database to
analyse the distribution of magnetic disturbance sources, and accordingly,
segmenting the test site. Then, WNBC blends the exclusive characteristics of
indoor EMF into original Naïve Bayes Classification (NBC) to enable a more
accurate and efficient segmentation approach. It is well known that the best IPS
implementation often exerts the use of multiple positing sources in order to
maximise accuracy. The fused features-based kNN component used in the
positioning phase finally learns the various parameters collected in the calibration
phase, continuously improving the positioning accuracy of the system.
The proposed system was evaluated on multiple indoor sites with diverse layouts.
The results show that it outperforms state-of-the-art approaches and demonstrate
an average accuracy between 1-2 meters achieved in typical sites by the best
methods proposed in this thesis across most of the experimental environments. It
can be believed that such an accurate approach will enable the future of
infrastructure–free IPS technologies