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© 2002-2012 IEEE. We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem and represent it by a factor graph, which enables the use of belief propagation (BP) for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. The algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetic measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.

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.

We present a localization algorithm for low-power long-range Internet-of-things (IoT) networks, which exploits angle of arrival (AoA) and range information from non-coherent received signal strength (RSS) measurements. In this work, each anchor node is equipped with array antennas of known geometry and radiation patterns. The position of the target node and the path-loss exponent to each anchor are unknown and possibly time-varying. The joint estimation problem is formulated with a Bayesian model, where the likelihood functions are derived from the classical path-loss model and an RSS difference model. A message passing method is then exploited for efficient computation of the marginal posterior distribution of each unknown variable. The proposed algorithm is validated using real outdoor measurements from a low-power wide area network based IoT system in a challenging scenario. Results show that the proposed algorithm can adapt to dynamic propagation conditions, and improve the localization accuracy compared to a method that exploits only single geometric feature. Furthermore, the algorithm scales well in different antenna array configurations, and is compatible with various existing IoT standards.

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 multipleoutput (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.

Sub-band divided ray tracing (RT) has been widely used to reproduce as reliably as possible the ultra-wideband (UWB) radio wave propagation channel in realistic indoor envi- ronments. However, its accuracy is strictly limited by the available description of the environment. Moreover, its computational complexity scales with the number of selected subbands and the number of propagation paths. In the present work, our RT tool considers not only deterministic propagation paths but also dif- fuse scattering components. Based on a low-complexity sub-band divided RT implementation, we propose a calibration method for indoor UWB sub-band divided RT. The method estimates the optimal material parameters, including the dielectric parameters and the scattering parameters, using channel measurements and multiobjective simulated annealing (MOSA). This calibration can improve the accuracy of sub-band divided RT in terms of the power delay profile (PDP) and the root mean square (RMS) delay spread for all test locations including those not considered by the calibration. A measurement campaign is used to verify the calibration technique.

Ray tracing has been extensively used to simulate indoor channel characteristics. For an ultra-wideband system, the channel characteristics vary significantly over the entire bandwidth. To cope with this, sub-band divided RT has been proposed by dividing the frequency of interest into multiple subbands and superposing the RT results at the individual center frequency of each subband. Thus, the computational complexity is directly proportional to the number of subbands. In this paper, we propose a mathematical method to significantly reduce the computational complexity of the sub-band divided RT, making it almost independent of the number of subbands. It is important to note that, based on our approach, not only the determination of the rays reaching a give location is made only once, but also the electromagnetic calculation of the received signal is not needed to perform repeatedly. The accuracy of low-complexity subband divided RT algorithm is verified through a measurement campaign.