RETRACTED CHAPTER: Preliminary Study for Improving Accuracy of the Indoor Positioning Method Using Compass and Walking Speed

Location-based service is one of the most popular buzzwords in the field of U-city. Positioning a user is an essential ingredient of a location-based system on a U-city. For the outdoor positioning, GPS based practical solutions have been introduced. However, GPS measurement error is too big to be used for U-campus services because the size of a campus is relatively smaller than a city. We propose Relative-Interpolation Method in order to improve the correctness of outdoor positioning. Besides, indoor positioning is necessary for U-campus while GPS signal is not available inside buildings. For the indoor positioning, Cricket, Active Badge, and so on have been introduced. These methods require special equipments dedicated for positioning. Our method does not require such equipments because it determines the user’s position based on the receiver signal strength indicators (RSSI) from access points (AP) which are already installed for WLAN. The algorithm we are using for indoor positioning is a kind of finger prints method. However our algorithm builds a decision tree instead of a look-up table in the off-line phase. Therefore, our method is faster than existing indoor positioning methods in the real-time phase. We have integrated our indoor and outdoor positioning methods and implemented a prototype of our indoor-outdoor positioning method on a laptop. Our experimental results are discussed in this paper.

The accuracy of pedestrian positioning is helpful to ensure the pedestrian safety in both indoor and outdoor environments. Improve the accuracy of pedestrian positioning is a key research issue. In order to solve the problem that the indoor and outdoor pedestrian navigation is not continuous and the accuracy is low, a pedestrian seamless navigation and positioning method based on BDS/GPS/IMU is proposed. In outdoor environment, in order to improve the availability of dynamic positioning when the single-system observation geometry is not ideal, the key techniques such as the differential coordinates and time benchmark in BDS/GPS positioning are studied, and a method of eliminating time difference by the independent combination difference in the system is proposed. This method simplifies the operation steps and overcomes the current compatible positioning difficulties without the time difference between BDS and GPS. It can be seen that the more the number of visible satellites, the better the space geometric distribution. In the combined positioning experiment results of BDS and GPS, the number of visual satellites are about 6–8 when GPS is used alone. When using the combined system, the number of visible satellites increased to about 16. The increase in the number of visible satellites greatly improves the observation geometry. For the Position Dilution of Precision (PDOP), the maximum PDOP of the dual system is 2.7, which is significantly lower than that of the single system, and the observation geometry performance is greatly improved. In the effective positioning time, for BDS/GPS, the positioning accuracy of elevation (U) direction is better than 4 cm, and the positioning accuracy of North (N) and East (E) direction is better than 2 cm. Based on the analysis of pedestrian gait characteristics, a multi-condition constrained zero-velocity detection algorithm is proposed. For the error of the inertial sensor error is accumulated over the time, the zero velocity update (ZUPT) algorithm is implemented to correct the cumulative errors by using to the designed extended Kalman filter (EKF) with the velocity and angular velocity information as the measurements. The results show that the accuracy of dual-mode positioning system of BDS compatible with GPS is better than the single-mode GPS positioning, the outdoor position accuracy can reach centimeter level, and under ZUPT compensation the indoor error ratio is 1%, which can achieve more accurate pedestrian seamless navigation.

Location based service (LBS) cannot be realized unless the location of the user is available. For indoor LBS, indoor positioning must be utilized and many researchers have been working on indoor positioning and tracking. For example, extended Kalman filter (EKF) was exploited in Bluetooth based indoor positioning. Nowadays, WLAN (wireless local area network) is available virtually everywhere. Thus, WLAN based indoor positioning and tracking is more economical than Bluetooth based ones. The main purpose of this paper is to propose a new WLAN based EKF indoor tracking method by extending existing Bluetooth based EKF positioning method. Experimental results comparing our method with other previous methods are discussed.

Indoor navigation is becoming a most wanted application especially on the background of the wide availability of powerful personal mobile devices and new methods for indoor positioning. Existing approaches do seldom incorporate that people can move freely through e.g. big halls and are not constrained to specific lanes as vehicles are on road networks. Thereby these approaches can only approximate shortest paths and cannot benefit from possible highly accurate indoor positioning methods. In this chapter we show how the concept of visibility graphs can be applied to indoor routing and how it results in highly accurate shortest paths. We demonstrate how any accurate position can be incorporated in the automatically constructed graph. Furthermore we show how the knowledge that different levels of a building are usually sparsely interconnected can be used to speed up the well-known shortest path algorithm A* by introducing a new heuristic. In experiments we show that our approach needs 29 % less run-time than a standard A*-algorithm.

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

One of the most recent and popular tourism application is the virtual visits (virtual tour guide) which are based on Augmented/Virtual Reality technology (AR/VR). Such systems suffer from the lack of precise indoor geolocation of visitors within the cultural heritage site. This issue can be resolved by using smartphone (and tablets) embedded sensors, which can catch a huge number of data on visitor behaviour (acceleration, orientation, etc.). This data can be fused using dedicated methods (Extended Kalman Filter (EKF), Particle Filter, etc.) in order to estimate visitor position. However, such algorithms are highly dependent on: sampling time, sensors technologie, number of used sensors, etc. With this kind of solution the error in position estimation growth with the covered distance. In this paper, we present an hybrid solution for reducing error in time without additional infrastructure such Wifi, etc. For this, we present an indoor geolocation solution based on smartphone inertial sensors and earth magnetic field. The proposed method is divided into three phases: heading estimation using 3 sensors (accelerometer, compass and gyroscope) with an Extended Kalman Filter, steps detection and step length estimation, error correction using fingerprinting. The proposed solution is implemented on a smartphone (Samsung-Galaxy S7 based on Android OS) and maintain the error under of 1.5m.

The Indoor positioning based on the ZigBee received signal strength index has attracted more and more researchers’ attention and, due to its low cost, low hardware power consumption and easy implementation. However, because of multipath effects and shadow effects, traditional indoor positioning algorithms cannot obtain good positioning effects. In order to improve the accuracy of ZigBee indoor positioning, this paper proposes an indoor positioning algorithm of annealing algorithm (SA) and genetic algorithm (GA) optimized neural network (SAGA-BP), and the superiority of this algorithm is proved through simulation and experiment. First, establish the position relationship between the received signal strength indicator(RSSI) and the target position, and arrange the node network structure model to collect signals to establish a fingerprint database. Then use the mechanism of the annealing algorithm combined with the genetic algorithm to optimize the initial weight and initial threshold of the neural network algorithm, so that it can quickly jump out of the local optimal solution and achieve high-precision positioning. Experiments have proved the effectiveness of the positioning algorithm. Compared with BP and GA-BP algorithms, SAGA-BP positioning algorithm has an average error of 0.75m for RSSI signals after acquisition and processing, and an average error of BP positioning algorithm is 1.24m. The average error of GA-BP algorithm is 0.98m. Thus, the SAGA-BP algorithm has higher positioning accuracy.

Indoor Positioning Systems are gaining market momentum, mainly due to the significant reduction of sensor cost (on smartphones or standalone) and leveraging standardization of related technology. Among various alternatives for accurate and cost-effective Indoor Positioning System, positioning based on the Magnetic Field has proven popular, as it does not require specialized infrastructure. Related experimental results have demonstrated good positioning accuracy. However, when transitioned to production deployments, these systems exhibit serious drawbacks to make them practical: a) accuracy fluctuates significantly across smartphone models and configurations and b) costly continuous manual fingerprinting of the area is required. The developed Indoor Positioning System Copernicus is a self-learning, adaptive system that is shown to exhibit improved accuracy across different smartphone models. Copernicus leverages a minimal deployment of Bluetooth Low Energy Beacons to infer the trips of users, learn and eventually build tailored Magnetic Maps for every smartphone model for the specific indoor area. In a practical deployment, after each trip execution by the users we can observe an increase in the accuracy of positioning.

Using smartphones for accurate indoor localization opens a new frontier of mobile services, offering enormous opportunities to enhance users' experiences in indoor environments. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), there are several stringent requirements for an indoor localization system: highly accurate that can differentiate massive users' locations (foot-level); no additional hardware components or extensions on users' smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. WiFi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accuracy. In this paper, we propose a practical and accurate solution that fills the long-lasting gap of smartphone-based indoor localization. Specifically, we design and implement an indoor localization ecosystem Guoguo. Guoguo consists of an anchor network with a coordination protocol to transmit modulated localization beacons using high-band acoustic signals, a realtime processing app in a smartphone, and a backend server for indoor contexts and location-based services. We further propose approaches to improve its coverage, accuracy, and location update rate with low-power consumption. Our prototype shows centimeter-level localization accuracy in an office and classroom environment. Such precise indoor localization is expected to have high impact in the future ILBS and our daily activities.

Ultra-wideband (UWB)-based indoor localization systems are characterized by high accuracy, strong anti-interference capabilities, low power consumption, and excellent scalability, making them a prominent technology in indoor positioning. Determining accurate anchor node positions is a prerequisite for achieving high-precision tag localization. However, this process often requires significant manual effort and time and may not be feasible for certain scenarios involving manual measurements. To address this challenge, this paper proposes an accurate and convenient anchor self-localization method. The proposed method requires only TOF and PDOA data from UWB measurements without relying on external sensor data, and can realize single-anchor localization without the need to introduce multiple anchors to construct a coordinate system. Subsequently, the iterative reweighted least squares (IRLS) algorithm is employed to centrally process measurement data from each anchor, yielding optimized position estimates. In simulation experiments, we analyzed the impact of ranging noise and AOA estimation errors on localization accuracy. Furthermore, we conducted validation experiments, and the results demonstrated that the proposed method effectively achieves accurate self-localization of UWB anchor nodes.

This paper proposes a BLE used indoor positioning method, which combines the fingerprint method with particle filter, for improving the estimation accuracy. Particle filter can be implemented by creating a likelihood function. The proposed method makes fingerprint map based on RSSI from BLE beacon, and creates particle filter which estimates indoor position from observed RSSI. The experiment results show the effectiveness of the proposed method.

Indoor localization using the magnetic fingerprint collected by the embedded sensors of smartphone has been under constant improvement with the widespread popularity of the smartphone. Most of the current systems rely on the Particle Filter (PF) or the Kalman Filter (KF) to combine the Pedestrian Dead Reckoning (PDR) with the magnetic or WiFi fingerprint for improving their accuracy. In this paper, a system using the PDR and the magnetic fingerprint is proposed for indoor localization, which is based on the combination of the Particle Filter (PF) and the Extend Kalman Filter (EKF). The system includes the Pedestrian Dead Reckoning (PDR) module and the magnetic fingerprint matching module. In particular, the hybrid indoor positioning algorithm which combines the Particle Filter (PF) and the Extend Kalman Filter (EKF) is proposed in the magnetic fingerprint matching module for the fusion of the results of the Pedestrian Dead Reckoning module and the magnetic fingerprint. This hybrid indoor positioning algorithm is the key component which can reduce the computation of the Particle Filter effectively and solve the inherent blindness and particles degeneration problem. The obtained results in the real scenarios show that our fusion system achieves better results than the widely adopted system in which the Particle Filter (PF) or the Kalman Filter (KF) is used. The evaluation shows the system achieves a localization accuracy about 1-2m on average in a large building.

Positioning systems are used in a wide range of applications which require determining the position of an object in space, such as locating and tracking assets, people and goods; assisting navigation systems; and mapping. Indoor Positioning Systems (IPSs) are used where satellite and other outdoor positioning technologies lack precision or fail. Ultra-WideBand (UWB) technology is especially suitable for an IPS, as it operates under high data transfer rates over short distances and at low power densities, although signals tend to be disrupted by various objects. This paper presents a comprehensive study of the precision, failure, and accuracy of 2D IPSs based on UWB technology and a pseudo-range multilateration algorithm using Time Difference of Arrival (TDoA) signals. As a case study, the positioning of a area, four anchors (transceivers), and one tag (receiver) are considered using bitcraze’s Loco Positioning System. A Cramér–Rao Lower Bound analysis identifies the convex hull of the anchors as the region with highest precision, taking into account the anisotropic radiation pattern of the anchors’ antennas as opposed to ideal signal distributions, while bifurcation envelopes containing the anchors are defined to bound the regions in which the IPS is predicted to fail. This allows the formulation of a so-called flyable area, defined as the intersection between the convex hull and the region outside the bifurcation envelopes. Finally, the static bias is measured after applying a built-in Extended Kalman Filter (EKF) and mapped using a Radial Basis Function Network (RBFN). A debiasing filter is then developed to improve the accuracy. Findings and developments are experimentally validated, with the IPS observed to fail near the anchors, precision around , and accuracy improved by about for static and for dynamic measurements, on average.

Modern smartphones and location-based services and apps are poised to transform our daily life. However, current smartphone-based localization solutions are limited mainly to outdoor, mostly missing practical, robust and accurate indoor location solutions. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), e.g., step-by-step navigation for the Blind and visually impaired, there are several stringent requirements: highly accurate (foot-level); no additional hardware components or extensions on users’ smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. Wi-Fi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accuracy. In this paper, we propose a practical and accurate solution that fills the long-lasting gap of smartphone-based fine-grained indoor localization. Specifically, we design and implement an indoor localization ecosystem Guoguo. Guoguo consists of an anchor network with a coordination protocol to transmit modulated localization beacons using high-band acoustic signals, a realtime processing app in a smartphone, and a backend server for indoor contexts and location-based services. We further propose approaches to improve its coverage, accuracy, and location update rate with low-power consumption. Our prototype shows centimeter-level localization accuracy in several typical indoor environments. Such precise indoor localization is expected to have high impact in the future ILBS and our daily activities.

Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.