INDOOR NAVIGATION ASSISTANCE SYSTEM USING BLUETOOTH

China will establish the Xihe System based on Beidou/GNSS, Mobile Communication, Internet and Satellite Communication system to realize the Seamless Outdoor and Indoor Positioning in Wide Area (SOIPWA) on personal mobile terminals. Indoor Positioning System (IPS) is one of the main components and effective supplement for indoor positioning signal resource to resolve the positioning signal coverage of blind areas in complicated indoor environment based on current 2G/3G communication network and existing equipment. According to the requirement, the IPS adopted the unified time-frequency reference which affects the accuracy of indoor positioning directly. This paper provides an acquisition method of high accuracy frequency source used in the indoor positioning signal supplement. Positioning signals from outdoor base stations are acquired by RF front end and tracked by the carrier loop and code loop adjusting. After that, positioning signals are locked and synchronized to extract the high accuracy frequency source used for IPS, which can be built with low cost and precisely integrated with an outdoor positioning system. In practical test, the method satisfies the need of the accuracy and stability in time-frequency reference for IPS with high frequency accuracy, quick start and low drift rate through adjusting high accuracy Voltage Controlled Oscillator (VCO) by Digital to Analog Converter (DAC) with the frequency difference result from the adaptive loop.

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.

Nowadays, location-based services are common in our daily lives. Traditional Global Positioning System (GPS) location can provide real-time location function in outdoor complex environments, but it is insufficient for indoor location. There are many indoor location technologies, such as ultrasound, Zigbee, RFID and WIFI. RFID location technology has attracted the attention of researchers due to its high precision and low cost. Most existing RFID location algorithms are based on RSSI (Received Signal Strength Indicator) measurement. When converting RSSI to distance, the inaccurate estimation of the path loss parameter may lead to large error. In order to reduce the deviation, this paper proposes a new RFID location algorithm. Specifically, the RSSI of the target tag is read in different directions of the antenna, and the position information is predicted by the general regression neural network, which is optimized by the optimization algorithm. The experimental results show the efficiency of our proposed algorithm.

The global positioning system is the most popular outdoor positioning system; however, it is inapplicable to indoor environments. In this study, we created a hybrid algorithm, which applies an artificial neural network to resolve the problems of indoor positioning and a swarm-intelligence algorithm to perform the time-consuming task of adjusting parameters. Experiment results demonstrate that the positioning accuracy of the proposed hybrid algorithm is equivalent to the exhaustive method with search times that are far shorter.

Despite the great attention that the research community has paid to the creation of novel indoor positioning methods, a rather limited volume of works has focused on the confidence that Indoor Positioning Systems (IPS) assign to the position estimates that they produce. The concept of estimating, dynamically, the accuracy of the position estimates provided by an IPS has been sporadically studied in the literature of the field. Recently, this concept has started being studied as well in the context of outdoor positioning systems of Internet of Things (IoT) based on Low-Power Wide-Area Networks (LPWANs). What is problematic is that the consistent comparison of the proposed methods is quasi nonexistent: new methods rarely use previous ones as baselines; often, a small number of evaluation metrics are reported while different metrics are reported among different relevant publications, the use of open data is rare, and the publication of open code is absent. In this work, we present an open-source, reproducible benchmarking framework for evaluating and consistently comparing various methods of Dynamic Accuracy Estimation (DAE). This work reviews the relevant literature, presenting in a consistent terminology commonalities and differences and discussing baselines and evaluation metrics. Moreover, it evaluates multiple methods of DAE using open data, open code, and a rich set of relevant evaluation metrics. This is the first work aiming to establish the state of the art of methods of DAE determination in IPS and in LPWAN positioning systems, through an open, transparent, holistic, reproducible, and consistent evaluation of the methods proposed in the relevant literature.

The car navigation service has opened through the global positioning system (GPS). People's lives have changed through car navigation services, and this service is indispensable nowadays. GPS provides reliable location accuracy in open space, but its location accuracy is degraded where multipath and other error factors are present. In particular, since GPS signals must be received in order to calculate the position, the navigation system cannot calculate the position in an indoor space such as underground parking lot, subway or tunnel where GPS signals are cut off. When car navigation is performed in an indoor parking lot where GPS signals are not transmitted, the user cannot calculate the current location, so the user has to go out by relying on the traffic sign because he does not know the current location. Also, when the user leave the parking lot, the car navigation system has problems. In an urban canyon, it is difficult to calculate accurate current position due to multipath. Because the route is calculated through the current location and route, the car navigation system provides the wrong route to the user. In addition, when entering a tunnel while using car navigation, the car navigation system calculates the position using the initial tunnel entry velocity estimated by the GPS. As the speed of the user increases or decreases, the position error also increases. Then the position suddenly changes when passing through the tunnel. And it is difficult to provide a proper guidance at the branch road or exit in the tunnel because of the positioning error. People are demanding a complete car navigation service, but the current navigation service provides the wrong route and position information to the user where the GPS signal quality is poor. In order to solve the above problem, in this paper, we have studied to improve the user's position accuracy by using LTE signal and mobile phone internal sensor. In general, observed time difference of arrival (OTDOA) technique is applied for the LTE based positioning. Because this method gives tens of meters of error, it is difficult to be used for the car navigation service. In our research, we estimate the position using the LTE signal strength. The difference from conventional fingerprinting method is that instead of using only current signal strength, the signal strength pattern accumulated while moving is utilized to estimate the user position. The signal strength increases as the user approach the access point (AP), and decreases as the user move away from the AP. Therefore, when a user moves and stores signal strength with respect to a specific AP, a pattern that could occur only in the corresponding region is saved. There are many similar candidates in the database when using only the current signal strength. However, when using a pattern, similar candidates exist only around the true position. And, when using several LTE signals, the positioning accuracy could be increased. Since the user receives multiple LTE signals, the pattern becomes surface. The surface is compared with the database, and the position with the highest correlation value is estimated as the current position. We call this method surface correlation (SC). In order to verify the performance of the proposed method, experiments were conducted in the Gangnam beltway tunnel in Seoul. The testbed consisted of three tunnel and total distance is almost 11.8 Km. In the tunnel, repeater and several antennas are installed to widen the area of the LTE signal. When an LTE signal is received by the vehicle, the largest signal strength is received every time when the vehicle passes the nearest antenna. As a result, for a specific LTE signal, when the vehicle has passed through the LTE signal region, a unique pattern with multiple peaks is generated. In order to estimate the position, we first estimate the position with the SC technique. Then, the estimated position is corrected by comparing the peak of the currently received pattern with the peak of the database. Through this method, the position was estimated with an error of less than 50meter in the tunnel. Conclusion In this research, the LTE signal strength based positioning technology is presented. Especially, this technology, SC, provides the positioning performance that could be used for the car navigation in the tunnel where GPS signal is blocked. We expected that user would be use the accurate car navigation service through this technology in the GPS challenging environment.

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.

Using visual odometry and inertial measurements, indoor and outdoor positioning systems can perform an accurate self-localization in unknown, unstructured environments where absolute positioning systems (e.g. GNSS) are unavailable. However, the achievable accuracy is highly affected by the residuals of calibration, the quality of the noise model, etc. Only if these unavoidable uncertainties of sensors and data processing can be taken into account and be handled via error propagation, which allows to propagate them through the entire system. The central filter (e.g. Kalman filter) of the system can then make use of the enhanced statistical model and use the propagated errors to calculate the optimal result. In this paper, we focus on the uncertaintiy calculation of the elementary part of the optical navigation, the template feature matcher. First of all, we propose a method to model the image noise. Then we use Taylor’s theorem to extend two very popular and efficient template feature matchers sum-of-absolute-differences (SAD) and normalized-cross-correlation (NCC) to get sub-pixel matching results. Based on the proposed noise model and the extended matcher, we propagate the image noise to the uncertainties of sub-pixel matching results. Although the SAD and NCC are used, the image noise model can be easily combined with other feature matchers. We evaluate our method by an Integrated Positioning System (IPS) which is developed by German Aerospace Center. The experimental results show that our method can improve the quality of the measured trajectory. Moreover, it increases the robustness of the system.

Visible light communication (VLC), have gain much interest in past decade in indoor communication. In particular, it is use for illumination and communication simultaneously. White light emitting diodes which are used as a transmitter for VLC are potential candidates for localization. Conventionally global positioning system (GPS) has very poor performance in indoor environments. Assisted global positioning system (AGPS) has positioning accuracy in few meter range. WLEDs can be used in small cell area inside the room hence indoor positioning can be measured accurately in centimeter. In this paper a WLEDs based indoor position system is investigated, the position was accurately confined in 11cm with low cost and on the shelf components.

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.

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.

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.

Indoor Propagation Investigation From a 2.4 GHz Waist Mounted Beacon