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

SLAM using LTE Multipath Component Delays

Cellular radio based localization can be an important complement or alternative to other localization technologies, as base stations continuously transmit signals of opportunity with beneficial positioning properties. In this paper, we use the long term evolution (LTE) cell-specific reference signal for this purpose. The multipath component delays are estimated by the ESPRIT algorithm, and the estimated multipath component delays of different snapshots are associated by global nearest neighbor with a Kalman filter. Rao-Blackwellized particle filter based simultaneous localization and mapping (SLAM) is then applied to estimate the position of user equipment and that of the base station and virtual transmitters. In a measurement campaign, data from one base station was logged, and the analysis based on the data shows that, at the end of the measurement, the SLAM performance is 11 meters better than that with only inertial measurement unit (IMU)

A Neural-enhanced Factor Graph-based Algorithm for Robust Positioning in Obstructed LOS Situations

This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components). The physics-based and data-driven models are embedded in a joint Bayesian framework allowing to derive from first principles a factor graph-based algorithm that fuses the information of these models. The proposed algorithm uses radio signal measurements from multiple base stations to robustly estimate the mobile agent's position together with all model parameters. It provides high localization accuracy by exploiting the position-related information of the LOS component via the physics-based model and robustness by exploiting the geometric imprint of multipath components independent of the propagation channel via the data-driven model. In a challenging numerical experiment involving obstructed LOS situations to all anchors, we show that the proposed sequential algorithm significantly outperforms state-of-the-art methods and attains the posterior Cramer-Rao lower bound even with training data limited to local regions.Comment: corrrected left-shift in Fig.

Simultaneous localization and mapping in millimeter wave networks with angle measurements

In this paper we propose a belief propagation (BP) based simultaneous localization and mapping (SLAM) approach suitable for millimeter wave (mm-Wave) networks. This approach leverages angle of arrival (AoA) and angle of departure (AoD) information with respect to multiple scatterers. Considering measurements from multiple base stations (BSs) and scatterers, seen as multiple sources, we solve out the data association problem from a centralized BP perspective, while jointly estimating the positions of both the mobile and scatterers. Simulations show that the proposed approach outperforms conventional distributed BS-wise BP methods in terms of estimation accuracy

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

Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information

Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the received signals are used to jointly estimate the positions of the vehicle, transmitters, and virtual transmitters (VT) with an extended fast simultaneous localization and mapping (FastSLAM) algorithm. The results show that the algorithm can accurately estimate the positions of the vehicle and the transmitters (and virtual transmitters). The vehicle’s horizontal position error of SLAM fused with proprioception is less than 6 meters after a traversed distance of 530 meters, whereas un-aided proprioception results in a horizontal error of 15 meters

Adaptive detection probability for mmWave 5G SLAM

In 5G simultaneous localization and mapping (SLAM), estimates of angles and delays of mm Wave channels are used to localize the user equipment and map the environment. The interface from the channel estimator to the SLAM method, which was previously limited to the channel parameters estimates and their uncertainties, is here augmented to include the detection probabilities of hypothesized landmarks, given certain a user location. These detection probabilities are used during data association and measurement update, which are important steps in any SLAM method. Due to the nature of mm Wave communication, these detection probabilities depend on the physical layer signal parameters, including beamforming, precoding, bandwidth, observation time, etc. In this paper, we derive these detection probabilities for different deterministic and stochastic channel models and highlight the importance of beamforming