Target Tracking and Data Fusion in Sensor Networks

Latest papers

• Online Learning via Learned Latent Bayesian Tracking

  Guy Gerson, Tomer Raviv, Nir Shlezinger, Tirza Routtenberg, Osvaldo Simeone · 28 September 2026

  Online learning in non-stationary environments requires models to adapt rapidly from streaming data under strict computational constraints. A principled approach casts online learning as Bayesian state tracking, where model parameters are updated sequentially via Bayesian filtering. However, applyin…

• Learning to Bias: Machine Learning-Enhanced Particle Filters

  Apoorv Srivastava, Eric Darve · 28 September 2026

  Sequential inference estimates latent states from noisy and incomplete observations. Particle Filters (PFs), a class of Monte Carlo methods based on importance sampling, provide a flexible framework for this task, but often suffer from poor sample efficiency and unfavorable scaling with dimension, p…

• Full-Covariance Smoothing of Bayesian Neural Networks for Online Adaptation

  Oren Wright, Haoming Jing, Qiaoan Shen, Koichiro Niinuma, Yorie Nakahira, Jos\'e M. F. Moura · 24 September 2026

  A neural network's layers can be treated as time steps of a state-space model, turning Bayesian training into a smoothing problem: a forward pass propagates Gaussian moments through the network, and a backward Rauch--Tung--Striebel pass updates the weight posteriors in closed form. Such methods lear…

• PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation

  Yuta Tarumi · 24 September 2026

  Many physical data assimilation (DA) workflows require smoothing methods that represent non-Gaussian posteriors over physical state variables, scale to high-dimensional simulators, train from observation windows alone, and remain compatible with calibration of the prescribed simulator. We introduce …

• FREESIA: Covariance-Aware Posterior Transport for Expressive and Scalable Data Assimilation

  Shiwei Ni, Yangwen Zhang, Hang Qi, Xiaofei Guan, Lili Ju · 23 September 2026

  Data assimilation aims to infer the state of complex dynamical systems based on observational data. However, accurate inference of the multimodal posteriors induced by nonlinear or non-injective observation operators remains a key challenge under high-dimensional and sparse observation conditions. E…

• Training Neural Networks to Approach the Optimum Bayes Estimator in Dense Multi-Emitter Localization

  Yi Sun, Mona Sharifi, Muzna Yumman · 18 September 2026

  We train neural networks on synthesized frames to approach the optimum Bayes estimator for dense emitter localization. The result justifies the future work on training neural networks to achieve high-throughput large-FOV super spatiotemporal resolution SMLM.…

• Generative models for simulation based filtering: Formulations and Empirical Comparisons

  Mohammad Al-Jarrah, Wei Deng, Bamdad Hosseini, Amirhossein Taghvaei · 16 September 2026

  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 …

• Causal neural set filtering for online multi-target tracking

  Zhongdi Liu, Huangyu Dai · 16 September 2026

  Transformer-based multi-target tracking (MTT) jointly learns data association and state estimation, but MT3/Track-MT3-style trackers repeatedly re-encode measurement windows, incurring redundant computation. We propose Causal Neural Set Filtering (CNSF)\footnote{\href{https://github.com/daihuangyu/C…

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

  Qifu Wen, Shuaijun Liu, Zihan Zhou, Xi Zeng, Ningxin Su · 11 September 2026

  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 dete…

• Kalman Delta Networks: Uncertainty-aware Associative Memory

  Ngoc Bui, Tinglin Huang, Rex Ying · 9 September 2026

  Linear attention is increasingly used in frontier language models for efficient long-context inference and constant-memory decoding. Its fixed-size recurrent memory, however, requires an online decision at each token: what to write and how strongly to overwrite existing associations before knowing w…

• Residual Kalman Dynamics for Event-Based UAV Forecasting

  Per Nyblom, Hannes Ovr\'en, David Gustafsson · 2 September 2026

  We study short- and mid-horizon UAV bounding-box forecasting on the FRED event-camera dataset. We use a constant-velocity Kalman filter over a full center-size box state as a strong physical baseline, and train a residual model to predict acceleration-like corrections from recent box history, filter…

• Beyond Effective Sample Size: Effective Number of Proposals for Adaptive Importance Sampling

  Ali Mousavi, Victor Elvira · 18 August 2026

  Population-based adaptive importance sampling (AIS) methods use a set of proposal densities to approximate complex target distributions. Their performance is commonly assessed through effective sample size (ESS) and related weight-based diagnostics, which measure the concentration of normalized impo…

• Multi-Observer Vehicle Localization Case Study with Roadside Radar and Connected Vehicle Sensing

  Aleksi Pippuri, Nilusha Jayawickrama, Risto Ojala · 18 August 2026

  In modern intelligent transportation systems, it is essential to accurately estimate vehicle positions, especially in mixed traffic conditions where both connected and conventional vehicles coexist. Roadside infrastructure and connected vehicles can provide complementary observations of the same tra…

• Online Learning of Scale Parameters in Score-Driven Filters

  Fabrizio Lillo, Giulia Livieri, Gianluca Palmari · 11 August 2026

  Score-driven filters multiply a scaled log-likelihood score by a gain that controls the update magnitude. We treat this gain as a decision variable and study its online learning. Conditional on the current state, observation, score, and scaling rule, each admissible gain induces a reachable next sta…

• Unscented KalmanNet: a hybrid deep learning filter with calibrated posterior covariance for nonlinear state estimation

  Minhyeok Ko, Abdollah Shafieezadeh · 6 August 2026

  State estimation for nonlinear dynamical systems is commonly performed with the Unscented Kalman filter (UKF), which propagates the state moments through deterministic sigma points and reports a posterior covariance at every step. In practice, however, unknown and time-varying noise statistics and m…

• AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoring

  Yousef Emami, Miguel Gutierrez Gaitan, Atefeh Hajijamali Arani, Jingjing Zheng, Hao Zhou · 4 August 2026

  Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous inspection and sensor data collection in large-scale infrastructure monitoring applications, such as pipeline monitoring, where timely anomaly detection is critical. Jointly optimizing data-collection schedules and flight veloc…

• Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance

  Andrei Starodubov, Yaqub Aris Prabowo, Andreas Hadjipieris, Roberto Galeazzi, Ioannis Kyriakides · 28 July 2026

  This paper presents an information-gain-guided reinforcement-learning sensor-selection framework for single-vessel tracking in heterogeneous maritime sensor networks. The proposed approach is motivated by information-theoretic sensor management: instead of activating all sensors or repeatedly perfor…

• IMMNet: Hybrid Fusion of Model-based and Data-driven Approaches for Maneuvering Target Tracking

  Yixuan Zhao, Chaoqun Yang, Lin Gao, Yongxiao Tian, Ting Yuan · 16 July 2026

  Maneuvering target tracking in three-dimensional space remains a challenging problem due to complex motion dynamics and model mismatch. To address this, this paper proposes a hybrid model/data-driven algorithm named IMMNet, which integrates the interpretable structure of the interacting multiple mod…

• Dynamic Online Processor-Native Inference for State Estimation

  Orestis Kaparounakis · 15 July 2026

  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 p…

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

  George Stamatelis, Kyriakos Stylianopoulos, George C. Alexandropoulos · 30 June 2026

  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,…

• XMSE-Aware Adaptive Empirical Bayes Estimation

  Minghao Chen, Jiale Zheng · 26 June 2026

  Empirical Bayes (EB) estimators can match the first-order asymptotic risk of maximum likelihood (ML) while behaving very differently at second order: recent excess mean squared error (XMSE) analysis shows that kernel-based EB estimation may be worse than ML when the kernel is poorly aligned with the…

• Active Sensing and Deferred-Decision Trajectory Optimization for Robust Target Identification

  Farbod Siahkali, Mengxue Hou, Vijay Gupta · 23 June 2026

  We study trajectory optimization in mobile sensing systems that must identify which member of a finite candidate set is the true target, while maintaining reachability to all potential candidate targets, under resource constraints. Deferred-Decision Trajectory Optimization (DDTO) addresses this sett…

• Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators

  Naichang Ke, Pongpisit Thanasutives, Yoshinobu Kawahara · 15 June 2026

  Kalman filters based on the Embedded Latent Transfer Operators (ELTO) emerge as novel statistical tools for sequential state estimation. However, a critical limitation stems from their use of simplified noise models, which fail to dynamically adapt to non-stationary processes. To address this limita…

• Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification

  Jiho Lee, Nisar R. Ahmed, Rebecca Russell · 3 June 2026

  Kalman filtering performance is highly sensitive to model mismatch and noise covariance tuning. Learning-based approaches address these limitations but typically rely on supervised training with large datasets and do not produce consistent uncertainty estimates. In this paper, we propose a self-supe…

• The Kalman Evolve: Closing the Gap in Kalman Filtering via Interpretable Algorithm Discovery

  Vasileios Saketos, Ming Xiao · 27 May 2026

  State estimation is a fundamental problem in control and signal processing, for which the Kalman Filter provides an optimal solution under linear dynamics, Gaussian noise, and known noise covariances. However, these assumptions often fail in realistic sensing settings such as Doppler radar and LiDAR…