State-of-the-art device-free localization systems infer presence and location of users based on received signal strength measurements of line-of-sight links in wireless networks. In this letter, we propose to enhance device-free localization systems by exploiting multipath propagation between the individual network nodes. Particularly indoors, wireless propagation channels are characterized by multipath propagation, i.e., received signals comprise multipath components due to reflection and scattering. Given prior information about the surrounding environment, e.g., a floor plan, the individual propagation paths of multipath components can be derived geometrically. Inherently, these propagation paths differ spatially from the line-of-sight propagation path and can be considered as additional links in the wireless network. This extended network determines the novel multipath-enhanced device-free localization system. Using theoretical performance bounds on the localization error, we show that including multipath components into device-free localization systems improves the overall localization performance and extends the effective observation area significantly.

Magnetic field localization utilizes position dependent and time persistent distortions of the earth magnetic field. These distortions are introduced by stationary ferromagnetic material in the environment and can be stored in a map to enable localization. Estimating the position of a magnetometer with these distortions requires a calibration of the sensor to enable the matching of the measurements to the map. Typically, the calibration is performed in a prior step and requires specific maneuvers like sensor rotations in a homogenous field. The goal of the maneuvers is to render the calibration parameters observable. For heavy platforms, e.g., cars, trains and driverless transport systems in factories, performing special maneuvers is cumbersome or even impossible. In addition they operate in an environment with an inhomogeneous magnetic field. To address this issue, this paper proposes a novel method that exploits the magnetic field distortions to render the calibration parameters observable. To simplify the calibration process, the calibration parameters are estimated simultaneously with the position of the platform. The method employs a Rao-Blackwellized particle filter that reduces the computational complexity and enables real time processing. The feasibility of the method is shown in an evaluation with measurements of a magnetometer mounted on a model train. The results show a high accuracy of the position and calibration parameter estimation.

Train-to-train (T2T) communication is proposed as an auxiliary safety-guaranteed measure for railway communications. Vehicular communication standards have been supporting railway use cases and T2T communications. It is essential to consider the propagation characteristics of railway environments with dedicated T2T channel models for simulation and testing of vehicular communication standards. In this paper, we propose six tapped delay line (TDL) channel models for T2T communications in Hilly Terrain and Railway Station. The measurement based proposed channel models consider different T2T distances and relative speeds of up to 50 km/h with trains moving in an approaching maneuver. Also, a study about the Doppler-delay behavior in the considered scenarios is presented using the scattering function. The proposed TDL channel models as well as the measured propagation characteristics are used in a 802.11bd simulation framework. Both simulation results, which are based on the TDL channel models or on the measurements, are compared to each other. The results show a very good match between the TDL channel models and the measurement data and indicate a good accuracy of the theoretical derivations and the simulation method.

We address the downlink of a cellular multi‐carrier CDMA (MC‐CDMA) system taking into account channel estimation. The system performance in presence of a synchronization mismatch between two interfering base stations (BS) is analyzed in the way that a mobile terminal receives the perfectly synchronized signal from the desired BS as well as the signal from one interfering BS with a synchronization offset. It is demonstrated through simulations that spreading and cell specific random subcarrier interleaving effectively decorrelates the interfering signal, independent of the synchronization offset. Furthermore, the robustness of the channel estimator to cellular interference is examined. Copyright © 2004 AEI

Multipath-enhanced device-free localization (MDFL) systems determine presence and location of objects and users not necessarily equipped with localization devices. For localization, MDFL systems exploit user-induced changes in the power of all received signal components, including both line-of-sight and multipath components (MPCs). In this work, we therefore provide a statistical fading model that describes user-induced changes in received power specifically for MPCs. The model is derived and validated empirically using an extensive set of wideband and ultra-wideband measurement data. Since the localization performance of MDFL systems strongly depends on the information about the propagation paths within the wireless network, we further propose a Bayesian calibration approach that estimates the location of the reflection points of MPCs caused by single-bounce reflections. For MPCs caused by single-bounce reflections, the solution space of possible locations of reflection points is constrained to the delay ellipse, which allows the formulation of a computationally efficient one-dimensional estimation problem. Eventually, the problem is solved by sequential Bayesian estimation. The applicability of the proposed approach is demonstrated and evaluated using measurement data. Independent of the underlying measurement system, the Bayesian calibration approach is shown to robustly estimate the locations of the reflection points in different environments. Finally, the localization results of MDFL for an indoor scenario confirm the applicability of the Bayesian calibration approach.

GRAMMAR (EC FP7) is a project aiming to deliver IP for future Galileo mass market receivers. One of the outcomes of this project will be an advanced Galileo receiver prototype. In this paper we present the research for finding the optimal dual frequency option for a Galileo mass market receiver. We discuss the impact of multiple frequencies on baseband algorithms and the implementation issues of a dual-frequency baseband. From the analysis results, we draw the following conclusions: E1/E5a combination is the optimal dual-frequency solution for Galileo; a common dual-frequency baseband architecture with flexible channels seems to be the best implementation approach for a Galileo E1/E5a dual-frequency receiver.