Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components

In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multiple-output (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distance-related phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and therefore also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40 MHz. The results show that high accuracy localization is possible even with such a low bandwidth.Comment: 14 pages (two columns), 13 figures. This work has been submitted to the IEEE Transaction on Wireless Communications for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Graph-based Algorithm for Robust Sequential Localization Exploiting Multipath for Obstructed-LOS-Bias Mitigation

This paper presents a factor graph formulation and particle-based sum-product algorithm (SPA) for robust sequential localization in multipath-prone environments. The proposed algorithm jointly performs data association, sequential estimation of a mobile agent position, and adapts all relevant model parameters. We derive a novel non-uniform false alarm (FA) model that captures the delay and amplitude statistics of the multipath radio channel. This model enables the algorithm to indirectly exploit position-related information contained in the MPCs for the estimation of the agent position. Using simulated and real measurements, we demonstrate that the algorithm can provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations, significantly outperforming the conventional amplitude-information probabilistic data association (AIPDA) filter. We show that the performance of our algorithm constantly attains the posterior Cramer-Rao lower bound (PCRLB), or even succeeds it, due to the additional information contained in the presented FA model.Comment: corrected small errors, changed titl

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

Data Fusion for Multipath-Based SLAM: Combing Information from Multiple Propagation Paths

Multipath-based simultaneous localization and mapping (SLAM) is an emerging paradigm for accurate indoor localization with limited resources. The goal of multipath-based SLAM is to detect and localize radio reflective surfaces to support the estimation of time-varying positions of mobile agents. Radio reflective surfaces are typically represented by so-called virtual anchors (VAs), which are mirror images of base stations at the surfaces. In existing multipath-based SLAM methods, a VA is introduced for each propagation path, even if the goal is to map the reflective surfaces. The fact that not every reflective surface but every propagation path is modeled by a VA, complicates a consistent combination "fusion" of statistical information across multiple paths and base stations and thus limits the accuracy and mapping speed of existing multipath-based SLAM methods. In this paper, we introduce an improved statistical model and estimation method that enables data fusion for multipath-based SLAM by representing each surface by a single master virtual anchor (MVA). We further develop a particle-based sum-product algorithm (SPA) that performs probabilistic data association to compute marginal posterior distributions of MVA and agent positions efficiently. A key aspect of the proposed estimation method based on MVAs is to check the availability of single-bounce and double-bounce propagation paths at a specific agent position by means of ray-launching. The availability check is directly integrated into the statistical model by providing detection probabilities for probabilistic data association. Our numerical simulation results demonstrate significant improvements in estimation accuracy and mapping speed compared to state-of-the-art multipath-based SLAM methods.Comment: 14 pages (two column), 8 figure

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Nowadays, the availability of the location information becomes a key factor in today’s communications systems for allowing location based services. In outdoor scenarios, the Mobile Terminal (MT) position is obtained with high accuracy thanks to the Global Positioning System (GPS) or to the standalone cellular systems. However, the main problem of GPS or cellular systems resides in the indoor environment and in scenarios with deep shadowing effect where the satellite or cellular signals are broken. In this paper, we will present a review over different technologies and concepts used to improve indoor localization. Additionally, we will discuss different applications based on different localization approaches. Finally, comprehensive challenges in terms of accuracy, cost, complexity, security, scalability, etc. are presente

Detection and Tracking of Multipath Channel Parameters Using Belief Propagation

We present a belief propagation (BP) algorithm with probabilistic data association (DA) for detection and tracking of specular multipath components (MPCs). In real dynamic measurement scenarios, the number of MPCs reflected from visible geometric features, the MPC dispersion parameters, and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for specular MPC detection and joint estimation problem, and represent it by a factor graph which enables the use of BP for efficient computation of the marginal posterior distributions. A parametric channel estimator is exploited to estimate at each time step a set of MPC parameters, which are further used as noisy measurements by the BP-based algorithm. The algorithm performs probabilistic DA, and joint estimation of the time-varying MPC parameters and mean false alarm rate. Preliminary results using synthetic channel measurements demonstrate the excellent performance of the proposed algorithm in a realistic and very challenging scenario. Furthermore, it is demonstrated that the algorithm is able to cope with a high number of false alarms originating from the prior estimation stage