Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments

• URL: http://arxiv.org/abs/2604.28107v1
• Date: Thu, 30 Apr 2026 16:55:15 GMT
• Title: Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments
• Abstract summary: We propose a hybrid framework coupling a trained BNN with a Kalman correction step for robust online UAV state estimation.<n>We evaluate BNKF under varying radar noise levels and sampling rates using synthetic nonlinear UAV flight data.
• Score: 0.3093890460224435
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Accurate state estimation of nonlinear dynamical systems is fundamental to modern aerospace operations across air, sea, and space domains. Online tracking of adversarial unmanned aerial vehicles (UAVs) is especially challenging due to agile nonlinear motion, noisy and sparse sensor measurements, and unknown control inputs; conditions that violate key assumptions of classical Kalman filter variants and degrade estimation performance. Neural networks (NNs) can learn complex nonlinear relationships from data, but lack principled uncertainty quantification, which is critical for state estimation tasks where confidence bounds drive downstream decisions. We address this with Bayesian Neural Networks (BNNs), which model uncertainty through distributions over network weights and produce predictive means and uncertainties via Monte Carlo sampling. Building on this, we propose the Bayesian Neural Kalman Filter (BNKF): a hybrid framework coupling a trained BNN with a Kalman correction step for robust online UAV state estimation. Unlike related neural Kalman approaches, BNKF produces full state predictions and incorporates Bayesian uncertainty directly into covariance propagation, improving robustness under high noise conditions. We evaluate BNKF under varying radar noise levels and sampling rates using synthetic nonlinear UAV flight data. Five fold cross validation demonstrates that BNKF outperforms Extended and Unscented Kalman Filters in accuracy, precision, and truth containment under degraded sensing. An ensemble variant (BNKFe) further improves precision in high-noise edge cases at a slight accuracy tradeoff. Runtime analysis confirms minimal inference overhead, supporting real-time deployment feasibility.

Related papers

• Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter [31.377504700683374]
  This paper presents a novel online distributed sensing framework, where agents collaborate and exchange information to perform latent state estimation.<n>The proposed estimator combines available partial domain knowledge with the representation capabilities of deep neural networks.<n>Experiments on linear, chaotic (z), and practical wireless tracking environments reveal that the proposed CoLoren-Agnostic Neural Kalman Consensus Filter (CA-NKCF) outperforms traditional distributed Kalman and particle filters.
  arXiv  Detail & Related papers  (2026-06-26T08:07:31Z)
• Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation [0.0]
  Unmanned Aerial Vehicles in dynamic environments face telemetry outages, structural vibrations, and regime-dependent noise.<n>We introduce the N-Deep Recurrent Sage-Husa Filter (NDR-SHKF), which replaces the scalar parameter with a vector-valued memory attenuation policy.<n>A bifurcated architecture routes shallow recurrent states to capture instantaneous sensor anomalies and deep states to encode sustained dynamic trends.
  arXiv  Detail & Related papers  (2026-05-18T17:38:13Z)
• Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization [55.338784037660474]
  Multi- curvature reinforcement learning in domains requires balancing complex, non-fragile interference trade-offs between conflicting objectives.<n>In this paper we introduce a novel based real-time based real-time optimization on a robotic visual -- a proxy for monitoring ecosystems.
  arXiv  Detail & Related papers  (2026-05-12T21:32:57Z)
• DOC-GS: Dual-Domain Observation and Calibration for Reliable Sparse-View Gaussian Splatting [80.43237927269575]
  In this paper, we revisit sparse-view 3DGS reconstruction from a new perspective.<n>We identify the core challenge as the unobservability of Gaussian primitive reliability.<n>Motivated by this observation, we propose a unified Dual-domain Observation and geometrics in rendered images framework.
  arXiv  Detail & Related papers  (2026-04-08T07:01:24Z)
• Adaptive Learned State Estimation based on KalmanNet [0.38290014221246177]
  Adaptive Multi-modal KalmanNet (AM-KNet) is an advancement of KalmanNet tailored to the multi-sensor autonomous driving setting.<n>AM-KNet introduces sensor-specific measurement modules that enable the network to learn the distinct noise characteristics of radar, lidar, and camera independently.<n>Results demonstrate improved estimation accuracy and tracking stability compared to the base KalmanNet.
  arXiv  Detail & Related papers  (2026-04-02T18:12:40Z)
• KD-EKF: Knowledge-Distilled Adaptive Covariance EKF for Robust UWB/PDR Indoor Localization [6.004649968619912]
  indoor localization provides centimeter-level accuracy and low latency.<n>Measurement reliability degrades severely under Non-Line-of-Sight conditions.<n>Inertial Measurement Unit (IMU)-based Pedestrian Dead Reckoning (PDR) provides infrastructure-free motion estimation.
  arXiv  Detail & Related papers  (2026-03-06T03:12:52Z)
• UAV-Deployed OAM-BB84 QKD: Turbulence- and Misalignment-Resilient Decoy-State Finite-Key Security with AI-Assisted Calibration [0.0]
  We present a theoretical framework for quantum key distribution (QKD) using orbital angular momentum encoded BB84 on an unmanned aerial vehicle (UAV) platform.<n>We derive composable finite key lower bounds on the secret key rate that incorporate statistical fluctuations, detector dark counts, efficiency mismatch, and error correction leakage.<n>We outline a complete evaluation pipeline including UAV system architecture, turbulence driven QBER maps, decoy optimization, finite key scaling, and AI calibration metrics.
  arXiv  Detail & Related papers  (2026-01-16T09:23:33Z)
• Developing Distance-Aware, and Evident Uncertainty Quantification in Dynamic Physics-Constrained Neural Networks for Robust Bearing Degradation Estimation [2.312232949770907]
  We introduce two distance-aware uncertainty methods for deterministic physics-guided neural networks.<n>We apply spectral normalization to the hidden layers so the network preserves distances from input to latent space.<n>We test our methods on rolling-element bearing degradation using the PRONOSTIA, XJTU-SY and HUST datasets.
  arXiv  Detail & Related papers  (2025-12-09T11:30:41Z)
• Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling [10.055838489452817]
  Deep ensembles (DEs) are efficient and scalable methods for uncertainty quantification (UQ) in Deep Neural Networks (DNNs)<n>We propose a novel method that combines Bayesian optimization (BO) with DE, referred to as BODE, to enhance both predictive accuracy and UQ.<n>We apply BODE to a case study involving a Densely connected Convolutional Neural Network (DCNN) trained on computational fluid dynamics (CFD) data to predict eddy viscosity in sodium fast reactor thermal stratification modeling.
  arXiv  Detail & Related papers  (2024-12-11T21:06:50Z)
• Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation [91.83820250747935]
  Pseudo-label noise is mainly contained in unstable samples in which predictions of most pixels undergo significant variations during self-training. We introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples. SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings.
  arXiv  Detail & Related papers  (2024-06-10T21:44:52Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics [84.18625250574853]
  We present KalmanNet, a real-time state estimator that learns from data to carry out Kalman filtering under non-linear dynamics. We numerically demonstrate that KalmanNet overcomes nonlinearities and model mismatch, outperforming classic filtering methods.
  arXiv  Detail & Related papers  (2021-07-21T12:26:46Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.