Particle Filters

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7 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

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Latest papers 20

Sep 30, 2026cs.CV

PartiCam: Camera Controlled Video Generation with Reward Guidance

We present PartiCam, a training-free Particle filtering rooted method for improved Camera controlled video generation. Generating videos that follow a precisely specified camera trajectory remains challenging for large video diffusion models. Training-free approaches are backbone-agnostic and avoid the need to construct large camera-annotated datasets by steering pretrained models toward the desired camera motion at test time. This enables the generation of camera-controlled video data that can subsequently be used to train camera-conditioned video diffusion models. Existing sampling-based guidance approaches often suffer from unstable trajectories: they either explore too broadly and fail to respect the target camera motion or collapse early and lose visual diversity over time. We introduce a global-local refinement framework for diffusion reward guidance, enabling accurate and consistent camera control during video generation. Our method builds on Sequential Monte-Carlo (SMC) guidance, but introduces a local refinement stage based on particle filtered resampling. Experiments show large improvements in camera trajectory adherence, reduced drift, and better visual quality, without requiring model retraining.

Amine Ouasfi, Runjia Li, Junlin Han +3

INRIA, Univ. Rennes, CNRS, IRISA · University of Oxford · Meta

Camera Trajectories Video Diffusion Models Video Generation Particle Filters

Sep 22, 2026cs.RO

HINT-Blimp: Human INTent Inference from Multimodal Cues for Robotic Blimps

In human-robot interaction, traditional interfaces such as joysticks and handheld tablets introduce latency into navigation tasks and require the operator's explicit attention on the device, instead of the robot. We propose a new human-robot interaction framework in which a human communicates intent directly through sparse multimodal signals such as physical pushes and spoken commands. Human intent is represented as a parameterized linear dynamical system (LDS) that encodes the desired goal and motion behavior. The robot estimates this intent (parameters) online using a particle filter, where each particle represents a candidate LDS hypothesis and is reweighted online as new information becomes available. We validate this framework on a robotic blimp, whose inherent compliance and collision tolerance make it well-suited for repeated physical interaction. Experiments with multiple participants across 300 trials show that combining pushes and voice commands identifies the intended goal in 86% of trials within at most five interactions, with most trials resolved in two. The inferred dynamical systems can also produce curved trajectories that avoid obstacles known only to the human.

Subhadeep Koley, Benjamin Greenberg, Yifei Simon Shao +3

Lehigh University, Bethlehem, PA 18015, USA · Swarthmore College, Swarthmore, PA 19081, USA · University of Pennsylvania, Philadelphia, PA 19104, USA

Human-Robot Interaction Particle Filters Motion

Sep 22, 2026cs.RO

Destination Support Restoration for Finite-Set Multimodal Trajectory Prediction

Robots operating around pedestrians often reason over a finite set of predicted human futures. Repeated online updates can concentrate this limited prediction budget on dominant destinations and leave plausible alternatives underrepresented or absent, removing those alternatives from the finite representation available to downstream decision making. We introduce Destination Support Restoration (DSR), a causal post-selection operator that repairs destination support without retraining the host predictor or increasing the maintained set size. At a repair step, DSR evaluates a temporary destination-stratified candidate bank from the observed prefix, converts candidate evidence into integer target counts, protects representatives of active modes, and reallocates redundant surplus hypotheses to deficient modes. The maintained and returned sets retain exactly

N

hypotheses, and DSR replaces at most

\lceilρN\rceil

entries. Protected representatives preserve current categorical support; lineage-aware particle filters also preserve surviving resampling ancestors. Each replacement reduces the allocation mismatch to the evidence-driven target by one. On the complete 3,719-trajectory Edinburgh protocol over three seeds, DSR reduces MIF weighted ADE and FDE by 13.36% and 13.30% at

N=64

. Paired integrations with CLiFF, PPT, causal GDTS, Social Informer, and PECNet improve both metrics in every evaluated pair. These results show that finite-set support allocation is a useful prediction-side control point when a fixed hypothesis set serves as the interface to downstream systems.

Fengrui Liu, Jiajun Peng, Duo Peng +1

School of Computer Science and Technology, East China Normal University, Shanghai, China · School of Data Science, University of Science and Technology of China, Hefei, China · School of Computer Science and Technology, Tongji University, Shanghai, China +1

Ego-Trajectory Prediction Particle Filters

Sep 16, 2026eess.SP

Beyond Direct Sensing: Harnessing Indirect Observations from Third-Party Sensors in Vehicle Tracking

Vehicle tracking is fundamental to applications ranging from urban mobility and public safety to security and defense. Conventional tracking relies on direct access to sensors that provide strong observations such as vehicle identity and location. In practice, however, factors such as ownership, privacy, cost, and operational constraints may limit directly accessible sensors, leaving sparse observations and long tracking gaps. Meanwhile, many additional third-party sensing assets may be present across the environment but remain inaccessible at the raw-data level, preventing their direct integration into the tracking system. In this work, we investigate whether weak, indirect observations with uncertain spatial and temporal cues can complement sparse direct sensing for vehicle tracking. Specifically, we propose GrayTrack, which fuses weak anonymous events with sparse direct observations using a road-constrained particle filter. We build a CARLA-Mininet-WiFi pipeline to evaluate the system under controlled conditions, generating direct observations from accessible cameras and indirect observations from third-party cameras. Our learning-based detector achieves an F1 score of 0.989 for anonymous vehicle passages. Further, incorporating indirect third-party observations reduces trajectory RMSE by 60.1% and catastrophic track loss from 35.8% to 0.3%. These results demonstrate that GrayTrack can effectively exploit weak indirect observations to extend tracking capabilities.

Gaofeng Dong, Vamsi Eyunni, Pragya Sharma +2

Department of Electrical and Computer Engineering University of California, Los Angeles Los Angeles, CA, USA

Multi-Object Tracking Automotive Detection Unmanned Aerial Vehicle Detection Particle Filters Sensors Camera

Sep 14, 2026cs.LG

Generative models for simulation based filtering: Formulations and Empirical Comparisons

This letter presents a unified formulation and a controlled numerical comparison of generative-model approaches to the nonlinear filtering problem. Under this formulation the analysis step is realized by a transport of the forecast distribution to the posterior, the approaches differing only in how that transport is selected and learned. We derive three new filters, based on stochastic interpolants, their deterministic flow-matching limit, and Schrödinger bridges realized through forward--backward SDEs. We develop a two-stage tuning procedure that separates the training of the generative model from its online refinement. The resulting methods are compared against the optimal transport filter (OTF), the Knothe--Rosenblatt filter (KRF), the sequential importance resampling (SIR) particle filter and the ensemble Kalman filter (EnKF), in terms of accuracy, computational time, and sensitivity to ensemble size and state dimension. The results indicate that every generative filter resolves multimodal posteriors that the EnKF and SIR do not, that no single generative framework dominates, the preferred method being set by the available online budget and ensemble size, and that the filters differ in the regularity of the particle trajectories they produce.

Mohammad Al-Jarrah, Wei Deng, Bamdad Hosseini +1

Department of Aeronautics & Astronautics, University of Washington, Seattle · Department of Applied Mathematics, University of Washington, Seattle · ML Research, Morgan Stanley

Bayesian Filtering Particle Filters Extended Kalman Filter Generative Models Data Assimilation Posterior Sampling+2

Sep 11, 2026cs.LG

How Wrong Can a Good Predictor Be? Diverging Updates with Vanishing Predictive KL

Accurate posterior prediction need not require accurate approximation of Bayesian updates. We prove that an unbounded gap between the update maps can coexist with vanishing predictive KL for every fixed finite

K\ge2

in a stationary symmetric Gaussian HMM. Exact Bayesian mixing and an explicit deterministic radial filter act on the same

K-1

belief coordinates. As

q\to0^+

, their separation in centered logits in the worst case grows at least linearly in the natural confidence scale

L_K(q)

, while their categorical

D_{\mathrm{KL}}(\mathrm{exact}\|\mathrm{radial})

vanishes at the same explicit witness. Along stationary HMM trajectories, the expected terminal KL between filtered posteriors also converges to zero at

H(q)=\lceil-\log(q)/c\rceil+1

. Typical blocks without switches drive both filters into a common confidence cone, where softmax curvature suppresses their disagreement; a single Gaussian maximal event controls adaptive noise. A sweep with equally spaced Gaussians over

K\in\{2,4,8\}

illustrates the opposing trends, and binary controls at long horizons compare saturating and nonsaturating recurrences. The result isolates two missing links between internal update gaps and predictive cost: the contribution of separating states to expected loss and decoder sensitivity. Thus even an unbounded internal update gap does not by itself certify predictive failure. The construction is fixed in

K

and does not provide a universal criterion for when compression is harmless or characterize when internal gaps must incur task loss.

Qifu Wen, Shuaijun Liu, Zihan Zhou +2

Boston University, Boston, MA, USA · Shanghai Jiao Tong University, Shanghai, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Posterior Predictive Distribution Kullback-Leibler Divergence Hidden Markov Models Mixture Models Particle Filters

Sep 8, 2026cs.RO

A Distributed Consensus Particle Filter for Target Tracking using Autonomous Surface Vessels

Maritime target tracking over large distances often requires multi-agent teams without centralized coordination, and intermittent communication. Each agent must maintain an independent estimate that can take advantage of opportunistic communications availability when possible. This can lead to overly confident local estimates in the absence of external data. In this work, we propose an augmentation to a classical particle filter implementation that accounts for this potential source of error by forcing particles to spread strategically in the absence of informative updates from other sensor nodes. We demonstrate our method using Unmanned Surface Vessels (USVs) on a lake, and show that our augmentations do not deteriorate nominal performance, and provide an advantage in some specific edge cases.

Carter Noh, Kyle Crandall, Connor Yates +1

1Brigham Young University · 2US Naval Research Lab, Code 5534

Particle Filters Autonomous Underwater Vehicles Event-Guided Sensor-Level Fusion

Sep 2, 2026cs.LG

A Unified Particle Filter LSTM for Data-Driven Process Simulation

Data-driven process simulation aims to generate realistic case trajectories from historical event logs without requiring an explicitly specified model of the underlying dynamics. Deep sequence models can capture complex temporal dependencies through next-activity probabilities and conditional time distributions. However, event logs provide only a partial view of the underlying process state, often recording activity completions without the corresponding service-start times. Consequently, the same observed process history may be consistent with multiple plausible latent process conditions, whereas standard recurrent models compress each process prefix into a single deterministic recurrent state. We propose a Unified Particle Filter LSTM (Unified PF-LSTM) that maintains and sequentially updates a weighted set of recurrent-state hypotheses. We summarize this particle belief using its weighted mean and learned features based on the moment-generating function. The resulting representation is used to predict a categorical distribution over the next activity and conditional quantiles of the current activity's sojourn time. The framework is trained end-to-end from event-log data and evaluated on three real-world emergency department datasets. The results show that the proposed framework consistently outperforms the considered data-driven baselines in reproducing routing, duration, and system-level behavior across all datasets, with particularly strong gains in settings where complex process dynamics are only partially reflected in the available event logs.

Parvin Malekzadeh, Opher Baron, Dmitry Krass

Rotman School of Management, University of Toronto, Toronto, ON, Canada

Particle Filters

Jul 31, 2026cs.RO

Localization in Spatiotemporal Fields via Environmental PDEs

This paper proposes a localization framework that uses spatiotemporal fields governed by partial differential equations (PDEs) as localization signatures. Two PDE classes are considered: the shallow water equations, which describe free-surface flows in coastal and riverine environments, and the advection-diffusion equation, which models the transport and mixing of scalar quantities such as temperature, salinity, and dissolved oxygen. A numerical PDE solver provides predicted fields over the domain, and multiple field channels are fused as multimodal measurements to improve localization accuracy. We formulate the problem within a Rao-Blackwellized particle filter (RBPF) that partitions the vehicle state into a nonlinear component sampled by particles and a linear sensor bias component tracked analytically via per-particle Kalman filters. This factorization reduces the required number of particles compared to a standard particle filter while accounting for realistic sensor drift. Simulation studies on both PDE scenarios show that the RBPF consistently outperforms a standard particle filter in terms of final position error and Root Mean Square Error (RMSE) across varying particle counts. Field experiments with an autonomous surface vehicle measuring salinity, temperature, and dissolved oxygen validate that PDE-governed environmental fields provide sufficient spatial variability for practical localization. Related experimental videos are available at https://localization-environmental-pdes.github.io/.

Jose Fuentes, Abdullah Al Redwan Newaz, Ana Cavalcanti +1

Faculty of Computer Engineering and Information Sciences, Florida International University, Miami, FL 33174, USA · University of New Orleans, New Orleans, LA 70148, USA

Particle Filters Spatiotemporal Fields Indoor Localization Extended Kalman Filter One-Dimensional Viscous Burgers Equation Reynolds-Averaged Navier-Stokes+3

Jul 28, 2026cs.RO

Belief-Aware Influence and Trust (BAIT): Shaping Human Belief During Repeated Human-Robot Interaction

Repeated human-robot interaction (HRI) requires proactively accounting for humans who continually adapt to evolving beliefs about the robot. Prior frameworks often treat encounters as isolated events, suffering cumulative task performance decay as human perception drifts, or maintain long-term influence through erratic, unpredictable behavior that erodes perceived human trust and relies on computationally unscalable formulations. To address these gaps, we introduce the Belief- Aware Influence and Trust (BAIT) controller. BAIT integrates a hierarchical particle filter, which infers both fast human strategic shifts and slow perceptual belief updates, with a belief-aware Model Predictive Path Integral planner. BAIT explicitly optimizes the trade-off between long-horizon influence and human trust, while enforcing immediate task performance as a strict constraint. Across simulations, a human-subject study, and a real-world GEM vehicle deployments in repeated lane-merging scenarios, BAIT achieves task performance comparable to baselines that optimize long-term influence through unpredictability while yielding significantly higher user trust. The video demonstrating our experiments is available at https://youtu.be/9o4GqKLWDCw.

Ye-Ji Mun, Mahsa Golchoubian, Shahabedin Sagheb +4

University of Illinois at Urbana-Champaign, Department of Electrical and Computer Engineering. · University of Toronto, Department of Mathematical and Computational Science. · Virginia Tech, Department of Mechanical Engineering.

Human-Robot Interaction Model Predictive Path Integral AI Trustworthiness Particle Filters

Jul 13, 2026stat.ML

Dynamic Online Processor-Native Inference for State Estimation

Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and physical models. Yet the computation of the likelihood remains an essential bottleneck for accurate posteriors and performant inference. This paper presents a Bayesian filtering technique that uses processor-native uncertainty tracking for both uncertainty propagation and inference. The technique implements deterministic hierarchical importance restructuring through a native operation, giving deterministic latency and bounded memory use for arbitrary models written as program code. Benchmarks across three nonlinear state-space systems compare the approach against particle filters and Monte-Carlo-based likelihood estimators. The technique enables deterministic approximate filtering with as high as 805

\times

average speedup against direct Monte Carlo work at matched result quality for model evaluation, and Pareto-dominant accuracy-latency trade-offs for posterior inference while remaining competitive in RMSE with baseline particle filters.

Orestis Kaparounakis

Bayesian Filtering Particle Filters State Estimation Bayesian Inference Posterior Input Uncertainties+1

Jul 9, 2026cs.AI

Playing ZendoWorld: Challenging AI Agents on Active Visual Concept Induction

A central challenge in building intelligent systems is enabling agents to jointly perceive complex inputs, form hypotheses about hidden patterns, and design informative experiments to test them. To study this problem, we propose ZendoWorld, a controlled interactive environment in which agents must infer a logical rule about visual game observations, acquire information by proposing new scenes, and refine their hypotheses based on feedback from the game environment. We evaluate several agents spanning pure VLM reasoning, Bayesian particle filtering, dynamic concept discovery, and neuro-symbolic methods. Our main findings are: (1) high accuracy in predicting labels for observed examples does not imply recovery of the underlying rule; (2) perception and induction are distinct bottlenecks for different agent classes; and (3) VLM-based agents propose near-uninformative experiments, failing to actively reduce hypothesis uncertainty. To compare these results, we collect human data on the task, which reveals a gap in inductive reasoning, particularly for more complex rules. Overall, ZENDOWORLD takes an important step toward evaluating intelligent agents and identifies concrete avenues for improvement, particularly in domains like scientific discovery.

Sophia Koehler, Antonia Wüst, Inga Ibs +5

1AIML Lab, TU Darmstadt · 2Hessian Center for AI (hessian.AI) · Psychology of Information Processing, TU Darmstadt +4

Artificial Intelligence Agents Open-World Induction Perception Pipeline Particle Filters

Jul 1, 2026cs.LG

Generative Model Proposal based Particle Filtering for Data Assimilation

Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications. In the filtering setting, the goal is to model the posterior over the current state given all observations so far. Classical solutions typically make simplifying distributional or functional assumptions, e.g., linear-Gaussian systems, which can be inaccurate in many scenarios. In principle, particle filters (PFs) remove these assumptions, yet often collapse in high dimensions. Recent generative approaches learn conditional state transitions, but without principled Bayesian updates they do not recover the correct filtering posterior and can accumulate error over long horizons. In this work, we introduce Flow Proposal Particle Filters (FPPF), which learn a conditional generative model based proposal approximating the variance-minimizing optimal proposal for particle propagation. Conditioning on observations steers particles toward high-likelihood regions before weighting, reducing weight variance and delaying degeneracy. Since our proposal admits tractable likelihood evaluation, FPPF computes accurate importance weights and retains a Bayesian update step. We further extend FPPF to high-dimensional problems through localization strategies, adressing another standard PF failure mode. Extensive experiments on a variety of dynamical systems show that FPPF outperforms statistical baselines and other generative methods in non-linear, non-Gaussian, and high-dimensional regimes.

Chandni Nagda, Mayank Shrivastava, Gudrun Thorkelsdottir +3

University of Illinois at Urbana-Champaign · 2NVIDIA

Particle Filters Data Assimilation Posterior Sampling Generative Models Proposal

Jun 26, 2026cs.LG

Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter

Online latent state estimation constitutes a fundamental challenge within the artificial intelligence field, serving as a foundational tool for diverse applications, including sequential decision making, anomaly and change-point detection. In this paper, a novel online distributed sensing framework, where agents collaborate and exchange information to perform latent state estimation, is presented. The proposed estimator combines available partial domain knowledge with the representation capabilities of deep neural networks. In particular, the designed sensing framework incorporates prior estimates, optimized consensus weights, and Kalman-like recursive updates to perform decentralized inference, without relying on knowledge of noise statistics. Extensive experiments on linear, chaotic (Lorenz), and practical wireless tracking environments reveal that the proposed Covariance-Agnostic Neural Kalman Consensus Filter (CA-NKCF) outperforms traditional distributed Kalman and particle filters as well as purely model-free deep neural networks, exhibiting robustness even when the underlying motion and observation models are misspecified. It is also demonstrated that CA-NKCF's performance advantage remains stable across varying noise levels, random communication topologies, latent state dimensions, and observation clutter densities induced by scattering objects in wireless systems.

George Stamatelis, Kyriakos Stylianopoulos, George C. Alexandropoulos

Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimiopolis Ilissia, 16122 Athens, Greece

Extended Kalman Filter Particle Filters Collaborative Sensing Covariance Latent States

Jun 10, 2026cs.CV

SG2Loc: Sequential Visual Localization on 3D Scene Graphs

Visual localization in complex indoor environments remains a critical challenge for robotics and AR applications. Sequential localization, where pose estimates are refined over time, is important for autonomous agents. However, traditional methods often require storing extensive image databases or point clouds, leading to significant overhead. This paper introduces a novel, lightweight approach to sequential visual localization using 3D scene graphs. Our method represents the environment with a compact scene graph, where nodes represent objects (with coarse meshes) and edges encode spatial relationships. For each image in the localization phase, we extract per-patch semantic features, predicting object identities. Localization is performed within a particle filter framework. Each particle, representing a camera pose, projects the coarse object meshes from the scene graph into the image, assigning object identities to patches based on visibility. The similarity of the per-patch features, in the input image, and object features from the scene graph determines the weight of a particle. Subsequent images are incorporated sequentially, refining the pose estimate. By leveraging a compact scene graph and efficient semantic matching, our method significantly reduces storage while maintaining performance on real-world datasets. The code will be available at https://github.com/DmblnNicole/sg2loc.

Nicole Damblon, Olga Vysotska, Federico Tombari +2

1ETH Zurich · 2Google · 3TU Munich +1

Sequential Visual Localization 3D Scene Graphs Spatio-Temporal Scene Graphs Localization Deep Semantic Representations Particle Filters

May 19, 2026cs.LG

Training-Free Bayesian Filtering with Generative Emulators

Bayesian filtering is a well-known problem that aims to estimate plausible states of a dynamical system from observations. Among existing approaches to solve this problem, particle filters are theoretically exact for non-linear dynamics and observations, but suffer from poor scalability in high dimensions. In this work, we show that diffusion-based emulators of dynamical systems can be used to implement, without additional training, an optimal variant of particle filters that has remained largely unexplored due to implementation challenges with classical numerical solvers. Experiments on nonlinear chaotic systems, including atmospheric dynamics, demonstrate that the proposed approach successfully scales particle filtering to high-dimensional settings.

Thomas Savary, François Rozet, Gilles Louppe

SAIL, Montefiore institute, University of Liège, Belgium.

Bayesian Filtering Particle Filters Empirical Bayes Emulators Chaos Atmospheric Dynamics

May 18, 2026stat.ML

SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate

Data assimilation (DA) addresses the problem of sequentially estimating the state of a dynamical system from noisy and incomplete observations. In this work, we employ a diffusion model as a world model to simulate and predict the system's dynamics. Recently, score-based diffusion models have learned global diffusion priors that effectively model (stochastic) dynamics, revealing strong potential for data assimilation. In this paper, we investigate how information from noisy observations can be incorporated to enable continuous correction and refinement of the predicted system state when using a diffusion prior. Motivated by particle filtering methods, we represent the posterior distribution using a set of particles. After receiving noisy observations, the diffusion model is guided using the observation likelihood to steer the generation process toward observation-consistent states. Nevertheless, such guidance does not guarantee sampling from the true posterior. We therefore employ a Sequential Monte Carlo approach over the diffusion trajectory, viewed as a path measure, to reweight and resample particles, thereby correcting the generation process and ensuring convergence toward the desired posterior distribution. This leads to an unbiased particle filtering method that rigorously fuses observational data with diffusion model simulations.

Lifu Wei, Yinuo Ren, Naichen Shi +1

Department of Mechanical Engineering, Northwestern University, Evanston, IL, United States · Institute for Computational & Mathematical Engineering, Stanford University, Stanford, CA, United States · Department of Industrial Engineering & Management Sciences, Northwestern University, Evanston, IL, United States

Data Assimilation Particle Filters One-Dimensional Viscous Burgers Equation Diffusion Models Approximation

May 15, 2026cs.RO

Bayesian Networks for Path-Based Sensors: Gathering Information and Path Planning in Communication Denied Environments

A "path-based sensor" produces a single observation along a continuous path. For example, a boolean path-based sensor returns a single "1" if an event of interest is detected at any point along the path and a "0" otherwise. Notably, a "1" provides no direct information about where along the path the event(s) may have occurred. Previous work has demonstrated that observations from multiple path-based sensors can be fused to create a Bayesian belief map over the spatial locations of the underlying event or phenomenon. Moreover, path planning can employ Shannon information theory to accelerate the rate of convergence of the belief map. In this paper, we present a new method to update the belief map based on a path-based sensor observation, and then plan paths to increase information gain. In contrast to prior work that approximates the posterior by averaging over the alternative event histories, we introduce a Bayesian Network (BN) formulation that models the probabilistic relationships between the latent variables and path-based sensor measurements, enabling a more principled Bayesian belief update. We consider static hazard detection in a communication-denied environment as a representative problem setting. The event of a robot returning from its path corresponds to a path-based hazard sensor reading of "0" (hazard not detected), while a robot failing to return corresponds to a reading of "1" (hazard detected). We consider false positives and false negatives. We find that the new method leads to quicker convergence of the belief map than prior work in both single- and multi-robot cases.

Alkesh K. Srivastava, George P. Kontoudis, Donald Sofge +1

University of Maryland, College Park, MD, US. · Temple University, Philadelphia, PA, US. · Colorado School of Mines, Golden, CO, US. +1

Path Planning Bayesian Networks Event-Guided Sensor-Level Fusion Particle Filters Bayesian Robot Systems

May 1, 2026cs.LG

Learning Discriminators for Resampling in the Ensemble Gaussian Mixture Filter through a Normalizing Flow Approach

The ensemble Gaussian mixture filter (EnGMF) is a powerful, convergent particle filter capable of medium-to-high dimensional non-linear filtering. The EnGMF relies on a resampling step that can generate physically unrealistic posterior samples, that would subsequently produce physically meaningless forecasts. This work introduces the discriminator-informed resampling procedure, that augments the posterior resampling step with a discriminator that accepts or rejects candidate particles based on their physical plausibility. In this work these discriminators are learned through a normalizing flow approach. Numerical experiments on both the Ikeda map and the Lorenz '63 system show that discriminator informed resampling procedure consistently reduces error relative to the standard EnGMF in low-ensemble regimes.

Zain Jabbar, Andrey A. Popov

Particle Filters Normalizing Flows Resampling Data Assimilation Discriminative Models Discriminative

Apr 28, 2026cs.AI

Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields

{Closed-loop inverse source localization and characterization (ISLC) requires a mobile agent to select measurements that localize sources and infer latent field parameters under strict time constraints.} {The core challenge lies in the belief-space objective: valid uncertainty estimation requires expensive Bayesian inference, whereas using fast learned belief model leads to reward hacking, in which the policy exploits approximation errors rather than actually reducing uncertainty.} {We propose \textbf{Distill-Belief}, a teacher--student framework that decouples correctness from efficiency. A Bayes-correct particle-filter teacher maintains the posterior and supplies a dense information-gain signal, while a compact student distills the posterior into belief statistics for control and an uncertainty certificate for stopping. At deployment, only the student is used, yielding constant per-step cost.} {Experiments on seven field modalities and two stress tests show that Distill-Belief consistently reduces sensing cost and improves success, posterior contraction, and estimation accuracy over baselines, while mitigating reward hacking.}

Yiwei Shi, Zixing Song, Mengyue Yang +2

SEMT, University of Bristol, Bristol, UK · LUCAS, Loughborough University, Loughborough, UK

Particle Filters Indoor Localization Localization