scholarly journals Indore Navigation Mobile Application using Indore Positioning System (IPS)

2020 ◽  
Vol 2 (10) ◽  
pp. 5-7
Author(s):  
Anusha Sanampudi* ◽  
Keyword(s):  
Mobile Application ◽  
Smart Phone ◽  
Dead Reckoning ◽  
Steel Structures ◽  
Indoor Positioning ◽  
Indoor Navigation ◽  
Smart Phones ◽  
Positioning System ◽  
Shopping Malls ◽  
Indoor Positioning System

Indoor Positioning system (IPS) is the technology that is used to locate smart phones, people or other objects inside a building where Global Positioning System (GPS) doesn’t work or lack precision such as airports, underground locations, parking, multi-storey buildings etc…There is no fixed standard for implementing IPS rather it could be customized according to the location chosen. IPS in turn uses a number of technologies such as Wi-Fi, Bluetooth, Beacons, magnetic positioning, dead reckoning etc…Among the various technologies available studies prove that Magnetic localization provides a most efficient solution for Indoor positioning. Our paper focuses on building an indoor navigation mobile application for a retail store that allows users to search for a product and navigate them to the particular aisle in which the product is located. There by enabling the application to be location sensitive and context aware. In order to collect magnetic fingerprints and convert the obtained data into latitude and longitude values we make use of an API called IndoorAtlas, which helps in locating smart phones inside a building using the accelerometer, gyroscope, magnetometer and Bluetooth in a mobile. Magnetic localization is the concept where deflections of magnetic field from the steel structures inside the building will be captured by the magnetometer and other sensors within a mobile and that will be used to locate a smart phone inside a building. The same application could be utilized for various use cases such as Supermarkets & Hypermarkets, museums & galleries, Libraries, Hospitals, Airports & stations, Shopping malls, Exhibition and Conferences.

scholarly journals IBPS: iBeacons Based Improved Indoor Positioning System

2020 ◽  
Vol 8 (5) ◽  
pp. 3792-3797
Keyword(s):  
Research Work ◽  
Energy Resource ◽  
Smart Phone ◽  
Indoor Positioning ◽  
Small Scale ◽  
Positioning System ◽  
Human Organ ◽  
Proposed Model ◽  
Small Hand ◽  
Indoor Positioning System

Smartphone plays a key role in integrating the entire world into a small hand. This feature made these smartphones as another human organ of many people. One of the main feature in every smart phone is GPS which used to travel new places, to locate and find optimized way to reach their destination. As we aware GPS is an outdoor application, GPS location is not accurate in indoor and small scale areas. This leads to an advanced research to improve the accuracy in GPS positing for the benefit of indoor applications. This research proposes a new iBeacons based Improved Indoor Positioning System for indoor positing application using Bluetooth low energy (BLE) beacons. This model helps the mobile application to find the exact location at micro-level scale. The objective of this research work is to design a potable indoor positing system (IPS) for indoor applications with at least 100m accuracy with in the inbuilt energy resource limitations. The proposed model has been built and verified in all the aspects. The location accuracy and energy efficiency of the proposed model is compared and found better than the existing models

scholarly journals Leveraging Visual Place Recognition to Improve Indoor Positioning with Limited Availability of WiFi Scans

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3657 ◽  
Author(s):  
Michał R. Nowicki ◽  
Piotr Skrzypczyński
Keyword(s):  
Indoor Localization ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Environments ◽  
University Campus ◽  
Sources Of Information ◽  
Visual Place Recognition ◽  
Indoor Positioning System

WiFi-based fingerprinting is promising for practical indoor localization with smartphones because this technique provides absolute estimates of the current position, while the WiFi infrastructure is ubiquitous in the majority of indoor environments. However, the application of WiFi fingerprinting for positioning requires pre-surveyed signal maps and is getting more restricted in the recent generation of smartphones due to changes in security policies. Therefore, we sought new sources of information that can be fused into the existing indoor positioning framework, helping users to pinpoint their position, even with a relatively low-quality, sparse WiFi signal map. In this paper, we demonstrate that such information can be derived from the recognition of camera images. We present a way of transforming qualitative information of image similarity into quantitative constraints that are then fused into the graph-based optimization framework for positioning together with typical pedestrian dead reckoning (PDR) and WiFi fingerprinting constraints. Performance of the improved indoor positioning system is evaluated on different user trajectories logged inside an office building at our University campus. The results demonstrate that introducing additional sensing modality into the positioning system makes it possible to increase accuracy and simultaneously reduce the dependence on the quality of the pre-surveyed WiFi map and the WiFi measurements at run-time.

Design of Continuous Indoor Navigation System Based on INS and Wifi

2013 ◽  
Vol 303-306 ◽  
pp. 2046-2049 ◽  
Author(s):  
Yi Hu ◽  
Lei Sheng ◽  
Shan Jun Zhang
Keyword(s):  
Navigation System ◽  
Smart Phone ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Indoor Navigation ◽  
High Price ◽  
Position Information ◽  
Positioning Method ◽  
Indoor Navigation System ◽  
Navigation Service

The application of navigation, such as guidance of pedestrians, requires a certain accuracy of continuous outdoor and indoor positioning. In outdoor environments GPS system has proved to be effective. However in indoor it is challenging to control the accuracy within 2 to 3 meters. At present several approaches have been developed for indoor positioning, such as RFID. But they are mainly been implemented in professional areas, for general user such as tourists and visual incapable users it is difficult to take advantage of these technologies because of the high price of terminal and the navigation service covered area is extremely limited. In this paper, a new approach of indoor navigation method is proposed to solve the problems of traditional methods. It is based on INS and wifi positioning technology. As hardware, wifi receiver, smart phone built-in accelerometer and digital compass are selected and investigated. User’s indoor position is first estimated by dead reckoning method with INS navigation system and then be recalibrated by wifi position information. Several experiments performed in the test verified the effectiveness of this indoor continuous positioning method described in this paper.

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

2012 ◽  
pp. 36-41
Author(s):  
A.MOHAMED RIAS ◽  
R.SAMBATH KUMAR ◽  
G.SATHISH KUMAR ◽  
A. SIVAGAMI
Keyword(s):  
Sensor Network ◽  
Indoor Positioning ◽  
Positioning System ◽  
Shopping Malls ◽  
Network Localization ◽  
Real Time Tracking ◽  
The Impact ◽  
Indoor Positioning System ◽  
Tracking Of Humans ◽  
Installation Cost

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.

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