Indoor positioning technology assessment using analytic hierarchy process for pedestrian navigation services

Indoor positioning is one of the biggest challenges of many Location Based Services (LBS), especially if the target users are pedestrians, who spend most of their time in roofed areas such as houses, offices, airports, shopping centres and in general indoors. Providing pedestrians with accurate, reliable, cheap, low power consuming and continuously available positional data inside the buildings (i.e. indoors) where GNSS signals are not usually available is difficult. Several positioning technologies can be applied as stand-alone indoor positioning technologies. They include Wireless Local Area Networks (WLAN), Bluetooth Low Energy (BLE), Ultra-Wideband (UWB), Radio Frequency Identification (RFID), Tactile Floor (TF), Ultra Sound (US) and High Sensitivity GNSS (HSGNSS). This paper evaluates the practicality and fitness-to-the-purpose of pedestrian navigation for these stand-alone positioning technologies to identify the best one for the purpose of indoor pedestrian navigation. In this regard, the most important criteria defining a suitable positioning service for pedestrian navigation are identified and prioritised. They include accuracy, availability, cost, power consumption and privacy. Each technology is evaluated according to each criterion using Analytic Hierarchy Process (AHP) and finally the combination of all weighted criteria and technologies are processed to identify the most suitable solution

Survey of Radio Navigation Systems

At present, there is a growing demand for radio navigation systems, ranging from pedestrian navigation to consumer behavior analysis. These systems have been successfully used in many applications and have become very popular in recent years. In this paper we present a review of selected wireless positioning solutions operating in both indoor and outdoor environments. We describe different positioning techniques, methods, systems, as well as information processing mechanisms

Cooperatively Extending the Range of Indoor Localisation

̶Whilst access to location based information has been mostly possible in the\ud outdoor arena through the use of GPS, the provision of accurate positioning estimations and\ud broad coverage in the indoor environment has proven somewhat problematic to deliver.\ud Considering more time is spent in the indoor environment, the requirement for a solution is\ud obvious. The topography of an indoor location with its many walls, doors, pillars, ceilings\ud and floors etc. muffling the signals to \from mobile devices and their tracking devices, is one\ud of the many barriers to implementation. Moreover the cha racteristically noisy behaviour of\ud wireless devices such as Bluetooth headsets, cordless phones and microwaves can cause\ud interference as they all operate in the same band as Wi -Fi devices. The limited range of\ud tracking devices such as Wireless Access Point s (AP), and the restrictions surrounding their\ud positioning within a buildings’ infrastructure further exacerbate this issue, these difficulties\ud provide a fertile research area at present.\ud The genesis for this research is the inability of an indoor location based system (LBS) to\ud locate devices beyond the range of the fixed tracking devices. The hypothesis advocates a\ud solution that extends the range of Indoor LBS using Mobile Devices at the extremities of\ud Cells that have a priori knowledge of their location, and utilizing these devices to ascertain\ud the location of devices beyond the range of the fixed tracking device. This results in a\ud cooperative localisation technique where participating devices come together to aid in the\ud determination of location of device s which otherwise would be out of scope

A Meta-Review of Indoor Positioning Systems

An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

Cooperatively extending the range of indoor localisation

Whilst access to location based information has been mostly possible in the outdoor arena through the use of GPS, the provision of accurate positioning estimations and broad coverage in the indoor environment has proven somewhat problematic to deliver. Considering more time is spent in the indoor environment, the requirement for a solution is obvious. The topography of an indoor location with its many walls, doors, pillars, ceilings and floors etc. muffling the signals to from mobile devices and their tracking devices, is one of the many barriers to implementation. Moreover the characteristically noisy behaviour of wireless devices such as Bluetooth headsets, cordless phones and microwaves can cause interference as they all operate in the same band as Wi-Fi devices. The limited range of tracking devices such as Wireless Access Points (AP), and the restrictions surrounding their positioning within a buildings' infrastructure further exacerbate this issue, these difficulties provide a fertile research area at present. The genesis for this research is the inability of an indoor location based system (LBS) to locate devices beyond the range of the fixed tracking devices. The hypothesis advocates a solution that extends the range of Indoor LBS using Mobile Devices at the extremities of Cells that have a priori knowledge of their location, and utilizing these devices to ascertain the location of devices beyond the range of the fixed tracking device. This results in a cooperative localisation technique where participating devices come together to aid in the determination of location of devices which otherwise would be out of scope

Client-server-based LBS architecture: A novel positioning module for improved positioning performance

Permission to distribute obtained from publisher.This work presents a new efficient positioning module that operates over client-server LBS architectures. The aim of the proposed module is to fulfil the position information requirements for LBS pedestrian applications by ensuring the availability of reliable, highly accurate and precise position solutions based on GPS single frequency (L1) positioning service. The positioning module operates at both LBS architecture sides; the client (mobile device), and the server (positioning server). At the server side, the positioning module is responsible for correcting user’s location information based on WADGPS corrections. In addition, at the mobile side, the positioning module is continually in charge for monitoring the integrity and available of the position solutions as well as managing the communication with the server. The integrity monitoring was based on EGNOS integrity methods. A prototype of the proposed module was developed and used in experimental trials to evaluate the efficiency of the module in terms of the achieved positioning performance. The positioning module was capable of achieving a horizontal accuracy of less than 2 meters with a 95% confidence level with integrity improvement of more than 30% from existing GPS/EGNOS services

Neural Network-Based Ranging with LTE Channel Impulse Response for Localization in Indoor Environments

A neural network (NN)-based approach for indoor localization via cellular long-term evolution (LTE) signals is proposed. The approach estimates, from the channel impulse response (CIR), the range between an LTE eNodeB and a receiver. A software-defined radio (SDR) extracts the CIR, which is fed to a long short-term memory model (LSTM) recurrent neural network (RNN) to estimate the range. Experimental results are presented comparing the proposed approach against a baseline RNN without LSTM. The results show a receiver navigating for 100 m in an indoor environment, while receiving signals from one LTE eNodeB. The ranging root-mean squared error (RMSE) and ranging maximum error along the receiver's trajectory were reduced from 13.11 m and 55.68 m, respectively, in the baseline RNN to 9.02 m and 27.40 m, respectively, with the proposed RNN-LSTM.Comment: Submitted to ICCAS 202