Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.

Urban environments present major challenges to the global navigation satellite system (GNSS) positioning to estimate the position of a self-driving vehicle. GNSS signals are often obstructed by buildings and other obstacles, leading to reflected and diffracted signals, which cause major positioning errors and known as line-of-sight (LOS) and non-line-of-sight (NLOS) multipath errors. Particularly, real-time kinematic (RTK)-GNSS, which can estimate the position in the centimeter-level accuracy, cannot estimate the precise position when there exist NLOS multipath errors. If we use NLOS GNSS code and carrier phase multipath measurements into the RTK-GNSS algorithm, carrier phase integer ambiguity cannot be solved, or wrong integer ambiguities are estimated. As a result, integer ambiguity resolved to fix solutions cannot be estimated, or wrong fix solutions which have large position error are estimated. That is a major barrier to using RTK-GNSS in autonomous driving. In this paper, we propose a novel NLOS detection and exclusion technique to enhance existing RTK-GNSS positioning in urban areas using only GNSS measurements alone. The key idea behind the proposed technique is that the NLOS pseudorange error will always be a positive value. NLOS pseudorange is longer than the true range, which is the distance between the GNSS antenna and satellite, due to the time delay caused by reflection and diffraction. The distribution of the NLOS error is not along with a Gaussian distribution. We use this phenomenon to detect the NLOS signals. We propose a technique to detect and eliminate the NLOS multipath using double-differenced pseudorange residuals (DD-PR). In the RTK-GNSS, DD GNSS measurement between the rover and base station is typically used. Here, we compute the DD-PR to detect the NLOS signals. In order to compute the DD-PR, the true position of the vehicle must be determined in advance. To solve this contradiction, we use a particle filter. The particle filter generates the position hypotheses, which is called as “particle”. These particles are used as the position candidate for estimating the DD-PR. When the DD-PR can be calculated at each particle position, we detect the large positive errors in DD-PR as NLOS signals. Then we compute the integer ambiguity of only the LOS DD carrier phase to estimate the precise vehicle position. The flow of the proposed technique is as follows: (1) The hypothetical user positions are created as particles, where the state vectors of these particles are 3D positions, x, y, and z. The initial particles are distributed based on the solution of the normal GNSS point positioning and its variance. (2) In the prediction step of the particle filter, the particle state in the next time step is estimated using the velocity of each particle computed from GNSS Doppler measurements. (3) Next, we compute the DD-PR at each particle location. The receiver clock bias, satellite clock bias, ionosphere error, and troposphere error in GNSS pseudorange are canceled through DD computation. As a result, DD geometrical range between receiver and satellite and DD multipath errors remain in the DD measurement. We compute DD-PR using the particle position. As a result, the only multipath error remains if the particle is close to the true position. NLOS multipath error is always positive value; we generate the subset of the LOS measurement hypothesis at each particle using a simple threshold test. (4) Next, we compute the RTK-GNSS position solution using the generated subset of the LOS measurement hypothesis at each particle location. It is expected that the RTK-GNSS fix solution can be calculated if the particle location is close to the true position because the NLOS signal has been eliminated. By contrast, if the particle is far from true position, the integer ambiguity cannot be solved because of using the incorrect subset of LOS measurements. The likelihood of each particle is computed from a distance between the particle location and the RTK-GNSS fix solution. The particle that is closest to the true position has a higher likelihood than the other particles. The probabilistic weight of each particle is then updated based on its likelihood. (5) Finally, all the particles are resampled based on their new weights. Particles that are sufficiently close to the true position survive this resampling step, and the average position of the remaining particles are employed as the final estimated user position. To confirm the effectiveness of the proposed technique, a kinematic positioning test was performed in Shinjuku, Tokyo, Japan. From the several positioning tests, the proposed technique could dramatically improve the availability and accuracy of RTK-GNSS fix solutions in the dense urban canyon where there exist a lot of NLOS multipath signals.

People have to move between indoor and outdoor frequently in city scenarios. The global navigation satellite system signal cannot provide reliable indoor positioning services. To solve the problem, this article proposes a seamless positioning system based on an inverse global navigation satellite system signal, which can extend the global navigation satellite system service into the indoor scenario. In this method, a signal source is arranged at a key position in the room, and the inverse global navigation satellite system signal is transmitted to the global navigation satellite system receiver to obtain a preset positioning result. The indoor positioning service is continued with the inertial navigation system after leaving the key position. The inverse global navigation satellite system seamless positioning system proposed in this article can unify indoor and outdoor positioning using the same receiver. The receiver does not need to re-receive navigation information when the scene changes, which avoids the switching process. Through the design of signal layer coverage, the receiver is in a warm start state, and the users can quickly fix the position when the scenario changes, realizing quick access in a true sense. This enables the ordinary commercial global navigation satellite system receiver to obtain indoor positioning capability without modification, and the algorithm can perform accurate positioning indoors and outdoors without switching.

Navigation is one of the most essential human needs. People need to know their location anywhere and everywhere, indoors and outdoors, for example for tracking children and older people who are in extreme need of care or for providing a precise indoor location of calls to 911. The latter is especially important since more than seventy percent of calls are generated indoors. Other important fields are tracking of personnel in possibly dangerous environment, LBS/LBA applications, and social networking that rely on indoor localization. Unfortunately, broadly used satellite navigation receivers work perfectly only under open sky. Operating indoors, GPS/GNSS receivers suffer from signal attenuation when satellite signals propagate through a roof and walls of a building and from the multipath due to their reflection. As a result, accurate GNSS-based position fix is almost impossible in most indoor conditions. We present Coursa Venue solution that was developed by TDK-Invensense for infrastructure-less indoor positioning on commercial smartphones. The solution consists of two major parts: cloud-based software for fingerprinting and mobile applications for Android and iOS that provide real-time blue dot positions to users. TDK-Invensense's approach to real-time indoor positioning is based on fusion of multiple technologies. Measurements from such smartphone sensors as IMU (3D accelerometer, gyroscope), a magnetic field sensor (3D magnetometer), WiFi and BLE modules are used for hybrid indoor positioning in the navigation engine. Particle filtering is used as the fusion engine. Indoor navigation software uses such technologies as PDR, geomagnetic fingerprinting, Wi-Fi/BLE fingerprinting, and, optionally, map matching. TDK-Invensense' ‘s PDR provides prediction of user relative movement regardless of orientation and misalignment of a smartphone, whereas magnetic and radio fingerprinting serves for correction of inertial sensors error. The cloud-based component can create magnetic and radio fingerprint databases using either data collected by designated surveyors who walked inside a venue by predetermined routes, or crowdsourced data from users of a real-time mobile application collected during their everyday activity. This paper discusses the architecture of the Coursa Venue solution and demonstrates its positioning results in several venues with comparison to ground truth paths to provide statistical assessment and key performance indices.

In sport science, Global Navigation Satellite Systems (GNSS) are frequently applied to capture athletes' position, velocity and acceleration. Application of GNSS includes a large range of different GNSS technologies and methods. To date no study has comprehensively compared the different GNSS methods applied. Therefore, the aim of the current study was to investigate the effect of differential and non-differential solutions, different satellite systems and different GNSS signal frequencies on position accuracy. Twelve alpine ski racers were equipped with high-end GNSS devices while performing runs on a giant slalom course. The skiers' GNSS antenna positions were calculated in three satellite signal obstruction conditions using five different GNSS methods. The GNSS antenna positions were compared to a video-based photogrammetric reference system over one turn and against the most valid GNSS method over the entire run. Furthermore, the time for acquisitioning differential GNSS solutions was assessed for four differential methods. The only GNSS method that consistently yielded sub-decimetre position accuracy in typical alpine skiing conditions was a differential method using American (GPS) and Russian (GLONASS) satellite systems and the satellite signal frequencies L1 and L2. Under conditions of minimal satellite signal obstruction, valid results were also achieved when either the satellite system GLONASS or the frequency L2 was dropped from the best configuration. All other methods failed to fulfill the accuracy requirements needed to detect relevant differences in the kinematics of alpine skiers, even in conditions favorable for GNSS measurements. The methods with good positioning accuracy had also the shortest times to compute differential solutions. This paper highlights the importance to choose appropriate methods to meet the accuracy requirements for sport applications.

With the rapid development of the economy and information services, there is an increasing demand for indoor positioning. However, Global Navigation Satellite System (GNSS) signals face difficulties in penetrating buildings, making indoor positioning challenging. As a complementary solution to outdoor GNSS signals, pseudolites have shown promising results in various scenarios, and have become one of the research hotspots in the field of indoor positioning. Previous studies on indoor positioning with pseudolites have focused on issues such as initial position determination and carrier phase ambiguity resolution. However, most of these studies have remained in the research stage due to data post-processing and complex calculations. To overcome the limitations associated with the aforementioned factors, this paper proposes a real-time indoor positioning method that utilizes only a smartphone equipped with a built-in GNSS chip. By effectively obtaining pseudolite signal information, the smartphone establishes a simple model based on the distance and received power between the pseudolite and the smartphone. Kalman filtering is employed to mitigate the effects of multipath and occlusion. This simple and low-complexity model enables fast estimation of the user's distance from the pseudolite, thereby facilitating indoor positioning services. In this paper, the effectiveness and low complexity of the method are verified through data collection and experiments in the actual scene of the underground garage. The valuation results show that the root mean square error (RMSE) of its navigation and positioning is better than 3 m, which can meet the needs of daily life applications.

Due to the limitation of Global Navigation Satellite System (GNSS), it cannot work properly without direct line of sight to GNSS satellites such as in urban and indoor environments. Visual processing such as Visual Odometry (VO) and Structure-From-Motion (SFM) is usually used for the indoor navigation or localization. SFM was introduced in 1990 and had been successfully used for reconstruction of unordered images, and in the process of reconstruction, the SFM would estimate the position of the camera for each image at the same time. This paper introduces three most common SFM tools and suggests a positioning method for small UAVs equipped with vision cameras in indoor environment. Each stage of the SFM algorithms is studied to analysis the impact on the final positioning accuracy. The output from different feature detection, feature matching, initial pair setting, and the reconstructions are compared with an indoor reference localization system based on infrared cameras. The objective of this paper is to suggest a positioning and navigation method for small UAVs equipped with vision cameras in indoor environment. In this research, we will study the impact of different stages of SFM algorithm on the final positioning accuracy. Several objects such as chairs or boxes would be placed randomly in a lab room to build a feature-based environment without any special landmarks. In our experiment, an indoor localization system based on infrared cameras will be used as the reference navigation result to evaluate the final performance of the suggested algorithm.

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

The Global Navigation Satellite System (GNSS) is a widely used positioning technique. Computational efficiency is crucial to applications such as real-time GNSS positioning and GNSS network data processing. Many researchers have made great efforts to address this problem by means such as parameter elimination or satellite selection. However, parameter estimation is rarely discussed when analyzing GNSS algorithm efficiency. In addition, most studies on Kalman filter (KF) efficiency commonly have defects, such as neglecting application-specified optimization and limiting specific hardware platforms in the conclusion. The former reduces the practicality of the solution, because applications that need such analyses on filters are often optimized, and the latter reduces its generality because of differences between platforms. In this paper, the computational cost enhancement of replacing the conventional KF with the information filter (IF) is tested considering GNSS application-oriented optimization conditions and hardware platform differences. First, optimization conditions are abstracted from GNSS data-processing scenarios. Then, a thorough analysis is carried out on the computational cost of the filters, considering hardware–platform differences. Finally, a case of GNSS dynamic differencing positioning is studied. The simulation shows that the IF is slightly faster for precise point positioning and much faster for the code-based single-difference GNSS (SDGNSS) with the constant velocity (CV) model than the conventional KF, but is not a good substitute for the conventional KF in the other algorithms mentioned. The real test shows that the IF is about 50% faster than the conventional KF handling code-based SDGNSS with the CV model. Also, the information filter is theoretically equivalent to and can produce results that are consistent with the Kalman filter. Our conclusions can be used as a reference for GNSS applications that need high process speed or real-time capability.

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

Commonly used Global Navigation Satellite Systems (GNSS) are inappropriate as Location Based Services (LBS) in indoor environment. Therefore research teams are developing different systems, which can be used as a suitable alternative. One of options is to use Inertial Navigation System (INS) which consists of inertial sensors and mathematic procedures. This concept has been known for a long time, but with arrival of Microelectro Mechanical System (MEMS) INS found wide use. Smartphones with inertial sensors, such as accelerometers and gyroscopes, allow us to use them as input devices for Pedestrian Dead Reckoning (PDR). In this paper we present PDR by using smartphone sensors. They can be classified as low-cost Inertial Measurement Unit (IMU), and have been compared with more precise and expensive Xsens IMU. Accuracy of inertial sensors has increased in the past few years, but they still cannot alone provide proper accuracy because of many negative effects, such as heading drift due to gyroscope bias. Particle Filter (PF) has been successfully used with map constraints to increase the accuracy of proposed location system. Presented results show that low-cost smartphone IMU combined with PF can be applicable as proper navigation system.

Location tracking is being increasingly used across many applications. While GPS is the most widely used location tracking technology, it is unavailable in many environments such as indoors and underground. Local positioning systems (LPS) that use time of arrival based ranging can provide high accuracy location tracking for many applications. Tracking location using range measurements is a non-linear state estimation problem and the measurement noise is often non-Gaussian in environments where LPS are typically used. Hence a particle filter is an appropriate state estimator for location tracking in LPS. Particle filters are computationally complex and have a serial bottleneck that prevents straightforward parallel implementation. In this paper we present a parallel architecture for the particle filter that can be efficiently implemented in a field programmable gate array or a fixed-point digital signal processor. We show that processing can be divided into up to twenty parallel particle filters to massively increase the processing speed. Mixing between the filters is essential and we present a new algorithm for this that minimises computational complexity and memory bandwidth. Finally, for efficient hardware implementation fixed point arithmetic should be used and we empirically determine the required precision.

Low Earth Orbit (LEO) constellations have recently gained tremendous attention in the navigational field due to their larger constellation size, faster geometry variations, and higher signal power levels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are heavily attenuated from the atmospheric layers, especially from the ionosphere. Ionospheric delays are, however, expected to be smaller in signals from LEO satellites than GNSS due to their lower orbital altitudes and higher carrier frequency. Nevertheless, unlike for GNSS, there are currently no standardized models for correcting the ionospheric errors in LEO signals. In this paper, we derive a new model called Interpolated and Averaged Memory Model (IAMM) starting from existing International GNSS Service (IGS) data and based on the observation that ionospheric effects repeat every 11 years. Our IAMM model can be used for ionospheric corrections for signals from any satellite constellation, including LEO. This model is constructed based on averaging multiple ionospheric data and reflecting the electron content inside the ionosphere. The IAMM model’s primary advantage is its ability to be used both online and offline without needing real-time input parameters, thus making it easy to store in a device’s memory. We compare this model with two benchmark models, the Klobuchar and International Reference Ionosphere (IRI) models, by utilizing GNSS measurement data from 24 scenarios acquired in several European countries using both professional GNSS receivers and Android smartphones. The model’s behaviour is also evaluated on LEO signals using simulated data (as measurement data based on LEO signals are still not available in the open-access community; we show a significant reduction in ionospheric delays in LEO signals compared to GNSS. Finally, we highlight the remaining open challenges toward viable ionospheric-delay models in an LEO-PNT context.

Global Navigation Satellite Systems (GNSS) underpin a number of modern life activities, such as air/marine transport, autonomous vehicles/machinery control and in areas such as construction, agriculture and offshore operations. GNSS signals are sensitive to a number of vulnerabilities, of which a one major shortcoming is related to the perturbations due to the Earth’s ionosphere. In particular, a phenomenon known as ionospheric scintillation, characterised by rapid fluctuations in GNSS signal amplitude and phase, may seriously disrupt satellite tracking and degrade system accuracy, reliability and integrity. The research presented in this paper was carried out under the “Ionosphere Prediction Service” (IPS) project funded by the European Commission (EC) under Horizon 2020, in the frame of the Galileo programme. The main aim of this project is to translate the forecast of the ionosphere into GNSS userdevoted metrics through the design and development of a prototype for an ionospheric prediction service. The statistical models developed to estimate the 3D positioning errors at the high and low latitudes as a function of scintillation levels are presented. The statistical approach of non-linear regression was applied on a long-term data set to develop these models. If scintillation information can be nowcasted, the developed statistical models can be used to nowcast the 3D positioning errors on a regional or a global scale, which can provide GNSS users with the expected error in the 3D position estimation under scintillation.

Global Satellite Navigation Systems (GNSS) based localization in the context of Advanced Driver Assistance Systems and Intelligent Transportation Systems often requires not only accuracy but focuses on integrity as well. Especially, for safety relevant tasks the computation of proper confidence levels even at degraded environments is of major importance. Low cost solutions that integrate GNSS and additional in-vehicle sensor information are able to bridge short periods of time with limited GNSS accessibility and can therefore improve availability and accuracy. However, non-line-of-sight (NLOS) effects in urban areas need special attention. This error source violates the estimated confidence and introduces an unobservable bias to the position solution. The algorithmic detection of these effects and the proper propagation of all uncertainties within a Bayes framework is one of the key technologies towards the adoption of GNSS for safety critical applications. This paper proposes a probabilistic NLOS detection algorithm that is able to improve both - accuracy and integrity of the position estimate in urban areas. As an extension of a previous implementation by the authors based on an unscented Kalman Filter the proposed system is implemented as a particle filter in order to meet automotive requirements in terms of real time and scalability.