用神经网络动态调整噪声参数，提升水下导航滤波精度
Adaptive Neural Unscented Kalman Filter
- 设计端到端网络ProcessNet实时估计过程噪声协方差
- 在真实水下车辆数据上实现比传统滤波器更优的定位精度
- 适用于对噪声变化敏感的非线性传感器融合场景
无迹卡尔曼滤波能处理非线性系统，但过程噪声协方差不确定性会降低估计性能甚至导致发散。本文针对自主水下航行器导航中的非线性惯性传感器与多普勒测速仪融合问题，提出一种自适应神经无迹卡尔曼滤波器。设计了ProcessNet——一个简单高效的端到端回归网络，用于实时自适应估计过程噪声协方差矩阵。基于真实水下航行器采集的数据集进行实验，验证了该方法在时间变化噪声条件下的有效性，其性能优于其他自适应及非自适应非线性滤波器。
原文摘要 · Abstract (English)
The unscented Kalman filter is an algorithm capable of handling nonlinear scenarios. Uncertainty in process noise covariance may decrease the filter estimation performance or even lead to its divergence. Therefore, it is important to adjust the process noise covariance matrix in real time. In this paper, we developed an adaptive neural unscented Kalman filter to cope with time-varying uncertainties during platform operation. To this end, we devised ProcessNet, a simple yet efficient end-to-end regression network to adaptively estimate the process noise covariance matrix. We focused on the nonlinear inertial sensor and Doppler velocity log fusion problem in the case of autonomous underwater vehicle navigation. Using a real-world recorded dataset from an autonomous underwater vehicle, we demonstrated our filter performance and showed its advantages over other adaptive and non-adaptive nonlinear filters.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。