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.
27 May 2023 in IET Microwaves, Antennas & Propagation
Device‐free localisation (DFL) is a prominent example of radio sensing and radio frequency (RF)‐based passive localisation. RF‐based passive localisation approaches determine the position of a non‐cooperative user based on the user's impact on radio propagation. In this regard, DFL systems measure user‐induced changes in the properties of the received RF signals. With multipath‐enhanced device‐free localisation (MDFL), a novel passive localisation approach which is taking the advantage of user‐induced fading in the multipath signals, that is, reflected and scattered signals is introduced. In this work, the authors realise an MDFL system using low‐cost ultra‐wideband (UWB) devices. Specifically, the Qorvo (DecaWave) DW1000 module is used, for which how to access the channel impulse response is described in detail. Additionally, an overview of the required signal processing for MDFL is provided and a possible sequential Bayesian approach is introduced. Moreover, an efficient ranging scheme based on time‐division multiple access (TDMA) and message broadcasting is outlined, which allows the deployment of a large number of interconnected UWB devices. Using an exemplary network of interconnected UWB devices, the localisation performance of both DFL and MDFL for an indoor scenario is evaluated. Thereby, MDFL is shown to clearly outperform DFL in terms of robustness and accuracy. Furthermore, a TDMA‐based ranging scheme for active localisation is used, that is, for localising an UWB device carried by the user, allowing for a direct comparison between active and passive localisation. Achieving a sub‐decimetre accuracy, the active localisation outperforms both DFL and MDFL, and thus, shows the possibility of using an active localisation device to initialize MDFL.
23 July 2020 in IEEE Transactions on Vehicular Technology
Mobile-to-mobile channels often exhibit timevariant Doppler frequency shifts due to the movement of transmitter and receiver. An accurate description of the Doppler frequency turns out to be very difficult in Cartesian coordinates, and any subsequent algebraic analysis of the Doppler frequency is intractable. In contrast to other approaches, we base our investigation on a geometric description of the Doppler frequency with the following three mathematical pillars: prolate spheroidal coordinate system, algebraic curve theory, and differential forms. The prolate spheroidal coordinate system is more appropriate to algebraically investigate the problem. After the transformation into the new coordinate system, the theory of algebraic curves is needed to resolve the ambiguities. Finally, the differential forms are required to derive the joint delay Doppler probability density function. This function is normalized by the equivalent ellipsoidal area of the scattering plane bounded by the delay ellipsoid. The results generalize in a natural way our previous model to a complete 3D description. Our solutions enable insight into the geometry of the Doppler frequency and we were able to derive a Doppler frequency that is dependent on the delay and the scattering plane. The presented theory allows describing any time-variant, single-bounce, mobile-to-mobile scattering channel.
27 December 2023 in IEEE Transactions on Wireless Communications
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.
Thousands of fatalities among pedestrians are caused every year by traffic accidents. Vehicle-to-pedestrian (V2P) communication promises to prevent accidents by enabling collision avoidance application. To develop and test a V2P communications system, accurate knowledge of the propagation channel is essential. However, only limited analysis of V2P channel have been reported in the literature. To fill this gap, the German Aerospace Center conducted an extensive channel sounding measurements campaign in a controlled environment. The measurements were performed at 5.2 GHz with a bandwidth of 120 MHz. In parallel to the channel sounding measurements, performance measurements were carried out using ITS-G5 system at 5.9 GHz and with a bandwidth of 10 MHz. This paper describes the setup and the scenarios for the two measurements. First results on channel evaluation in different scenarios as well as path loss models are presented.
This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér–Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér–Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of m at 90% confidence.