Indoor Positioning System Using Artificial Neural Network With Swarm Intelligence

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.

Object positioning is a trending topic currently. The global positioning system (GPS) is the most common positioning method. Cars rely on GPS to navigate and plan routes to destinations. Unmanned aerial vehicles employ the GPS to assist flight and achieve cruise functions of automatic altitude and fixed point flights. However, the GPS fails when positioning is conducted indoors because GPS signals cannot be received indoors. Therefore, an indoor positioning system (IPS) should be used for indoor positioning. This paper mainly discusses the localization error of an ultra-wideband (UWB) indoor positioning system. A two-triangle positioning method was used to locate a target point separately through each triangle. Finally, values obtained using the two triangles were calculated using an algorithm to obtain a more accurate position.

Since the well-established navigation systems such as GPS are ineffective in indoor environments, research into developing novel indoor positioning technologies has emerged in recent years. While several technologies are being investigated, a practical and reliable indoor positioning system is yet to emerge. Indoor positioning using light signals holds a great potential to provide a reliable solution to the indoor positioning problem. This paper presents a comprehensive review of the available literature on light-based indoor positioning systems. The paper establishes a classification based on the design of various light-based indoor positioning (LIP) systems, and discusses their key components including hardware, software and algorithms. The limitations and challenges of the LIP systems are discussed in details, and current published solutions are presented. The review also provides a comparison between LIP systems and other optical positioning systems, and identifies the associated research gaps. Finally, based on the identified gaps, the future research directions in both LIP systems and other optical positioning systems are presented.

Global positioning system (GPS) based indoor positioning techniques generally utilize repeaters to amplify and retransmit the original GPS signals to indoor receivers. In this paper, a GPS repeater based indoor positioning approach is proposed. Unlike most repeater-based indoor positioning techniques in current literature, repeaters are not required to connect to an outdoor antenna with fixed length cables. Neither do repeaters need to be synchronized. Each repeater retransmits the GPS signals continuously to the indoor receivers without sequential switching. Assume more than one satellite is visible to four or more repeaters. At a measuring time instance, the range from a repeater to the user receiver is identical for any travelling satellite signal, which is repeated by this repeater. Incorporating this feature and comparing the ranges measured by means of repeated signals of different satellites, the receiver is able to identify the repeater, which repeats a given satellite signal. With the known coordinates of the identified repeaters and the ranges from repeaters to the receiver, then the receiver position can be found. To compromise the impact of the repeater signal processing time difference on positioning accuracy, this work proposes two methods to calculate the receiver position and fuses the results by Kalman filter. Furthermore, considering the general indoor positioning environments, altitudes of floors of a building are utilized to assist in improving the positioning performance. Simulation results with true repeater coordinate data are provided to verify the proposed approach.

Indoor Propagation Investigation From a 2.4 GHz Waist Mounted Beacon

In the past few years, a number of ideas have been proposed for indoor navigation systems. These ideas were not as widely implemented as outdoor positioning systems like GPS(Global Positioning Systems). We propose an indoor navigation assistance system using Bluetooth which is low cost and feasible to use in daily life. Our system enables users with handheld mobile devices to steer with ease through the indoor premises using the short range radio frequencies of Bluetooth. It also establishes user’s current location and the various paths leading to the destination. Dijkstra’s algorithm is used to determine the shortest path from the source to the required destination.

Over the last years, Indoor Localization Systems (ILS) evolved, due to the inability of Global Positioning Systems (GPS) to localize in indoor environments. A variety of studies tackle indoor localization with technologies such as Bluetooth Beacons and RFID that require costly installation, or techniques such as Google Wi-Fi/Cell DB and fingerprinting that leverage from the already existing Wi-FI and telecommunication infrastructure. Additionally, recent studies attempt to solve the same problem using Bio-Inspired techniques, such as Artificial Neural Networks (ANNs) and Deep Neural Networks (DNN). In this paper, we introduce a Multi-Objective Optimization Radiomap Modelling (MOO-RM) based ILS. The MOO-RM ILS divides the dataset into clusters using a K-Means algorithm and trains ANN models on the data of each cluster. The resulting models are fed into a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), which minimizes the required storage space and the localization error, simultaneously. Our experimental studies demonstrate the superiority of the proposed approach on real datasets of Wi-Fi traces with respect to various existing techniques.

In large-scale commercial facilities, location information has much importance for visitor, administrator, and staff of facilities in order to provide various services such as navigation, information inquiry and response, recommendation, marketing analysis, and so on. In particular, indoor positioning and navigation are expected to be useful for visitors of the facilities, and the information of positioning result can be utilized for the analysis of marketing and demand control for the supply side of the commercial facilities. However, it is not easy to achieve indoor positioning and navigation, because there are some crucial problems, for example, it is difficult to apply Global Positioning System (GPS) technologies in indoor environments. In this chapter, we introduce an indoor autonomous positioning and navigation system without using the GPS technology. Some attractive features of the proposed system are that it can work on a smartphone as an application alone without server communications and can provide continuous positioning results. In addition to it, the system is robust against disturbances in real target environments. We have carried out some experiments of the system in a real large-scale commercial facility called Yokohama Landmark Plaza. A result of experiments shows that the positioning result can be obtained with high accuracy and that system can produce continuous positioning results in a robust way against noise and changes of radio wave strength in real environments. These properties will work effectively for providing the services mentioned above.

The Global Positioning System (GPS) uses signals from satellites to specialised devices to detect an entity’s location on Earth’s surface. However, GPS is ineffective for indoor navigation due to high power consumption and signal obstruction by walls. This study proposes a low-cost, energy-efficient alternative using Bluetooth 4.0-enabled Bluetooth Low Energy (BLE) beacons to enable Indoor Positioning and guide users in confined spaces, such as libraries. Tiny, low-energy BLE beacons transmit Advertising Packets at regular intervals. In contrast, Anchor Nodes send and receive signals to construct a Received Signal Strength Indication (RSSI)-based signal-to-distance model, enabling triangulation-based localisation. The system monitors behaviour and environmental interaction through RSSI analysis and user interaction with Beacons. Proven in a library setting, the BLE-based system proved extremely accurate in guiding users to destinations, bridging GPS shortcomings at minimal energy expenditure and cost.

The Global Positioning System (GPS) is widely applied for outdoor positioning. However, GPS signals may degrade in an indoor environment. A nursing cart with a stable and accurate indoor positioning system inside is highly demanded in the hospital. The cart can be used for tracking the employees, or positioning movable medical devices. In this paper, we propose a Pedestrian Dead Reckoning (PDR) -based indoor positioning system with iBeacon calibrations. The proposed system can be implemented in resource- limited embedded device. To evaluate the property of the proposed system, real experiments under indoor environments have been conducted. The experimental results showed the effectiveness of the proposed approach. We also compared the impact of the number of iBeacons on positioning accuracy.

<p>The interest in indoor positioning systems has been rising in the last couple of decades. This interest was further accelerated by the availability of signals of opportunity. The introduction of signals of opportunity into the indoor environments has meant that these widely available technologies can be utilised to achieve higher levels of accuracy and precision for positioning in parking garages, airports, underground locations, large office buildings, and shopping centres. Indoor positioning can also be utilised for emergency services or for Building Information Models (BIM) for purposes such as construction sites or hospital building management. In addition to the need for indoor and outdoor positioning, there is a need to ensure seamless transitions in and out of these different environments. For example, building information from BIM could be utilised for navigation of BIM users that considers the architectural barriers (e.g., for wheelchairs, baby strollers, and transport of large equipment within the facility). For this, a positioning system capable of seamlessly transitioning in and out of buildings is needed. This paper proposes a multi-sensor and cooperative positioning method of seamless environment transition and presents the first architecture concept. For this purpose, Global Navigation Satellite System (GNSS) data are planned to be fused with Ultra Wide-Band (UWB), Inertial Measurement Unit (IMU), Wireless-Fidelity (Wi-Fi), visual camera and LiDAR (Light Detection and Ranging) data. All sensors required for such a positioning system, are already available on modern smartphones commonly used as low-cost platforms for pedestrian navigation and localisation. Newer smartphones are also equipped with LiDAR and UWB chips, which are a future signal of opportunity, just like Wi-Fi currently is. The transition to different environments will be determined based on the UWB anchors called identical anchors that will be used to transition from the global coordinate system for the outdoor environment to the local building environment for positioning in indoor environments. The proposed multi-sensor and cooperative positioning system is expected to be able to achieve decimetre-level accuracy.</p>

The Global Positioning System (GPS) has eventually become a common positioning technology. While GPS enabled many applications, satellite signals have yet to overcome many obstacles to enable indoor positioning. Meanwhile, due to the wide deployment of Wireless Local Area Networks (WLAN) in recent years, WLAN positioning algorithms have become popular for mobile device positioning in indoor environments. The most accurate WLAN positioning algorithms exploit the so-called fingerprinting concept which consists of two stages. In the offline stage (training), the Received Signal Strength Indicator (RSSI) from a set of available Access Points (AP) is measured for a number of reference locations and stored in a database. Due to the availability of many APs and the complex structure of indoor environments, this information is distinctive for each reference location and thus is called a position fingerprint. In the online stage (testing), a mobile device receives RSSIs from the APs and their fingerprint is compared to the fingerprints that are stored in the database for a best possible match. WLAN fingerprinting can provide high accuracy in indoor environments when there are many APs which ensure fingerprint uniqueness. This paper provides a novel approach to use WLAN multipath signals as possible fingerprints for positioning algorithms for applications with lower number of access points. The paper provides an initial study.

Indoor Positioning Systems (IPS) are an alternative to Global Positioning System (GPS) in those environments where its signal is attenuated. This is one of the main reasons why IPS has been the subject of much research over the last two decades and where different technologies and methods have been used. Among the technologies used in IPS are those that use radio frequency (RF) signals, such as Bluetooth Low Energy (BLE) or Wi-Fi. BLE is widely used in ubiquitous computing and in many applications of the Internet of Things (IoT) mainly due to its low power consumption and because it can provide advanced services to users. The aim of this paper is to provide an overview of the state of the art of BLE-based IPS including its main methods and algorithms.

Global Positioning System (GPS) is based on a satellite system. Much work has been carried out on a non-satellite positioning systems using the existing Global System of Mobile Communications (GSM) infrastructure. This leads to a hybrid GPS/GSM positioning systems in the paper. We purpose to adopt GSM for positioning via an integrated switch design with fuzzy logic, if GPS lose efficacy. This induces four main research features in this paper: (1) Designs with optimal-based Fourier analyzer to achieve error of GPS decrease is presented. (2) New applications of fuzzy set theory are presented to solve the problem of GPS accuracy. (3) An integrated switch of fuzzy logic is designed for selecting GPS or GSM positioning systems. (4) Discussions on various GSM positioning methods are adopted with different error and location parameters of environment.

The wireless indoor positioning system (IPS) has high demand due to wide applications such as asset and personal tracking, location analysis, and mapping. Global Positioning System (GPS) doesn't work within an indoor environment due to the non-line-of-sight (NLOS), to overcome this limitation we are using the most demanded technology Wi-Fi due to its high range, high data throughput with high availability and accessibility. The main problem is the positioning estimation accuracy in the Wi-Fi-based positioning system due to noise and interference in the environment. The received signal strength (RSSI) and signal quality (SQ) become low due to the multipath effects, reflections, signal loss, fluctuations, and other unknown factors. The objective of the proposed method is to reduce the fluctuations and the noise of the received signal and improve the signal strength and signal quality to increase the positioning estimation accuracy and distance estimation accuracy. RSSI smoothing technique of weighted moving average and feedback filter is used to make signal cleaner by removing the fluctuations and signal quality parameters SNR margin, frequency, and power noise to improve the signal quality. The trilateration technique is used to realize distance estimation and position estimation. To analyze the positioning estimation accuracy and distance estimation accuracy of the proposed method we implement the regression accuracy metrics based on mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). By doing this we achieved an accuracy below 0.5 meter.