Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected

AIM: Acoustic Inertial Measurement for Indoor Drone Localization and Tracking

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR). We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than commercial UWB-based systems in complex indoor scenarios, where state-of-the-art infrared systems would not even work because of NLoS settings. We further demonstrate that AIM can be extended to support indoor spaces with arbitrary ranges and layouts without loss of accuracy by deploying distributed microphone arrays

Collaborative Indoor Positioning Systems: A Systematic Review

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

Kinematic State Estimation using Multiple DGPS/MEMS-IMU Sensors

Animals have evolved over billions of years and understanding these complex and intertwined systems have potential to advance the technology in the field of sports science, robotics and more. As such, a gait analysis using Motion Capture (MOCAP) technology is the subject of a number of research and development projects aimed at obtaining quantitative measurements. Existing MOCAP technology has limited the majority of studies to the analysis of the steady-state locomotion in a controlled (indoor) laboratory environment. MOCAP systems such as the optical, non-optical acoustic and non-optical magnetic MOCAP systems require predefined capture volume and controlled environmental conditions whilst the non-optical mechanical MOCAP system impedes the motion of the subject. Although the non-optical inertial MOCAP system allows MOCAP in an outdoor environment, it suffers from measurement noise and drift and lacks global trajectory information. The accuracy of these MOCAP systems are known to decrease during the tracking of the transient locomotion. Quantifying the manoeuvrability of animals in their natural habitat to answer the question “Why are animals so manoeuvrable?” remains a challenge. This research aims to develop an outdoor MOCAP system that will allow tracking of the steady-state as well as the transient locomotion of an animal in its natural habitat outside a controlled laboratory condition. A number of researchers have developed novel MOCAP systems with the same aim of creating an outdoor MOCAP system that is aimed at tracking the motion outside a controlled laboratory (indoor) environment with unlimited capture volume. These novel MOCAP systems are either not validated against the commercial MOCAP systems or do not have comparable sub-millimetre accuracy as the commercial MOCAP systems. The developed DGPS/MEMS-IMU multi-receiver fusion MOCAP system was assessed to have global trajectory accuracy of _0:0394m, relative limb position accuracy of _0:006497m. To conclude the research, several recommendations are made to improve the developed MOCAP system and to prepare for a field-testing with a wild animal from a family of a terrestrial megafauna

A Review of pedestrian indoor positioning systems for mass market applications

In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications

A loose-coupled fusion of inertial and UWB assisted by a decision-making algorithm for localization of emergency responders

Combining different technologies is gaining significant popularity among researchers and industry for the development of indoor positioning systems (IPSs). These hybrid IPSs emerge as a robust solution for indoor localization as the drawbacks of each technology can be mitigated or even eliminated by using complementary technologies. However, fusing position estimates from different technologies is still very challenging and, therefore, a hot research topic. In this work, we pose fusing the ultrawideband (UWB) position estimates with the estimates provided by a pedestrian dead reckoning (PDR) by using a Kalman filter. To improve the IPS accuracy, a decision-making algorithm was developed that aims to assess the usability of UWB measurements based on the identification of non-line-of-sight (NLOS) conditions. Three different data fusion algorithms are tested, based on three different time-of-arrival positioning algorithms, and experimental results show a localization accuracy of below 1.5 m for a 99th percentile.This work has been partially supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019 and Project UID/CTM/00264/2019 of 2C2T - Centro de Ciência e Tecnologia Têxtil, funded by National Founds through FCT/MCTES. The work of A. G. Ferreira and D. Fernandes was supported by the FCT under Grant SFRH/BD/91477/2012 and Grant SFRH/BD/92082/2012

Survey on Recent Advances in Integrated GNSSs Towards Seamless Navigation Using Multi-Sensor Fusion Technology

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.©2021 The Authors. Published by ION.fi=vertaisarvioimaton|en=nonPeerReviewed

Context Detection, Categorization and Connectivity for Advanced Adaptive Integrated Navigation

Context is the environment that a navigation system operates in and the behaviour of its host vehicle or user. The type and quality of signals and environmental features available for positioning varies with the environment. For example, GNSS provides high-quality positioning in open environments, low-quality positioning in dense urban environments and no solution at all deep indoors. The behaviour of the host vehicle (or pedestrian) is also important. For example, pedestrian, car and train navigation all require different map-matching techniques, different motion constraints to limit inertial navigation error growth, and different dynamic models in a navigation filter [1]. A navigation system design should therefore be matched to its context. However, the context can change, particularly for devices, such as smartphones, which move between indoor and outdoor environments and can be stationary, on a pedestrian, or in a vehicle. For best performance, a navigation system should therefore be able to detect its operating context and adapt accordingly; this is context-adaptive positioning [1]. Previous work on context-adaptive navigation and positioning has focused on individual subsystems. For example, there has been substantial research into determining the motion type and sensor location for pedestrian dead reckoning using step detection [2-4]. Researchers have also begun to investigate context-adaptive (or cognitive) GNSS [5-7]. However, this paper considers context adaptation across an integrated navigation system as a whole. The paper addresses three aspects of context-adaptive integrated navigation: context detection, context categorization and context connectivity. It presents experimental results showing how GNSS C/N0 measurements, frequency-domain MEMS inertial sensor measurements and Wi-Fi signal availability could be used to detect both the environmental and behavioural contexts. It then looks at how context information could be shared across the different components of an integrated navigation system. Finally, the concept of context connectivity is introduced to improve the reliability of context detection. GNSS C/N0 measurement distributions, obtained using a smartphone, and Wi-Fi reception data collected over a range of indoor, urban and open environments will be compared to identify suitable features from which the environmental context may be derived. In an open environment, strong GNSS signals will be received from all directions. In an urban environment, fewer strong signals will be received and only from certain directions. Inside a building, nearly all GNSS signals will be much weaker than outside. Wi-Fi signals essentially vary with the environment in the opposite way to GNSS. Indoors, more access points (APs) can be received at higher signal strengths and there is greater variation in RSS. In urban environments, large numbers of APs can still be received, but at lower signal strengths [6]. Finally, in open environments, few APs, if any, will be received. Behavioural context is studied using an IMU. Although an Xsens MEMS IMU is used in this study, smartphone inertial sensors are also suitable. Pedestrian, car and train data has been collected under a range of different motion types and will be compared to identify context-dependent features. Early indications are that, as well as detecting motion, it is also possible to distinguish nominally-stationary IMUs that are placed in a car, on a person or on a table from the frequency spectra of the sensor measurements. The exchange of context information between subsystems in an integrated navigation system requires agreement on the definitions of those contexts. As different subsystems are often supplied by different organisations, it is desirable to standardize the context definitions across the whole navigation and positioning community. This paper therefore proposes a framework upon which a “context dictionary” could be constructed. Environmental and behavioural contexts are categorized separately and a hierarchy of attributes is proposed to enable some subsystems to work with highly specific context categories and others to work with broader categories. Finally, the concept of context connectivity is introduced. This is analogous to the road link connectivity used in map matching [8]. As context detection involves the matching of measurement data to stored context profiles, there will always be occurrences of false or ambiguous context identification. However, these may be minimized by using the fact that it is only practical to transition directly between certain pairs of contexts. For example, it is not normally possible to move directly from an airborne to an indoor environment as an aircraft must land first. Thus, the air and land contexts are connected, as are the land and indoor contexts, but the air and indoor contexts are not. Thus, by only permitting contexts that are connected to the previous context, false and ambiguous context detection is reduced. Robustness may be further enhanced by considering location-dependent connectivity. For example, people normally board and leave trains at stations and fixed-wing aircraft typically require an airstrip to take off and land. / References [1] Groves, P. D., Principles of GNSS, inertial, and multi-sensor integrated navigation systems, Second Edition, Artech House, 2013. [2] Park, C. G., et al., “Adaptive Step Length Estimation with Awareness of Sensor Equipped Location for PNS,” Proc. ION GNSS 2007. [3] Frank, K., et al., “Reliable Real-Time Recognition of Motion Related Human Activities Using MEMS Inertial Sensors,” Proc. ION GNSS 2010. [4] Pei, L., et al., “Using Motion-Awareness for the 3D Indoor Personal Navigation on a Smartphone,” Proc. ION GNSS 2011. [5] Lin, T., C. O’Driscoll, and G. Lachapelle, “Development of a Context-Aware Vector-Based High-Sensitivity GNSS Software Receiver,” Proc. ION ITM 2011. [6] Shafiee, M., K., O’Keefe, and G. Lachapelle, “Context-aware Adaptive Extended Kalman Filtering Using Wi-Fi Signals for GPS Navigation,” Proc. ION GNSS 2011. [7] Shivaramaiah, N. C., and A. G. Dempster, “Cognitive GNSS Receiver Design: Concept and Challenges,” Proc. ION GNSS 2011. [8] Quddus, M. A., High Integrity Map Matching Algorithms for Advanced Transport Telematics Applications, PhD Thesis, Imperial College London, 2006