MiPOS - the Mote Indoor Positioning System

In the past few years, there have been huge research efforts into ubiquitous and context aware platforms that offer a user a custom level of service based on some known local parameters. The utility of such systems is greatly enhanced if a physical locational area can be determined. Recently, hybrid devices have been developed combining low power micro controllers with short range FM radio transceivers. Some location identification work has been carried out with these systems such as the Matrix Pencil approximation technique[8],however most of these all provide information for an ideal square area with no RF obstructions.Here we present MiPOS, a scalable locationing system based on the MICA mote[11] family of devices.The design goal of MiPOS is to provide a low-power, scalable, distributed locationing system suited to an indoor (office) environment.During the presentation of this paper we will highlight solutions in the areas of security, radio and network management and power awareness for a hybrid context aware wearable locationing device

Directional GPS antenna for indoor positioning applications

In this paper, a directional GPS antenna for L1 frequency - 1575 MHz - with RHCP and a high directive gain is proposed for indoor positioning applications. The proposed antenna is made of a standard off the shelf GPS patch antenna with an additional conical reflector to enhance the gain and the beamwidth of the antenna. The angle of the cone reflector is optimized by HFSS 11 software. Finally, the cone is fabricated, integrated with the patch antenna and measured. The measurement results show that the antenna with the reflector has a 9 dBi gain and a beamwidth of 60 degrees with an axial ratio of 1 dB which agrees well with simulation results

STATISTICAL ANALYSIS OF WSN BASED INDOOR POSITIONING LOCALIZATION SCHEMES WITH KALMAN FILTERING

Wireless Sensor Network (WSN) is used for determining the Indoor Positioning of objects and persons since recent years. WSN has been implemented in indoor positioning applications such as real time tracking of humans/objects, patient monitoring in health care, navigation, warehouses for inventory monitoring, shopping malls, etc. But one of the problems while implementing WSN in Indoor positioning system is to ensure more coverage large number of sensors must be deployed which increases the installation cost. So in this paper, we have used MATLAB GUI named Sensor Network Localization Explorer to analyze the impact of node density on indoor positioning localization schemes. Later we have integrated the Kalman filter with the indoor positioning system to increase the reliability and reduce the localization error of the system with lesser number of nodes

iBeacon-based indoor positioning system: from theory to practical deployment

Developing an indoor positioning system became essential when global positioning system signals could not work well in indoor environments. Mobile positioning can be accomplished via many radio frequency technology such as Bluetooth low energy (BLE), wireless fidelity (Wi-Fi), ultra-wideband (UWB), and so on. With the pressing need for indoor positioning systems, we, in this work, present a deployment scheme for smartphone using Bluetooth iBeacons. Three main parts, hardware deployment, software deployment, and positioning accuracy assessment, are discussed carefully to find the optimal solution for a complete indoor positioning system. Our application and experimental results show that proposed solution is feasible and indoor positioning system is completely attainable

Indoor Positioning System

The purpose of our project is to develop a complementary system to the current GPS with a focus on indoor localization and navigation. The current need for localization extends beyond what GPS can provide in today’s state of technology. Radio signals used in the global system are vast but weak, unable to penetrate obstacles and buildings in high density, populous areas of the world. Our system is designed to solve this problem by implementing an Indoor Localization System using a stronger ultra-wideband signal in the frequency spectrum. At a high level, the system is modeled after the architecture of the global positioning system by utilizing anchors as the satellites and tags as the receivers. With the use of up to date cloud technology, an end-to-end system is created through the Internet of Things with the inclusion of information security and a fully developed front-end user interface. The packaging is encapsulated within a miniature PCB design at a low cost, aimed as a plug-and-play integration within other systems in need of indoor detection. Applications of our IPS design include domains such as navigation (room-to-room assistance in a building), national defense (search and rescue operations, underground tracking, surveillance), commercialized zones (indicators for specific products on shelf, asset tracking in warehouses), and robotics (autonomous vehicles, drone detection). We demonstrate that all the components mentioned are essential to effectively carry out successful indoor localization with a focus on user flexibility and efficiency in response. We are able to use the system to enable an indoor drone show

A Simple Geometric-Aware Indoor Positioning Interpolation Algorithm Based on Manifold Learning

Interpolation methodologies have been widely used within the domain of indoor positioning systems. However, existing indoor positioning interpolation algorithms exhibit several inherent limitations, including reliance on complex mathematical models, limited flexibility, and relatively low precision. To enhance the accuracy and efficiency of indoor positioning interpolation techniques, this paper proposes a simple yet powerful geometric-aware interpolation algorithm for indoor positioning tasks. The key to our algorithm is to exploit the geometric attributes of the local topological manifold using manifold learning principles. Therefore, instead of constructing complicated mathematical models, the proposed algorithm facilitates the more precise and efficient estimation of points grounded in the local topological manifold. Moreover, our proposed method can be effortlessly integrated into any indoor positioning system, thereby bolstering its adaptability. Through a systematic array of experiments and comprehensive performance analyses conducted on both simulated and real-world datasets, we demonstrate that the proposed algorithm consistently outperforms the most commonly used and representative interpolation approaches regarding interpolation accuracy and efficiency. Furthermore, the experimental results also underscore the substantial practical utility of our method and its potential applicability in real-time indoor positioning scenarios

PINSPOT: An oPen platform for INtelligent context-baSed Indoor POsiTioning

This work proposes PINSPOT; an open-access platform for collecting and sharing of context, algorithms and results in the cutting-edge area of indoor positioning. It is envisioned that this framework will become reference point for knowledge exchange which will bring the research community even closer and potentially enhance collaboration towards more effective and efficient creation of indoor positioning-related knowledge and innovation. Specifically, this platform facilitates the collection of sensor data useful for indoor positioning experimentation, the development of novel, self-learning, indoor positioning algorithms, as well as the enhancement and testing of existing ones and the dissemination and sharing of the proposed algorithms along with their configuration, the data used, and with their results