arXiv subjects

Klaus Witrisal

Publications and source records attributed to Klaus Witrisal.

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

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

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

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

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

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

Multi-Sensor Fusion of Active and Passive Measurements for Extended Object Tracking

This paper addresses the challenge of achieving robust and reliable positioning of a radio device carried by an agent, in scenarios where direct line-of-sight (LOS) radio links are obstructed by the agent. We propose a Bayesian estimation algorithm that integrates active measurements between the radio device and anchors with passive measurements in-between anchors reflecting off the agent. A geometry-based scattering measurement model is introduced for multi-sensor structures, and multiple object-related measurements are incorporated to formulate an extended object probabilistic data association (PDA) algorithm, where the agent that blocks, scatters and attenuates radio signals is modeled as an extended object (EO). The proposed approach significantly improves the accuracy during and after obstructed LOS conditions, outperforming the conventional PDA (which is based on the point-target-assumption) and methods relying solely on active measurements.

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arXiv2025

Graph-based Simultaneous Localization and Bias Tracking

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent's position together with a time-varying number of multipath components (MPCs). The MPCs are represented by "delay biases" corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations. Using simulated and real measurements in different scenarios we demonstrate that the proposed algorithm significantly outperforms state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm constantly attains the posterior Cramer-Rao lower bound (P-CRLB). Furthermore, we demonstrate the implicit capability of the proposed method to identify unreliable measurements and, thus, to mitigate lost tracks.

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arXiv2025

Geometry-Based Channel Estimation, Prediction, and Fusion

Reciprocity-based beamforming-most commonly employed in time-division duplexing-uses noisy, estimated (i.e., measured) channel state information (CSI) acquired on the uplink. While computationally efficient, reciprocity-based beamforming suffers severe losses under (i) low signal-to-noise ratio (SNR) and (ii) user mobility because it ignores the underlying physics of the radio channel beyond its reciprocity. Based on a physics-driven geometry-based channel model, we propose a method that jointly infers the mobile user's position and environment map on the uplink. It then leverages the estimated user position and environment map to predict CSI on the downlink. We demonstrate significant efficiency gains under both (i) low SNR and (ii) user mobility on measured data. While the user position may allow efficient beamforming in strong line-of-sight (LoS) channels, inferring an environment map allows bypassing obstructed LoS conditions using non-LoS beamforming via multipath components. We further propose "channel fusion," a probabilistic (Bayesian) combination of estimated and predicted CSI, which increases the beamforming robustness, particularly when either source of CSI is unreliable. Notably, this approach shares similarities with the minimum mean square error (MMSE) channel estimator, with geometry-based prior parameters inferred from the data.

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arXiv2025

Physically Large Apertures for Wireless Power Transfer: Performance and Regulatory Aspects

Wireless power transfer (WPT) is a promising service for the Internet of Things, providing a cost-effective and sustainable solution to deploy so-called energy-neutral devices on a massive scale. The power received at the device side from a conventional transmit antenna with a physically small aperture decays rapidly with the distance. New opportunities arise from the transition from conventional far-field beamforming to near-field beam focusing. We argue that a physically large aperture, i.e., large with respect to the distance to the receiver, enables a power budget that remains practically independent of distance. Distance-dependent array gain patterns allow focusing the power density maximum precisely at the device location, while reducing the power density near the infrastructure. Physical aperture size is a key resource in enabling efficient yet regulatory-compliant WPT. We use real-world measurements to demonstrate that a regulatory-compliant system operating at sub-10GHz frequencies can increase the power received at the device into the milliwatt range. Our empirical demonstration shows that power-optimal near-field beam focusing inherently exploits multipath propagation, yielding both increased WPT efficiency and improved human exposure safety.

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arXiv2024

Variational Signal Separation for Automotive Radar Interference Mitigation

Algorithms for mutual interference mitigation and object parameter estimation are a key enabler for automotive applications of frequency-modulated continuous wave (FMCW) radar. In this paper, we introduce a signal separation method to detect and estimate radar object parameters while jointly estimating and successively canceling the interference signal. The underlying signal model poses a challenge, since both the coherent radar echo and the non-coherent interference influenced by individual multipath propagation channels must be considered. Under certain assumptions, the model is described as a superposition of multipath channels weighted by parametric interference chirp envelopes. Inspired by sparse Bayesian learning (SBL), we employ an augmented probabilistic model that uses a hierarchical Gamma-Gaussian prior model for each multipath channel. Based on this, an iterative inference algorithm is derived using the variational expectation-maximization (EM) methodology. The algorithm is statistically evaluated in terms of object parameter estimation accuracy and robustness, indicating that it is fundamentally capable of achieving the Cramer-Rao lower bound (CRLB) with respect to the accuracy of object estimates and it closely follows the radar performance achieved when no interference is present.

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arXiv2024

Joint Localization, Synchronization and Mapping via Phase-Coherent Distributed Arrays

Extremely large-scale antenna array (ELAA) systems emerge as a promising technology in beyond 5G and 6G wireless networks to support the deployment of distributed architectures. This paper explores the use of ELAAs to enable joint localization, synchronization and mapping in sub-6 GHz uplink channels, capitalizing on the near-field effects of phase-coherent distributed arrays. We focus on a scenario where a single-antenna user equipment (UE) communicates with a network of access points (APs) distributed in an indoor environment, considering both specular reflections from walls and scattering from objects. The UE is assumed to be unsynchronized to the network, while the APs can be time- and phase-synchronized to each other. We formulate the problem of joint estimation of location, clock offset and phase offset of the UE, and the locations of scattering points (SPs) (i.e., mapping). Through comprehensive Fisher information analysis, we assess the impact of bandwidth, AP array size, wall reflections, SPs and phase synchronization on localization accuracy. Furthermore, we derive the maximum-likelihood (ML) estimator, which optimally combines the information collected by all the distributed arrays. To overcome its intractable high dimensionality, we propose a novel three-step algorithm that first estimates phase offset leveraging carrier phase information of line-of-sight (LoS) paths, then determines the UE location and clock offset via LoS paths and wall reflections, and finally locates SPs using a null-space transformation technique. Simulation results demonstrate the effectiveness of our approach in distributed architectures supported by radio stripes (RSs) -- an innovative alternative for implementing ELAAs -- while revealing the benefits of carrier phase exploitation and showcasing the interplay between delay and angular information under different bandwidth regimes.

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arXiv2024

Multipath-based SLAM with Cooperation and Map Fusion in MIMO Systems

Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising approach in wireless networks for obtaining position information of transmitters and receivers as well as information on the propagation environment. MP-SLAM models specular reflections of radio frequency (RF) signals at flat surfaces as virtual anchors (VAs), the mirror images of base stations (BSs). Conventional methods for MP-SLAM consider a single mobile terminal (MT) which has to be localized. The availability of additional MTs paves the way for utilizing additional information in the scenario. Specifically enabling MTs to exchange information allows for data fusion over different observations of VAs made by different MTs. Furthermore, cooperative localization becomes possible in addition to multipathbased localization. Utilizing this additional information enables more robust mapping and higher localization accuracy.

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arXiv2024

MIMO Multipath-based SLAM for Non-Ideal Reflective Surfaces

Multipath-based simultaneous localization and mapping (MP-SLAM) is a well established approach to obtain position information of transmitters and receivers as well as information regarding the propagation environments in future multiple input multiple output (MIMO) communication systems. Conventional methods for MP-SLAM consider specular reflections of the radio signals occurring at smooth, flat surfaces, which are modeled by virtual anchors (VAs) that are mirror images of the physical anchors (PAs), with each VA generating a single multipath component (MPC). However, non-ideal reflective surfaces (such as walls covered by shelves or cupboards) cause dispersion effects that violate the VA model and lead to multiple MPCs that are associated to a single VA. In this paper, we introduce a Bayesian particle-based sum-product algorithm (SPA) for MP-SLAM in MIMO communications systems. Our method considers non-ideal reflective surfaces by jointly estimating the parameters of individual dispersion models for each detected surface in delay and angle domain leveraging multiple-measurement-to-feature data association. We demonstrate that the proposed SLAM method can robustly and jointly estimate the positions and dispersion extents of ideal and non-ideal reflective surfaces using numerical simulation.

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arXiv2024

Fusion of Active and Passive Measurements for Robust and Scalable Positioning

This paper addresses the challenge of achieving reliable and robust positioning of a mobile agent, such as a radio device carried by a person, in scenarios where direct line-of-sight (LOS) links are obstructed or unavailable. The human body is considered as an extended object that scatters, attenuates and blocks the radio signals. We propose a novel particle-based sum-product algorithm (SPA) that fuses active measurements between the agent and anchors with passive measurements from pairs of anchors reflected off the body. We first formulate radio signal models for both active and passive measurements. Then, a joint tracking algorithm that utilizes both active and passive measurements is developed for the extended object. The algorithm exploits the probabilistic data association (PDA) for multiple object-related measurements. The results demonstrate superior accuracy during and after the obstructed line-of-sight (OLOS) situation, outperforming conventional methods that solely rely on active measurements. The proposed joint estimation approach significantly enhances the localization robustness via radio sensing.

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arXiv2023

A Neural-enhanced Factor Graph-based Algorithm for Robust Positioning in Obstructed LOS Situations

This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components). The physics-based and data-driven models are embedded in a joint Bayesian framework allowing to derive from first principles a factor graph-based algorithm that fuses the information of these models. The proposed algorithm uses radio signal measurements from multiple base stations to robustly estimate the mobile agent's position together with all model parameters. It provides high localization accuracy by exploiting the position-related information of the LOS component via the physics-based model and robustness by exploiting the geometric imprint of multipath components independent of the propagation channel via the data-driven model. In a challenging numerical experiment involving obstructed LOS situations to all anchors, we show that the proposed sequential algorithm significantly outperforms state-of-the-art methods and attains the posterior Cramer-Rao lower bound even with training data limited to local regions.

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