aggregate distributed particle filtering

Existing distributed particle filtering methods are constrained by fixed architectures and rigid communication assumptions, limiting their adaptability in open, heterogeneous Internet of Things (IoT) environments. This work introduces aggregation computing to this domain for the first time, proposing a unified framework based on the computational field abstraction that decouples state estimation from information propagation. This design enables flexible configuration of fusion centers, measurement aggregation schemes, and dissemination strategies. The approach significantly enhances system adaptability and scalability in dynamic IoT settings. Simulation experiments demonstrate effective trade-offs among estimation accuracy, communication overhead, and robustness across various configurations, confirming the framework’s broad applicability to diverse deployment scenarios.

This study addresses the inability of existing labeled random finite set filters to characterize cooperative swarm motion among targets by proposing a novel particle filter. The method couples joint particle filtering with an ensemble Gaussian mixture filter, directly optimizing individual particle densities rather than merely reweighting them during the measurement update step, thereby overcoming the limitations of conventional approaches. This work achieves precise modeling of cooperative swarm dynamics for the first time and theoretically proves that the proposed filter converges to the true Bayesian posterior under limiting conditions. Experimental evaluations based on the Vicsek model and coupled Brownian motion systems validate the efficiency and accuracy of the proposed approach.

针对海上目标跟踪中多自主船只无中心协调及间歇通信问题，提出一种改进的分布式共识粒子滤波方法，通过策略性地分散粒子来提高估计准确性。

Traditional particle filters for state space models (SSMs) are non-differentiable, hindering end-to-end joint optimization of latent states and unknown parameters. To address this, we propose and implement a unified, differentiable particle filtering (DPF) framework in PyTorch. Our method ensures full gradient flow through the filtering pipeline by reformulating resampling operations—including SoftResample and differentiable multinomial resampling—while providing a standardized API that integrates multiple state-of-the-art DPF algorithms for flexible configuration and fair benchmarking. Experimental reproduction across standard SSM benchmarks demonstrates significant improvements in parameter estimation accuracy and training stability. The framework serves as an efficient, open-source, plug-and-play differentiable inference tool for modeling complex dynamical systems.

Existing differentiable particle filters for nonlinear, non-Gaussian state-space models are constrained by prespecified distribution families (e.g., Gaussians) or the bootstrap framework, limiting their capacity to capture complex, multimodal posterior densities. To address this, we propose the first end-to-end differentiable particle filtering framework based on conditional normalizing flows. Our method uniformly integrates conditional flows into all key components—state dynamics propagation, proposal distribution design, and observation likelihood modeling—enabling flexible approximation of arbitrarily complex posterior densities and joint sequential state inference and model learning. We provide theoretical guarantees on filter consistency and gradient unbiasedness. Experiments demonstrate substantial improvements in estimation accuracy and robustness over state-of-the-art baselines, particularly in highly nonlinear and multimodal settings.

This work addresses the challenges of Bayesian filtering in high-dimensional nonlinear dynamical systems, where particle degeneracy and prohibitive computational costs severely limit scalability. The study proposes a novel approach that integrates a pretrained diffusion model as a training-free generative dynamics simulator within a particle filtering framework. By circumventing the constraints of conventional numerical solvers, this method enables a theoretically optimal filtering variant previously deemed infeasible. Notably, it requires no additional training and can be efficiently deployed in high-dimensional chaotic systems—such as those arising in atmospheric dynamics—yielding substantial improvements in both estimation accuracy and computational scalability.

This work addresses the challenges of particle degeneracy in traditional particle filters and the lack of rigorous Bayesian updating in existing generative approaches when assimilating high-dimensional, nonlinear, non-Gaussian data. To overcome these limitations, the authors propose the Flow-based Proposal Particle Filter (FPPF), which, for the first time, integrates a conditional generative model with computable likelihood into the particle filtering framework. By learning an approximation to the optimal proposal distribution that minimizes variance, FPPF steers particles toward high-likelihood regions and enables exact importance weighting for principled Bayesian updating. A localization strategy is further incorporated to ensure scalability in high-dimensional settings. Experimental results demonstrate that FPPF significantly outperforms both conventional and generative baselines across diverse complex dynamical systems, effectively mitigating particle degeneracy and yielding more accurate and stable posterior estimates.

本文提出了一种统一的生成模型方法来解决非线性滤波问题，并通过数值比较评估了基于不同传输学习方式的新滤波器，对比了其准确性、计算时间和对集合大小及状态维度的敏感性。

This study addresses the low sample efficiency and poor dimensional scalability of particle filters arising from suboptimal proposal distributions. To overcome these limitations, this work proposes the Neural Optimal Particle Filter, which approximates the optimal proposal distribution through neural network amortized learning and offline simulation-based training. By seamlessly integrating data-driven learning into classical inference frameworks, the method optimizes the standard update procedure without altering the underlying filtering objective. Evaluated on nonlinear benchmarks, the proposed approach significantly enhances both sample efficiency and posterior estimation accuracy while maintaining moderate computational overhead and asymptotic correctness.

This work addresses the inefficiency, high latency, and unbounded memory consumption associated with likelihood computation in Bayesian state estimation by introducing a novel Bayesian filtering approach grounded in native processor operations. For the first time, it integrates native uncertainty tracking into Bayesian inference and combines it with deterministic stratified importance resampling, enabling online inference for arbitrary procedural dynamic models. The proposed method achieves root mean square error (RMSE) accuracy comparable to particle filters while delivering up to an 805× average speedup over Monte Carlo methods. It further guarantees deterministic latency, bounded memory usage, and attains Pareto optimality in the trade-off between accuracy and latency.