arXiv subjects

Erik Leitinger

Publications and source records attributed to Erik Leitinger.

At least 19 recordsLinked to original sources

arXiv2026

Coherent Direct D-MIMO Localization

Distributed multiple-input multiple-output (D-MIMO) is envisioned as a key deployment architecture for future wireless systems, offering improved coverage and robustness through spatial separation, and favorable geometry for localization and sensing. Its greatest potential for localization lies in joint coherent processing across distributed antenna panels. However, stringent frequency-synchronization and phase-calibration requirements, together with multimodal likelihood functions, hinder the estimation process. Consequently, most existing algorithms process the panels noncoherently, potentially sacrificing localization accuracy. We present a unified family of Bayesian state-space filters that are based on concentrated Type-I and marginal Type-II likelihoods for wideband near-field D-MIMO systems and operate directly on noisy channel observations. The Type-I filters explicitly realize (i) noncoherent, (ii) coherent, and (iii) carrier-phase-based processing. For Type-II filtering, we show that a zero-mean model is inherently noncoherent under distributed processing, whereas observation stacking restores coherence. A nonzero-mean model can automatically adapt to the coherence available in the data, a property that we term ``soft coherence''. We derive posterior Cram\'er-Rao lower bounds (PCRLBs) for all three coherence levels and show that each level is fundamentally tied to the number of phase parameters used for positioning or treated as nuisance parameters. Numerical results show that the coherence-specific filters closely approach their respective PCRLBs and that coherent processing can substantially outperform noncoherent processing. We derive particle-based belief propagation methods, which parallelize over particles and distributed panels, scale linearly with the observed data, and achieve runtimes of tens of milliseconds per time step in a GPU-accelerated implementation.

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arXiv2026

Integrated Real-Time Testbed for Wideband RFID and Wireless Power Transfer

This contribution presents an experimental integrated real-time 8 x 8 distributed MIMO (D-MIMO) testbed for wideband backscatter communication (BSC) and wireless power transfer (WPT). The testbed operates in the 2.45 GHz band with coherent sampling at 200 MS/s, employs a backscatter link frequency of 40 kHz, and uses wideband 5G NR reference signals for excitation. We evaluate the testbed by exploiting the estimated channel state information (CSI) in two target applications: wireless power transfer towards the backscatter device (BD) and real-time positioning of a BD in an indoor environment. In conjunction with the baseband processing chain introduced, the testbed requires less than 2 ms of total airtime to excite the system and acquire the signals for subsequent synchronization and CSI estimation on uplink BSC signals. With the CSI, we demonstrate effective energy harvesting gains of up to 12 dB.

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arXiv2026

Soft-Coherent Direct Multipath SLAM

Challenging indoor and urban environments with severe multipath propagation and obstructed line-of-sight degrade classical radio positioning. Multipath-based simultaneous localization and mapping (MP-SLAM) addresses this by building and exploiting propagation maps for robust localization. Emerging distributed multiple-input multiple-output (D-MIMO)/extremely large-scale MIMO (XL-MIMO) infrastructures provide large spatial apertures and high-resolution sensing, especially when phase coherence is maintained across base stations, subarrays, or distributed arrays. We propose a scalable Bayesian direct MP-SLAM method for coherent data fusion in D-MIMO/XL-MIMO systems that jointly infers the environment while performing robust, high-accuracy localization directly from raw radio signals. While commonly used zero-mean Type-II likelihood functions inherently lead to noncoherent processing across distributed arrays and thus to aperture loss, the proposed phase-preserving nonzero-mean Type-II likelihood shares a complex mean across distributed arrays. This enables coherent fusion and preserves the distributed aperture gain, while the variance captures noncoherent signal power. The method is combined with a surface model that enables map-feature fusion across the distributed infrastructure and supports near-field propagation and visibility effects. Bayesian inference is performed using belief propagation by means of the sum-product algorithm on a factor graph with particle-based messages. Parallelizing over particles and arrays, the GPU-accelerated implementation achieves millisecond-level runtimes even in large or distributed infrastructures. Simulation results show that the proposed method achieves performance gains over existing noncoherent methods and approaches the corresponding posterior CRLB, highlighting the potential of coherent processing for high-resolution sensing and localization.

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arXiv2026

A Variational Message Passing Framework for Multi-Sensor Multi-Object Tracking using Raw Radar Signals

The growing proliferation of unmanned aerial vehicles (UAVs) poses major challenges for reliable airspace surveillance, as drones are typically small, have low radar cross-sections, and often move slowly in cluttered environments. These characteristics make the joint tasks of detecting, localizing, and tracking multiple objects difficult for conventional detect-then-track (DTT) approaches, which rely on pre-processed measurements and may discard informative low-signal-to-noise ratio (SNR) signal components. To overcome these limitations, we propose a variational message passing (VMP)-based direct multiobject tracking (MOT) method that operates directly on raw radar signals and explicitly accounts for an unknown and time-varying number of objects. The proposed method is formulated for MIMO multi-radar systems and performs data fusion by jointly processing the signals of all radar sensors using a probabilistic model. A superimposed signal model is employed to capture correlations in the raw sensor data caused by closely spaced objects, and a hierarchical Bernoulli-Gamma model is introduced to jointly model object existence, reflectivities, and the reliability of individual radar-object links. Using a mean-field approximation, we derive message updates, yielding a computationally efficient VMP algorithm that simultaneously performs object detection, track formation, state estimation, and nuisance parameter learning directly from the radar signal. Simulation results in synthetic scenarios with weak and closely-spaced objects show that the proposed direct-MOT method outperforms a conventional pipeline based on super-resolution estimation followed by belief propagation (BP)-based tracking, particularly in low-SNR and clutter-rich conditions, demonstrating the advantages of direct signal-level inference and coherent multi-radar fusion.

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arXiv2026

Multi-Sensor Fusion for Extended Object Tracking Exploiting Active and Passive Radio Signals

Reliable and robust positioning of radio devices remains a challenging task due to multipath propagation, hardware impairments, and interference from other radio transmitters. A frequently overlooked but critical factor is the agent itself, e.g., the user carrying the device, which potentially obstructs line-of-sight (LOS) links to the base stations (anchors). This paper addresses the problem of accurate positioning in scenarios where LOS links are partially blocked by the agent. The agent is modeled as an extended object (EO) that scatters, attenuates, and blocks radio signals. We propose a Bayesian method that fuses ``active'' measurements (between device and anchors) with ``passive'' multistatic radar-type measurements (between anchors, reflected by the EO). To handle measurement origin uncertainty, we introduce an multi-sensor and multiple-measurement probabilistic data association (PDA) algorithm that jointly fuses all EO-related measurements. Furthermore, we develop an EO model tailored to agents such as human users, accounting for multiple reflections scattered off the body surface, and propose a simplified variant for low-complexity implementation. Evaluation on both synthetic and real radio measurements demonstrates that the proposed algorithm outperforms conventional PDA methods based on point target assumptions, particularly during and after obstructed line-of-sight (OLOS) conditions.

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arXiv2026

Adaptive Multipath-Based SLAM for Distributed MIMO Systems

Localizing users and mapping the environment using radio signals is a key task in emerging applications such as low-latency communications and safety-critical navigation. Recently introduced multipath-based SLAM methods can jointly localize a mobile agent and map reflective surfaces in radio frequency (RF) environments. Most existing methods assume that map features and their corresponding RF propagation paths are statistically independent. This assumption neglects inherent dependencies that arise when a single reflective surface contributes to multiple propagation paths or when an agent communicates with multiple base stations. Existing approaches that aim to fuse information across propagation paths are further limited by their inability to perform ray tracing in RF environments with nonconvex geometries. In this paper, we propose a Bayesian multipath-based SLAM method for distributed MIMO systems that addresses these limitations. We exploit amplitude statistics to establish adaptive, time-varying detection probabilities. Based on the resulting 'soft' ray-tracing strategy, the proposed method can fuse information across propagation paths in RF environments with nonconvex geometries. A Bayesian estimation framework for the joint estimation of map features and agent state is developed by applying the message passing rules of the sum-product algorithm to a factor graph representation of the proposed statistical model. We further introduce a new initialization procedure for reflective surfaces that enables the introduction of new surface states even when measurements arise solely from double-bounce paths. The proposed method is validated using both synthetic and real RF measurements obtained in challenging scenarios with nonconvex geometries and OLoS conditions. The results demonstrate that it provides accurate localization and mapping performance and approaches the posterior CRLBs.

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arXiv2026

AI-enhanced Direct SLAM: A Principled Approach to Unsupervised Learning in Bayesian Inference

In this paper, we propose an artificial intelligence (AI)-enhanced hybrid simultaneous localization and mapping (SLAM) method that performs Bayesian inference directly on raw radio-frequency (RF) signals while learning an environment model in an unsupervised manner. The approach combines a physically interpretable signal model for line-of-sight (LOS) components with an AI model that captures multipath component statistics. Building on this formulation, we develop a particle-based sumproduct algorithm (SPA) on a factor graph that jointly estimates the mobile terminal (MT) state, visibility, multipath parameters, and noise variances, and integrate it into a variational framework that maximizes the evidence lower bound (ELBO) to learn the neural network (NN) parametrization directly from measurements. We further present a highly efficient GPU-based implementation that enables parallel likelihood evaluation across particles and base stations (BSs). Simulation results in multipath environments demonstrate that the proposed method learns the generative, environment-dependent signal model in an unsupervised manner while accurately localizing the MT and effectively exploiting the learned map in obstructed-line-of-sight (OLOS) scenarios.

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arXiv2026

Probabilistic Occupancy Grid for Radio-Based SLAM

Sensing is an integral part of 6G and beyond systems, providing exceptional environmental perception along with communication. Radio frequency (RF)-based sensing often relies on simplified geometric assumptions (e.g., point scatterers or planar surfaces) to model specular multipath and keep inference tractable. However, such representations are limited in their ability to capture extended objects with complex geometries and properties. This paper presents a probabilistic occupancy grid framework for radio-based simultaneous localization and mapping (SLAM), jointly reconstructing geometric structures and their RF-related properties. The proposed occupancy grid map representation is integrated into a multipath-based SLAM formulation to enable simultaneous mobile-agent localization and environment mapping using multipath measurements. To connect RF measurements with the grid map, a surface model is employed to describe candidate reflection paths, while occupancy grid cell states capture measurement uncertainties and fine-grained geometric details. RF-related object properties are represented through reflection coefficients. The proposed framework offers a principled, proof-of-concept approach to physically interpretable radio-based mapping, and simulation results demonstrate accurate reconstruction of geometry and material properties, as well as high-accuracy localization. In addition, the results highlight the potential to use prior occupancy maps obtained from other radio devices or complementary sensors for subsequent map extension and refinement.

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arXiv2026

Simultaneous Source Separation, Synchronization, Localization and Mapping for 6G Systems

Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising approach for future 6G networks to jointly estimate the positions of transmitters and receivers together with the propagation environment. In cooperative MP-SLAM, information collected by multiple mobile-terminals (MTs) is fused to enhance accuracy and robustness. Existing methods, however, typically assume perfectly synchronized base stations (BSs) and orthogonal transmission sequences, rendering inter-BS interference at the MT negligible. In this work, we relax these assumptions and address simultaneous source separation, synchronization, and mapping. A relevant example arises in modern 5G systems, where BSs employ muting patterns to mitigate interference, yet localization performance still degrades. We propose a novel BS-dependent data association and synchronization bias model, integrated into a joint Bayesian framework and inferred via the sum-product algorithm on a factor graph. The impact of joint synchronization and source separation is analyzed under various system configurations. Compared with state-of-the-art cooperative MP-SLAM assuming orthogonal and synchronized BSs, our statistical analysis shows no significant performance degradation. The proposed BS-dependent data association model constitutes a principled approach for classifying features by arbitrary properties that persist over time, such as reflection order or feature type (scatter points versus walls).

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arXiv2025

A Block-Sparse Bayesian Learning Algorithm with Dictionary Parameter Estimation for Multi-Sensor Data Fusion

We propose an sparse Bayesian learning (SBL)-based method that leverages group sparsity and multiple parameterized dictionaries to detect the relevant dictionary entries and estimate their continuous parameters by combining data from multiple independent sensors. In a MIMO multi-radar setup, we demonstrate its effectiveness in jointly detecting and localizing multiple objects, while also emphasizing its broader applicability to various signal processing tasks. A key benefit of the proposed SBL-based method is its ability to resolve correlated dictionary entries-such as closely spaced objects-resulting in uncorrelated estimates that improve subsequent estimation stages. Through numerical simulations, we show that our method outperforms the newtonized orthogonal matching pursuit (NOMP) algorithm when two objects cross paths using a single radar. Furthermore, we illustrate how fusing measurements from multiple independent radars leads to enhanced detection and localization performance.

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arXiv2025

Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM

Radio-frequency simultaneous localization and mapping (RF-SLAM) methods jointly infer the position of mobile transmitters and receivers in wireless networks, together with a geometric map of the propagation environment. An inferred map of specular surfaces can be used to exploit non-line-of-sight components of the multipath channel to increase robustness, bypass obstructions, and improve overall communication and positioning performance. While performance bounds for user location are well established, the literature lacks performance bounds for map information. This paper derives the mapping error bound (MEB), i.e., the posterior Cramér-Rao lower bound on the position and orientation of specular surfaces, for RF-SLAM. In particular, we consider a very general scenario with single- and double-bounce reflections, as well as distributed anchors. We demonstrate numerically that a state-of-the-art RF-SLAM algorithm asymptotically converges to this MEB. The bounds assess not only the localization (position and orientation) but also the mapping performance of RF-SLAM algorithms in terms of global features.

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arXiv2025

Bayesian Self-Calibration and Parametric Channel Estimation for 6G Antenna Arrays

Accurate channel estimation is essential for both high-rate communication and high-precision sensing in 6G wireless systems. However, a major performance limitation arises from calibration mismatches when operating phased-array antennas under real-world conditions. To address this issue, we propose to integrate antenna element self-calibration into a variational sparse Bayesian learning (VSBL) algorithm for parametric channel estimation. We model antenna gain and phase deviations as latent variables and derive explicit update equations to jointly infer these calibration parameters and the channel parameters; the number of multipath components (MPCs) along with their complex amplitudes, delays, and angles-of-arrival (AoA), as well as the noise variance. We assess its performance in terms of the optimal subpattern-assignment (OSPA) metric, demonstrating consistent improvements over conventional VSBL without calibration. Furthermore, we show that integrating the estimation of the calibration parameters into the VSBL algorithm actually increases convergence speed, since a missing or wrong calibration results in the additional estimation of spurious components.

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arXiv2025

General Pruning Criteria for Fast SBL

Sparse Bayesian learning (SBL) associates to each weight in the underlying linear model a hyperparameter by assuming that each weight is Gaussian distributed with zero mean and precision (inverse variance) equal to its associated hyperparameter. The method estimates the hyperparameters by marginalizing out the weights and performing (marginalized) maximum likelihood (ML) estimation. SBL returns many hyperparameter estimates to diverge to infinity, effectively setting the estimates of the corresponding weights to zero (i.e., pruning the corresponding weights from the model) and thereby yielding a sparse estimate of the weight vector. In this letter, we analyze the marginal likelihood as function of a single hyperparameter while keeping the others fixed, when the Gaussian assumptions on the noise samples and the weight distribution that underlies the derivation of SBL are weakened. We derive sufficient conditions that lead, on the one hand, to finite hyperparameter estimates and, on the other, to infinite ones. Finally, we show that in the Gaussian case, the two conditions are complementary and coincide with the pruning condition of fast SBL (F-SBL), thereby providing additional insights into this algorithm.

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arXiv2025

Optimizing Sparse Antenna Arrays for Localization and Sensing using Vector Spherical Wave Functions

In increasing number of electronic devices implement wideband radio technologies for localization and sensing purposes, like ultra-wideband (UWB). Such radio technologies benefit from a large number of antennas, but space for antennas is often limited, especially in devices for mobile and IoT applications. A common challenge is therefore to optimize the placement and orientations of a small number of antenna elements inside a device, leading to the best localization performance. We propose a method for systematically approaching the optimization of such sparse arrays by means of Cramér-Rao lower bounds (CRLBs) and vector spherical wave functions (VSWFs). The VSWFs form the basis of a wideband signal model considering frequency, direction and polarization-dependent characteristics of the antenna array under test (AUT), together with mutual coupling and distortions from surrounding obstacles. We derive the CRLBs for localization parameters like delay and angle-of-arrival for this model under additive white Gaussian noise channel conditions, and formulate optimization problems for determining optimal antenna positions and orientations via minimization of the CRLBs. The proposed optimization procedure is demonstrated by means of an exemplary arrangement of three Crossed Exponentially Tapered Slot (XETS) antennas.

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arXiv2025

Robust Localization in Modern Cellular Networks using Global Map Features

Radio frequency (RF) signal-based localization using modern cellular networks has emerged as a promising solution to accurately locate objects in challenging environments. One of the most promising solutions for situations involving obstructed-line-of-sight (OLoS) and multipath propagation is multipathbased simultaneous localization and mapping (MP-SLAM) that employs map features (MFs), such as virtual anchors. This paper presents an extended MP-SLAM method that is augmented with a global map feature (GMF) repository. This repository stores consistent MFs of high quality that are collected during prior traversals. We integrate these GMFs back into the MP-SLAM framework via a probability hypothesis density (PHD) filter, which propagates GMF intensity functions over time. Extensive simulations, together with a challenging real-world experiment using LTE RF signals in a dense urban scenario with severe multipath propagation and inter-cell interference, demonstrate that our framework achieves robust and accurate localization, thereby showcasing its effectiveness in realistic modern cellular networks such as 5G or future 6G networks. It outperforms conventional proprioceptive sensor-based localization and conventional MP-SLAM methods, and achieves reliable localization even under adverse signal conditions.

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arXiv2025

Low-latency D-MIMO Localization using Distributed Scalable Message-Passing Algorithm

Distributed MIMO and integrated sensing and communication are expected to be key technologies in future wireless systems, enabling reliable, low-latency communication and accurate localization. Dedicated localization solutions must support distributed architecture, provide scalability across different system configurations and meet strict latency requirements. We present a scalable message-passing localization method and architecture co-designed for a panel-based distributed MIMO system and network topology, in which interconnected units operate without centralized processing. This method jointly detects line-of-sight paths to distributed units from multipath measurements in dynamic scenarios, localizes the agent, and achieves very low latency. Additionally, we introduce a cycle-accurate system latency model based on implemented FPGA operations, and show important insights into processing latency and hardware utilization and system-level trade-offs. We compare our method to a multipath-based localization method and show that it can achieve similar localization performance, with wide enough distribution of array elements, while offering lower latency and computational complexity.

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arXiv2025

Fast Variational Block-Sparse Bayesian Learning

We propose a variational Bayesian (VB) implementation of block-sparse Bayesian learning (BSBL) to compute proxy probability density functions (PDFs) that approximate the posterior PDFs of the weights and associated hyperparameters in a block-sparse linear model, resulting in an iterative algorithm coined variational BSBL (VA-BSBL). The priors of the hyperparameters are selected to belong to the family of generalized inverse Gaussian distributions. This family contains as special cases commonly used hyperpriors. Inspired by previous work on classical sparse Bayesian learning (SBL), we investigate the update stage in which the proxy PDFs of a single block of weights and of its associated hyperparameter are successively updated, while keeping the proxy PDFs of the other parameters fixed. This stage defines a nonlinear first-order recurrence relation for the mean of the proxy PDF of the hyperparameter. By iterating this relation "ad infinitum" we obtain a criterion that determines whether the so-generated sequence of hyperparameter means converges or diverges. Incorporating this criterion into the VA-BSBL algorithm yields a fast implementation, coined fast-BSBL (F-BSBL), which achieves a two-order-of-magnitude runtime improvement. We further identify the range of the parameters of the generalized inverse Gaussian distribution which result in an inherent pruning procedure that switches off "weak" components in the model, which is necessary to obtain sparse results. Lastly, we show that expectation-maximization (EM)-based and VB-based implementations of BSBL are identical methods. Thus, we extend a well-known result from classical SBL to BSBL. Consequently, F-BSBL and BSBL using coordinate ascent to maximize the marginal likelihood coincide. These results provide a unified framework for interpreting existing BSBL methods.

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arXiv2025

A Sigma Point-based Low Complexity Algorithm for Multipath-based SLAM in MIMO Systems

Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising approach in wireless networks to jointly obtain position information of transmitters/receivers and information of the propagation environment. MP-SLAM models specular reflections at flat surfaces as virtual anchors (VAs), which are mirror images of base stations. Particle-based methods offer high flexibility and can approximate posterior probability density functions of the mobile agent state and the map feature states, (i.e., VA states) with complex shapes. However, they often require a large number of particles to counteract degeneracy in high-dimensional parameter spaces, leading to high computational complexity. Conversely using an insufficient number of particles leads to reduced estimation accuracy. In this paper, we introduce a low-complexity MP-SLAM algorithm using a sigma point (SP)-based implementation of the sum-product algorithm (SPA). We model the messages of continuous states of the agent and the VAs as Gaussian distributions and approximate nonlinearities via SP-transformations. This approach substantially reduces the computational complexity without decreasing accuracy. Since probabilistic data association yields Gaussian mixtures for the agent and VA states, we use moment matching to combine each mixture into a single Gaussian. Numerical results using synthetic and real data demonstrate that our method achieves significantly reduced computational runtimes compared to particle-based schemes, while exhibiting comparable (or even superior) localization and mapping performance.

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