用非线性模型传播采样点，提升导航滤波精度。
Unscented Kalman Filter with a Nonlinear Propagation Model for Navigation Applications
- 用非线性动态模型更新采样点，改进状态预测
- 实测水下机器人数据验证，显著提升导航性能
- 适合高精度导航系统，尤其适用于复杂运动场景
无迹卡尔曼滤波是一种常用于导航的非线性估计算法。均值和协方差矩阵的预测对滤波器的稳定性至关重要，通常通过根据动态模型传播采样点来实现。本文提出一种新方法，基于导航误差状态向量的非线性动态模型传播采样点，从而提升滤波精度与导航性能。我们利用自主水下机器人在多个场景中采集的真实传感器数据，验证了该方法的有效性。
原文摘要 · Abstract (English)
The unscented Kalman filter is a nonlinear estimation algorithm commonly used in navigation applications. The prediction of the mean and covariance matrix is crucial to the stable behavior of the filter. This prediction is done by propagating the sigma points according to the dynamic model at hand. In this paper, we introduce an innovative method to propagate the sigma points according to the nonlinear dynamic model of the navigation error state vector. This improves the filter accuracy and navigation performance. We demonstrate the benefits of our proposed approach using real sensor data recorded by an autonomous underwater vehicle during several scenarios.
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。