arXiv subjects

Florian Meyer

Publications and source records attributed to Florian Meyer.

At least 19 recordsLinked to original sources

arXiv2026

Learning Latent Fields in Multiobject Tracking

Multiobject tracking using measurements from ar- rays of wireless sensors is a key task in applications ranging from autonomous navigation to public safety. Many existing methods compress raw complex-valued signals into detections or intensity maps during preprocessing, thereby discarding poten- tially relevant information about the objects and the propagation environment. We propose a Bayesian framework for multiobject tracking that relates object states to raw complex-valued signals via a physics-based superpositional model. The model combines a physics-based array manifold, such as radar steering vectors or the replica vectors of matched-field processing, with a latent field. This latent field is a family of context-dependent signal- power distributions that captures the effects of the objects and the propagation environment and is shared across objects and time. It is parameterized by a conditional normalizing flow, which is trained during runtime without labeled data by maximizing an evidence lower bound (ELBO) derived from the Bayesian model. Learning and inference are thus coupled in a sequential framework. We evaluate the method in simulation and in real-world radio and acoustic experiments, with spatial scales ranging from meters to kilometers, and show that it outperforms detection-based methods.

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arXiv2026

Normalizing Flow for Neural-Enhanced Track-Before-Detect

Multiobject tracking (MOT) is a fundamental capability in autonomous navigation, radar and sonar systems, as well as 6G integrated sensing-and-communication (ISAC) networks. In this paper, we propose a neural-enhanced track-before-detect (TkBD) framework that operates directly on complex-valued raw signals and learns a latent representation of unmodeled propagation effects during runtime. We introduce an environment-dependent latent intensity variable that characterizes the signal power of each object in the shared propagation environment. The probability density function of this intensity variable is represented by a normalizing flow and trained online in an unsupervised manner within the sequential Bayesian inference procedure. The learning objective is derived from a variational Bayes formulation by maximizing the evidence lower bound (ELBO), thereby accounting for the uncertainties inherent in the Bayesian TkBD problem. To enable scalable inference, we make use of a recently introduced Bayesian TkBD framework that combines belief propagation, which avoids high-dimensional operations, with sequential Monte Carlo methods for efficient low-dimensional updates.

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arXiv2026

Sparse Signal Reconstruction for Track-Before-Detect

Accurately tracking an unknown and time-varying number of objects in complex environments is a challenging problem in radar and sonar sensing. The track-before-detect (TkBD) paradigm aims to avoid a detection stage by operating directly on sensor data to prevent information loss. This paper introduces a novel TkBD method based on a superpositional signal model adopted from the sparse signal reconstruction literature. The resulting TkBD measurement model accounts for sensor data correlations, amplitude fluctuations, and unknown measurement noise variance. By retaining relative phase information in complex-valued sensor data, the proposed measurement model supports phase-coherent processing directly within the TkBD method. It also enables the accurate representation of the physics of the data-generating process in the statistical model used for TkBD, including the complex acoustic wavefields used in matched-field processing. To accommodate measurements from multiple sensors or frequencies, we introduce a general multi-snapshot, multi-dictionary formulation of this model. The proposed statistical model is suitable for a wide range of problems in active and passive radar, active and passive sonar, as well as integrated sensing and communication systems. Based on a factor graph representation of the proposed model, a scalable belief propagation (BP) method is developed to perform efficient Bayesian inference. Experimental results obtained with both synthetic radar data and real acoustic data for matched-field processing demonstrate that the proposed method outperforms state-of-the-art conventional multiobject tracking (MOT) methods.

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arXiv2026

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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arXiv2026

Bayesian optimization with kernel ensembles and disagreement-based acquisition for source localization and acoustic inversion

Joint source localization and geoacoustic inversion requires optimizing an objective built from an expensive normal mode propagation model. Bayesian optimization (BO) with a Gaussian process (GP) surrogate can obtain accurate parameter estimates within a limited number of forward model evaluations, but its performance depends on the choice of kernel family. With few observations in a seven-dimensional search space, no single kernel can be expected to perform consistently well across individual inversions. To reduce this dependence, we use a weighted ensemble of GPs with different kernel families, allowing the surrogate to adapt to the observed objective without committing to one kernel in advance. The ensemble is combined with an optimum-conditioned acquisition function that determines where the expensive objective should be evaluated next. Experiments on simulated and measured SWellEx-96 data show that the resulting method achieves the lowest mean final objective among the considered BO strategies and reduces parameter estimation error on most coordinates. Ablation results further show that the ensemble provides robustness to kernel choice, while the acquisition function accounts for most of the optimization gain.

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arXiv2026

Bridging Echolocation Gaps in Automated Beaked Whale Tracking

Passive acoustic monitoring (PAM) is an effective and widely used tool for tracking marine mammals, particularly beaked whales, which are infrequently observed visually because of their deep-diving behavior. However, the large data sets generated by PAM methods often require time-consuming hand labeling to identify whale trajectories in the recorded audio. Automated multi-target tracking (MTT) methods could significantly reduce human workload, but current methods have difficulty forming continuous tracks because of the irregularity of beaked whale echolocation clicks. More precisely, regular sequences of clicks are often interrupted by longer pauses that occur when whales face away from the sensors or stop clicking. Consequently, the probability of detection is difficult to model accurately, and MTT trajectories become fragmented at these pauses. In this paper, we propose a multistage target-estimation method aimed at bridging large gaps in click sequences by combining belief propagation-based MTT with track smoothing and stitching. We validate our method using acoustic recordings of clicks from goose-beaked whales (Ziphius cavirostris), and demonstrate that it improves track estimates and reduces fragmentation in the presence of consecutive missed detections. When evaluated with the generalized optimal subpattern assignment (GOSPA) metric, our method outperforms existing MTT reference methods through reductions in missed-target errors.

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arXiv2026

Moment-Matching Probabilistic Data Association for Optimization-Based SLAM

Optimization-based simultaneous localization and mapping (SLAM) makes it possible to reduce accumulated navigation errors of sensing platforms by returning to known areas (loop closure). In this paper, we present an approach to combine probabilistic data association (PDA) with optimization-based SLAM. Instead of associating a single measurement with each landmark, we follow the PDA paradigm from the multiobject tracking community. In particular, in a processing stage performed in addition to the nonlinear least-squares solver of optimization-based SLAM, our method (i) assigns multiple measurements to landmarks probabilistically, (ii) computes the mean and covariance of landmark distributions via moment matching by taking multiple measurement-to-landmark associations into account, and (iii) establishes a virtual landmark measurement and a corresponding linear-Gaussian measurement model that leads to the mean and covariance matrix as moment-matching PDA in (ii). By converting the PDA update step into an equivalent linear-Gaussian measurement update step, PDA can be performed effectively within any optimization-based SLAM method. Our preliminary numerical evaluation in a scenario with false negatives and false positives indicates that incremental smoothing and mapping 2 (iSAM2), combined with the proposed PDA approach, can improve agent localization performance compared to conventional iSAM2.

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arXiv2026

Gaussian Belief Propagation for Tracking With Unresolved Measurements

Unresolved measurements occur in many inference problems where two or more hidden processes may, at times, jointly generate a single measurement. For instance, such phenomena are encountered in multiobject tracking owing to the limited resolution capabilities of practical sensors; or in camera-aided autonomous driving due to shadowing or occlusions. Substantial performance degradation, such as track losses, are incurred when unresolved measurements are not accounted for. In this paper, we address multiobject tracking under a generalized unresolved measurement model, where any subset of objects may generate a single unresolved measurement according to a probabilistic model. Our innovation lies both in modeling and algorithm-design directions. First, we develop a probability distribution for object partitions based on a model of pairwise coupling of objects and subsequently a probability distribution for object-to-measurement association variables. This generic model incorporates sensor resolution capabilities, sensor detection, and sensor noise characteristics for object groups. Second, a generic Loopy Belief Propagation (LBP) method as well as a specialized Gaussian-LBP (GLBP) algorithm are proposed that perform object state inference under the aforementioned model. In contrast to direct marginalization methods, which involve a computational complexity of $O(m^n)$, for $m$ measurements and $n$ objects, the proposed GLBP algorithm achieves a computational complexity on the order of $O(m n 2^{n})$. Numerical results demonstrate the effectiveness of our proposed GLBP, with estimation performance that closely matches that of exact marginalization for only a fraction of the computational resources.

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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

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

Bayesian Multiobject Tracking With Neural-Enhanced Motion and Measurement Models

Multiobject tracking (MOT) is an important task in applications including autonomous driving, ocean sciences, and aerospace surveillance. Traditional MOT methods are model-based and combine sequential Bayesian estimation with data association and an object birth model. More recent methods are fully data-driven and rely on the training of neural networks. Both approaches offer distinct advantages in specific settings. In particular, model-based methods are generally applicable across a wide range of scenarios, whereas data-driven MOT achieves superior performance in scenarios where abundant labeled data for training is available. A natural thought is whether a general framework can integrate the two approaches. This paper introduces a hybrid method that utilizes neural networks to enhance specific aspects of the statistical model in Bayesian MOT that have been identified as overly simplistic. By doing so, the performance of the prediction and update steps of Bayesian MOT is improved. To ensure tractable computation, our framework uses belief propagation to avoid high-dimensional operations combined with sequential Monte Carlo methods to perform low-dimensional operations efficiently. The resulting method combines the flexibility and robustness of model-based approaches with the capability to learn complex information from data of neural networks. We evaluate the performance of the proposed method based on the nuScenes autonomous driving dataset and demonstrate that it has state-of-the-art performance.

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arXiv2025

Statistical Model and Estimation Method for Ranging a Moving Ship Using a Single Acoustic Receiver in Shallow Water

Passive acoustics is a versatile tool for maritime situational awareness, enabling applications such as source detection and localization, marine mammal tracking, and geoacoustic inversion. This study focuses on estimating the range between an acoustic receiver and a transiting ship in an acoustically range-independent shallow water environment. Here, acoustic propagation can be modeled by a set of modes that are determined by the shallow water waveguide and seabed characteristics. These modes are dispersive, with phase and group velocities varying with frequency, and their interference produces striation patterns that depend on range and frequency in single-hydrophone spectrograms. These striation patterns can often be characterized by the waveguide invariant (WI), a single parameter describing the waveguide's properties. This paper presents a statistical model and corresponding WI-based range estimation approach using a single hydrophone, leveraging broadband and tonal sounds from a transiting ship. Using data from the Seabed Characterization Experiment 2017 (SBCEX17), the method was evaluated on two commercial ships under different environmental conditions and frequency bands. Range estimation errors remained below 4 % up to 62 km in the best case, with robust performance demonstrated in the 40-60 Hz band.

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arXiv2025

Association-Based Track-Before-Detect with Object Contribution Probabilities

Multiobject tracking provides situational awareness that enables new applications for modern convenience, applied ocean sciences, public safety, and homeland security. In many multiobject tracking applications, including radar and sonar tracking, after coherent prefiltering of the received signal, measurement data is typically structured in cells, where each cell represent, e.g., a different range and bearing value. While conventional detect-then-track (DTT) multiobject tracking approaches convert the cell-structured data within a detection phase into so-called point measurements in order to reduce the amount of data, track-before-detect (TBD) methods process the cell-structured data directly, avoiding a potential information loss. However, many TBD tracking methods are computationally intensive and achieve a reduced tracking accuracy when objects interact, i.e., when they come into close proximity. We here counteract these difficulties by introducing the concept of probabilistic object-to-cell contributions. As many conventional DTT methods, our approach uses a probabilistic association of objects with data cells, and a new object contribution model with corresponding object contribution probabilities to further associate cell contributions to objects that occupy the same data cell. Furthermore, to keep the computational complexity and filter runtimes low, we here use an efficient Poisson multi-Bernoulli filtering approach in combination with the application of belief propagation for fast probabilistic data association. We demonstrate numerically that our method achieves significantly increased tracking performance compared to state-of-the-art TBD tracking approaches, where performance differences are particularly pronounced when multiple objects interact.

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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

A Landmark-Aided Navigation Approach Using Side-Scan Sonar

Cost-effective localization methods for Autonomous Underwater Vehicle (AUV) navigation are key for ocean monitoring and data collection at high resolution in time and space. Algorithmic solutions suitable for real-time processing that handle nonlinear measurement models and different forms of measurement uncertainty will accelerate the development of field-ready technology. This paper details a Bayesian estimation method for landmark-aided navigation using a Side-scan Sonar (SSS) sensor. The method bounds navigation filter error in the GPS-denied undersea environment and captures the highly nonlinear nature of slant range measurements while remaining computationally tractable. Combining a novel measurement model with the chosen statistical framework facilitates the efficient use of SSS data and, in the future, could be used in real time. The proposed filter has two primary steps: a prediction step using an unscented transform and an update step utilizing particles. The update step performs probabilistic association of sonar detections with known landmarks. We evaluate algorithm performance and tractability using synthetic data and real data collected field experiments. Field experiments were performed using two different marine robotic platforms with two different SSS and at two different sites. Finally, we discuss the computational requirements of the proposed method and how it extends to real-time applications.

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arXiv2025

An Approach of Directly Tracking Multiple Objects

In conventional approaches for multiobject tracking (MOT), raw sensor data undergoes several preprocessing stages to reduce data rate and computational complexity. This typically includes coherent processing that aims at maximizing the signal-to-noise ratio (SNR), followed by a detector that extracts "point" measurements, e.g., the range and bearing of objects, which serve as inputs for sequential Bayesian MOT. While using point measurements significantly simplifies the statistical model, the reduced data rate can lead to a loss of critical, object-related information and, thus, potentially to reduced tracking performance. In this paper, we propose a direct tracking approach that avoids a detector and most preprocessing stages. For direct tracking, we introduce a measurement model for the data-generating process of the sensor data, along with state-transition and birth models for the dynamics and the appearance and disappearance of objects. Based on the new statistical model, we develop a factor graph and particle-based belief propagation (BP) method for efficient sequential Bayesian estimation. Contrary to the track-before-detect (TBD) paradigm which also avoids a detector, direct tracking integrates coherent processing within the Bayesian MOT framework. Numerical experiments based on a passive acoustic dataset demonstrate that the proposed direct approach outperforms state-of-the-art conventional methods that rely on multiple preprocessing stages.

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arXiv2024

A New Statistical Model for Waveguide Invariant-Based Range Estimation in Shallow Water

Navigation and source localization in the undersea environment are challenged by the absence of a ubiquitous positioning system. Passive acoustic ranging offers a valuable means of obtaining location information underwater. We present a range estimation method based on waveguide invariant (WI) theory, using ship noise recorded by a hydrophone as an acoustic source. The WI is a scalar parameter that describes the interference patterns in spectrograms caused by the interaction of acoustic wave modes propagating in a waveguide, such as shallow water. WI theory enables ranging using a single receiver without detailed knowledge of the environment. In this paper, underwater acoustic signals radiated by a moving large ship, which include broadband and tonal components, are employed for WI-based ranging in a range-independent shallow water environment. In particular, we develop a likelihood function for WI-based range estimation by introducing a statistical model for high signal-to-noise ratio scenarios. Here, the broadband component is assumed to dominate the background noise. The effectiveness of the proposed range estimation method is demonstrated using real acoustic measurements of a moving container ship recorded during the Seabed Characterization Experiment 2017 (SBCEX17).

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arXiv2024

Importance Sampling With Stochastic Particle Flow and Diffusion Optimization

Particle flow (PFL) is an effective method for overcoming particle degeneracy, the main limitation of particle filtering. In PFL, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. Recently proposed stochastic PFL introduces a diffusion term in the ordinary differential equation (ODE) that describes particle motion. This diffusion term reduces the stiffness of the ODE and makes it possible to perform PFL with a lower number of numerical integration steps compared to traditional deterministic PFL. In this work, we introduce a general approach to perform importance sampling (IS) based on stochastic PFL. Our method makes it possible to evaluate a "flow-induced" proposal probability density function (PDF) after the parameters of a Gaussian mixture model (GMM) have been migrated by stochastic PFL. Compared to conventional stochastic PFL, the resulting processing step is asymptotically optimal. Within our method, it is possible to optimize the diffusion matrix that describes the diffusion term of the ODE to improve the accuracy-computational complexity tradeoff. Our simulation results in a highly nonlinear 3-D source localization scenario showcase a reduced stiffness of the ODE and an improved estimating accuracy compared to state-of-the-art deterministic and stochastic PFL.

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