Accurate indoor positioning remains challenging due to the absence of global navigation satellite system (GNSS) signals and the complexity of indoor environments. While foot-mounted inertial measurement unit (IMU) offer infrastructure-free operation, they inevitably accumulate drift over time without external corrections. In this work, we introduce a hybrid localization approach combining a foot-mounted IMU with received signal strength indicator (RSSI) measurements from ambient Wi-Fi access points (APs). Unlike traditional fingerprinting methods, which typically require labor-intensive manual labeling, our approach autonomously constructs a probabilistic radio map during normal pedestrian movement by associating precise pedestrian dead reckoning (PDR) positions with Wi-Fi scans in real-time. The collected RSSI data is discretized onto a two-dimensional spatial grid and statistically normalized to account for variations in signal strength across different devices. During the online phase, a point mass filter (PMF) compares live RSSI measurements against the previously constructed radio map, enabling accurate position estimation without relying on inertial sensors or prior knowledge of AP locations. We evaluated our system extensively in a real-world office environment, testing various hardware devices and multiple users. Experimental results confirm that the method achieves meter accuracy under realistic conditions and exhibits strong generalization capabilities across different devices, underscoring its potential as a lightweight and scalable indoor localization solution.

Transportation Mode Detection (TMD) plays a key role in enabling intelligent transportation systems, optimizing mobility services, and supporting energy-efficient urban planning. However, methods that utilize Global Navigation Satellite System (GNSS) signals typically suffer from performance degradation in urban canyons and tunnels due to satellite occlusion and multipath interference, while Inertial Measurement Unit (IMU)-based methods are prone to cumulative errors from sensor drift. Hence, we propose in this paper, a novel ultrasonic sensing framework for classifying urban transportation modes based on vehicle-borne acoustic emissions in the ultrasonic range. To the best of the authors’ knowledge, this is the first study to analyze ultrasonic sound for transport mode identification. Using a Pettersson u384 microphone sampling at 384 kHz, we captured audio within the 20–80 kHz frequency band across four representative modes, bus, streetcar, subway, and suburban railway, during several hours of real-world operation in Munich. Each transportation mode exhibits a distinct acoustic signature that can be leveraged to differentiate between modes. We employ a stacked ensemble learning approach combining Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), and Multi-Layer Perceptron (MLP), with a LR meta-learner integrating the predictions. Our method achieves an accuracy of 99.57% in distinguishing between modes. This ultrasonic-based approach provides a robust, privacy-preserving alternative that complements traditional sensing modalities in context-aware mobility systems.

This paper presents an algorithm that exploits multipath propagation for the position estimation of mobile receivers. The proposed method utilizes two pieces of information of this multipath signal component: First the delay and thus the path length restricting possible reflection points to an ellipse. And second the Doppler shift of this multipath component to infer angular information along this ellipse. By exploiting relative Doppler information, obtained from the phase difference between the line-of-sight path and multipath components, the approach eliminates the need for strict synchronization requirements. In contrast to state-of-the-art methods that rely on the concept of static virtual transmitters and assume idealized straight wall geometries, the proposed algorithm directly estimates the positions of reflection points. The direct estimation of reflection points allows simultaneous localization and mapping of environments with arbitrary wall shapes. The feasibility of the approach is demonstrated through simulations incorporating delay and Doppler measurements of multipath components. Results confirm that the method enables accurate estimation of both receiver position and reflection points for a variety of wall geometries, including convex and concave surfaces.

Robotic swarms or portable sensor networks are emerging technologies for sensing physical processes that are spatially distributed- and temporally dynamic, both on Earth and in future Moon/Mars exploration missions. We develop a portable network composed of a multitude of self-organized “sensor eggs”. These eggs are equipped with ultra-wideband (UWB) transceivers, providing precise time and position information without additional infrastructures like Global Navigation Satellite Systems (GNSSs). Each egg is additionally equipped with environmental sensors, for example, a Sulfur dioxide gas sensor to explore volcanic activity. We use a real time decentralized particle filter (DPF) to estimate the a-posteriori probability density functions (PDFs) of the egg positions. These PDFs are then used in a static state binary Bayes filter for estimating the gas sources with potentially complex structures such as cracks on the volcano surface. The proposed sensor network is verified with an in-field experiment at La Fossa volcano on the island of Vulcano, Italy, in 2023.

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.

Bluetooth Low Energy (BLE) technology has gained significant attention in recent years due to its low power consumption, wide availability, and compatibility with a variety of devices. This article proposes a novel BLE based positioning system utilizing received signal strength (RSS) measurements. We introduce a server based BLE positioning method. Here, the mobile devices, e.g. smartphones, act as BLE beacons. Static BLE sniffers are installed in the environment. They record RSS measurements and send them to a server. The position of the mobile device is calculated at the server using a particle filter (PF) and can be communicated back to the mobile device. To enhance the distance and position estimation accuracy, each receiver is equipped with multiple BLE receivers which are closely spaced, which helps to reduce fading effects caused by multipath propagation. Furthermore, the RSS measurements are smoothed by a smoothing filter. The proposed method is evaluated by indoor measurements, where we obtain a positioning error with a mean root mean square error (RMSE) of 0.85 m for a mobile device carried by a robot and a mean RMSE of 1.3 m for a walking pedestrian.

The trend towards urbanization increases the need for highly available public transportation. Nevertheless, the majority of society demands for individual transport too. To address these demands in urban areas, intelligent transportation systems (ITS) aim at increasing capacity and safety while reducing costs, accidents, and environmental impact. To that end, both the railway industry and the automotive industry focus on automation, digitization, and wireless communications to cope with increasing numbers of vehicles and passengers. These two industries may rely on cooperative ITS (C-ITS) communicating in the same frequency band. Without appropriate measures, interference between the different radio technologies must be assumed and reliable communication for safety-critical applications cannot be guaranteed. To develop accurate and realistic interference models for current and future radio technologies, we conducted a four-day measurement campaign with the Deutsche Bahn (DB) advanced TrainLab on the Berlin “Süd-Ring” tracks. In this paper, we present an overview on C-ITS radio technologies, the measurement campaign, first results, and conclusions. An initial data analysis shows that adjacent channel interference can cause severe performance degradation on urban rail C-ITS if generated in line-of-sight (LOS) to the train with a significant number of interfering signals.

In this paper, we describe an infrastructure-independent indoor localization approach for various indoor environments. Our method introduces a novel particle filter implementation that enables the fusion of inertial motion unit sensors, user context, user gait direction, and map information. Due to this novel fusion, it performs localization with up to two orders of magnitude fewer particles than state-of-the-art approaches. Additionally, it extracts map information via existing open services, such as the Open Street Maps and it follows defined standards for the map handling. We evaluated all the components of our method in realtime in off-the-shelf smartphones and we find that it performs a median error of 2.3m, while using only 40 particles instead of 400 or up to 4000 particles that other methods require for the same accuracy.

Hybridization of Global Navigation Satellite Systems (GNSS) and fifth generation (5G) cellular positioning is foreseen as a key solution to fulfill high-accuracy positioning requirements in future use cases, such as autonomous vehicles. The evaluation of the hybrid positioning capabilities implies the physical-layer simulation of observables from both GNSS and 5G technologies. In order to ease the complexity of the resulting system-level simulations, a physical-layer abstraction of GNSS and 5G ranging observables is here proposed. The abstraction of GNSS ranging observables is based on a Gaussian-distributed model of the errors sources, while the abstraction of 5G ranging observables is based on the interpolation of the cumulative density function (CDF) of the ranging errors for certain propagation conditions and signal-to-noise (SNR) levels. Thanks to the exploitation of the proposed physical-layer abstraction, low-complexity system- level simulations are performed to assess the positioning capabilities of GNSS and 5G downlink time-difference of arrival (DL-TDoA) in urban macro-cell (UMa) environments. The simulation results indicate the need to adopt hybrid solutions based on multiple GNSS constellations and 5G DL-TDoA with 100-MHz bandwidth, in order to ensure a horizontal positioning accuracy below 5 m for 95% of cases in outdoor urban environments.

In multipath assisted positioning, multipath components (MPCs) are exploited for positioning as they are regarded as line-of-sight (LoS) signals from virtual transmitters. With simultaneous localization and mapping (SLAM), the locations of physical and virtual transmitters are estimated jointly with and relative to the user position. A robust data association scheme is crucial for the robustness of SLAM. In multipath assisted positioning, data association refers to the question which MPCs correspond to which transmitters. We say that a physical or virtual transmitter is visible to the user if the user is in LoS to the transmitter. Within this paper, we propose to map information on the visibility of physical and virtual transmitters in addition to their locations, and use such information for a reliable data association. Visibility information may stem from previous observations of a user, or from a visibility map of the scenario obtained from another user or a central entity. Our simulations in an indoor scenario show that information on the visibility of transmitters considerably improves the positioning performance by increasing the robustness of data association.

A huge variety of services require a precise localization. While global navigation satellite systems may show accurate positioning results in good view-to-sky conditions, their performance decreases drastically in case of shadowing and multipath propagation, such as indoors or in urban scenarios. Our approach is therefore to use terrestrial signals of opportunity for positioning. We exploit multipath propagation in a multipath-assisted positioning approach: each multipath component is regarded as being emitted by a virtual transmitter in a line-of-sight condition. Since the locations of the virtual transmitters are unknown, they are estimated in addition to the user position. This results in a simultaneous localization and mapping (SLAM) problem, where physical and virtual transmitters are considered as landmarks. This paper discusses our approach named Channel-SLAM, and extends it by a solution to the data association problem. We present and compare two different methods to decide for associations among virtual transmitters. By means of simulations, we show that data association can increase the positioning performance of Channel-SLAM remarkably.

Ultra-wideband (UWB) is a promising positioning system that has undergone massive research development in recent years. Most UWB systems assume prior knowledge on the positions of the UWB anchors. Without knowing the anchor positions, an accurate position estimate of a user is difficult. Hence, this paper presents a novel simultaneous localization and mapping (SLAM) approach for pedestrian localization using a UWB system, where the locations of the anchors are unknown. We fuse the distance estimates of the UWB system with heading information obtained from an inertial measurement unit (IMU). We evaluate the proposed algorithm based on measurements with a moving pedestrian and fixed anchors with unknown positions. The evaluations show that an accurate position estimation of both the pedestrian and the anchors is possible without any prior knowledge on the anchor positions.

This paper describes an algorithm that exploits multipath propagation for position estimation of mobile receivers. We apply a novel algorithm based on recursive Bayesian filtering, named Channel-SLAM. This approach treats multipath components as signals emitted from virtual transmitters, which are time synchronized to the physical transmitter and static in their positions. Contrary to other approaches, Channel-SLAM considers also paths occurring due to multiple numbers of reflections or scattering as well as the combination. Hence, each received multipath component increases the number of transmitters resulting in a more accurate position estimate or enabling positioning when the number of physical transmitters is insufficient. Channel-SLAM estimates the receiver position and the positions of the virtual transmitters simultaneously; hence, the approach does not require any prior information, such as a room-layout or a database for fingerprinting. The only prior knowledge needed is the physical transmitter position as well as the initial receiver position and moving direction. Based on simulations, the position precision of Channel-SLAM is evaluated by a comparison to simplified algorithms and to the posterior Cramér-Rao lower bound. Furthermore, this paper shows the performance of Channel-SLAM based on measurements in an indoor scenario with only a single physical transmitter.

Positioning is next to communication the most important field of applications for wireless radio transmissions. This paper considers indoor positioning using wireless signals. Especially in indoor scenarios, multipath reception degrades the accuracy of the positioning device as long as the receiver is based on standard methods. Strategies to mitigate multipath effects on range estimates are in general based on the estimation of the channel impulse response (CIR). All these methods have in common that they determine the CIR in order to remove the influence on the estimate of the line-of-sight path delay. This paper focuses on multipath aided positioning by using the time difference of arrival between multipath components (TDoAbMC). Hence, the paper uses the multipath propagation of the wireless signal to allow positioning in cases of a insufficient number of transmitters or increase the accuracy otherwise. Measurements with a moving receive antenna showed, that multipath components are visible for several meters of receiver movement. To estimate and track the time-variant multipath components of the received signal, the paper uses a Kalman filter which utilizes maximum likelihood estimates as measurements. For positioning, the novel approach treats multipath components as signals from virtual transmitters which are time synchronized to the physical transmitter and fixed in their position. Additionally, using a time difference of arrival approach, the estimation of the user clock bias is not necessary. To use the information of the multipath components, the positioning algorithm has to estimate the user position and the position of the virtual transmitters simultaneously. Furthermore, the new approach does not rely on any prior information such as the room layout or a database for fingerprinting.

Orthogonal frequency division multiplexing (OFDM) became a popular transmission approach since it suppresses intersymbol interference (ISI) due to large channel delays. However, intercarrier interference (ICI) for high mobility receivers yields corrupted channel estimates for pilot-aided OFDM channel estimation. Thus, this paper presents a novel time-variant channel estimation approach to mitigate this system impairment. We linearize the time-variant channel and determine the expansion point by channel estimates corresponding to the current OFDM symbol, and we get the unknown channel slopes by an iterative data and channel estimation. Our algorithm combines a least squares or a minimum norm channel slope estimation and the detection of the channel paths. It sets any channel path power equal zero once the corresponding power estimate is smaller than a given threshold. Our algorithm exploits the large channel correlations between the cyclic prefix and the successive OFDM symbol optimally. These maximum correlations reason the superiority of ICI mitigation with cyclic prefixes compared to channel slope estimation with adjacent OFDM symbols, which needs at least twice the number of pilot symbols. Additionally, our iterative ICI mitigation reduces noise impairments.

This paper presents a novel method for the analytical mean time to lose lock (MTLL) computation of coherent second-order Langevin delay-locked loops (DLLs). Analytical MTLL computation is a key task for DLLs, since the computational complexity of numerical MTLL simulations is far too high in many operating ranges of the second-order Langevin DLLs. To obtain the crucial MTLL values analytically without simulations, we rewrite the Langevin stochastic differential equation (SDE) as a vector-valued Ornstein-Uhlenbeck (OU) SDE. It includes a Gaussian noise term, which yields as a solution of the vector-valued OU SDE a time-variant Gaussian distribution. Thus, the complementary error function yields the loss of lock probability and thereby the MTLL. If we replace the complementary error functions by suitable exponential approximations, we obtain a simple MTLL expression with an exponential function as dominant term. The simple exponential MTLL expression yields the optimum loop parameters corresponding to the maximum MTLL. Simulation results confirm that the optimum loop parameters corresponding to our analytical MTLL computation method and to the simplified exponential approximation coincide. Besides the crucial analytical MTLL results, the OU random processes yield additionally the likewise crucial analytical jitter results.

This paper presents a novel analytical derivation of the false alarm probability (FAP) and detection probability (DP) for non-line-of-sight (NLOS) detection of GNSS signals. In navigation systems, the NLOS propagation in for example urban environments causes positioning errors in the order of hundreds of meters. Thus, detection and mitigation of NLOS signals is a critical task for high accuracy navigation receivers. In this paper, we derive the FAP and DP of power-scaled detectors based on the multipath signals. The derivation considers frequency-selective fading channels taking sub-chip multipath interference, channel dynamics and initial frequency offsets into account. The results show a simple way to calculate the DP versus FAP. Simulation results verify the analytical probabilities and show the trade-off between temporal non-coherent versus temporal coherent averaging. Clearly, the optimum noise averaging depends on the length of the symbol sequence and channel dynamics. Additionally, the simulations show the improved DP of an antenna array at the receiver. Furthermore we see the influence of sub-chip interference in the simulation results.

Motivated by the possibility of decreasing the intersymbol interference (ISI) which is due to large delays of a multipath mobile radio channel, orthogonal frequency division multiplexing (OFDM) became very popular. However, the timevariance of the mobile radio channel induces intercarrier interference (ICI) yielding substantial channel estimation errors and thereby tremendous transmission impairments. Contrary to previous algorithms which resort to a linearization of the time-variant channel, we combat the ICI using eigenspaces of time-domain covariance matrices defined by the autocorrelation function of the Doppler spread. We perform a basis expansion using Slepian sequences and determine the basis coefficients of the time-variant channel by channel estimates from previous OFDM symbols. Once we know these basis coefficients, we obtain the necessary time-variant channel estimation by the Slepian sequences. These time-variant channel estimates allow a symbol detection in frequency domain which eliminates the ICI almost completely. Simulation results investigate both the signal to interference ratio (SIR) and the bit error ratio (BER) of our new ICI mitigation methods and reveal the superiority compared to previous algorithms for ICI reduction.

The key step of syndrome-based decoding of Reed-Solomon codes up to half the minimum distance is to solve the so-called Key Equation. List decoding algorithms, capable of decoding beyond half the minimum distance, are based on interpolation and factorization of multivariate polynomials. This article provides a link between syndrome-based decoding approaches based on Key Equations and the interpolation-based list decoding algorithms of Guruswami and Sudan for Reed-Solomon codes. The original interpolation conditions of Guruswami and Sudan for Reed-Solomon codes are reformulated in terms of a set of Key Equations. These equations provide a structured homogeneous linear system of equations of Block-Hankel form, that can be solved by an adaption of the Fundamental Iterative Algorithm. For an (n,k) Reed-Solomon code, a multiplicitysand a list sizel, our algorithm has time complexityO(ls4n2).