神经辅助卡尔曼滤波用于退化传感环境下的无人机状态估计
Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments
AI总结：
本文提出基于贝叶斯神经网络的卡尔曼滤波框架，用于在传感器退化环境下提升无人机状态估计的鲁棒性和精度。
摘要：
准确估计非线性动态系统的状态对于现代航空航天操作至关重要。在线跟踪对抗性无人机（UAVs）尤其具有挑战性，因为其敏捷的非线性运动、噪声稀疏的传感器测量和未知的控制输入会违反经典卡尔曼滤波变体的关键假设并降低估计性能。神经网络（NNs）可以学习复杂非线性关系，但缺乏原理性的不确定性量化，这对于需要置信区间驱动下游决策的状态估计任务至关重要。我们通过贝叶斯神经网络（BNNs）解决这一问题，通过网络权重上的分布建模不确定性，并通过蒙特卡洛采样生成预测均值和不确定性。在此基础上，我们提出了贝叶斯神经卡尔曼滤波器（BNKF）：一种将训练好的BNN与卡尔曼修正步骤结合的混合框架，用于鲁棒的在线无人机状态估计。与相关神经卡尔曼方法不同，BNKF生成完整状态预测，并将贝叶斯不确定性直接纳入协方差传播，从而在高噪声条件下提高鲁棒性。我们使用合成非线性无人机飞行数据，在不同雷达噪声水平和采样率下评估BNKF。五折交叉验证显示，BNKF在退化传感条件下比扩展卡尔曼滤波器和无迹卡尔曼滤波器在准确性、精度和真实性包含方面表现更优。一种集成变体（BNKFe）在高噪声边缘情况下的精度进一步提高，但以轻微的精度牺牲为代价。运行时间分析证实了最小的推理开销，支持实时部署的可能性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
