基于轨迹的轻量级传感器融合方法，提升非线性系统状态估计精度。
Trajectory Based Observer Design: A Framework for Lightweight Sensor Fusion
- 通过预录轨迹优化观测器参数，实现通用非线性系统的快速设计。
- 在越野车定位任务中，姿态估计优于扩展卡尔曼滤波，位置精度相当。
- 模块化设计易部署，适合多传感器融合场景，尤其适用于资源受限系统。
高效观测器设计与精确传感器融合是状态估计的关键。本文提出一种基于优化的方法——轨迹基础优化设计（TBOD），使用户能够轻松为一般非线性系统和多传感器配置设计观测器。该方法从参数化观测器动力学出发，利用来自理想被控对象的预录测量轨迹，通过数值优化调节观测器参数。研究基于经典观测器理论与移动时域估计算法。优化过程简化了设计流程，提供了一种轻量、通用的传感器融合方案。TBOD的核心优势在于能高效、模块化处理各类传感器，并具备直观的调参方式。在基于惯性测量单元（IMU）与超宽带（UWB）测距传感器的地面机器人定位任务中进行了验证，采用动作捕捉系统进行实证评估。与扩展卡尔曼滤波（EKF）对比，其位置估计精度相当，姿态估计显著更优。
原文摘要 · Abstract (English)
Efficient observer design and accurate sensor fusion are key in state estimation. This work proposes an optimization-based methodology, termed Trajectory Based Optimization Design (TBOD), allowing the user to easily design observers for general nonlinear systems and multi-sensor setups. Starting from parametrized observer dynamics, the proposed method considers a finite set of pre-recorded measurement trajectories from the nominal plant and exploits them to tune the observer parameters through numerical optimization. This research hinges on the classic observer's theory and Moving Horizon Estimators methodology. Optimization is exploited to ease the observer's design, providing the user with a lightweight, general-purpose sensor fusion methodology. TBOD's main characteristics are the capability to handle general sensors efficiently and in a modular way and, most importantly, its straightforward tuning procedure. The TBOD's performance is tested on a terrestrial rover localization problem, combining IMU and ranging sensors provided by Ultra Wide Band antennas, and validated through a motion-capture system. Comparison with an Extended Kalman Filter is also provided, matching its position estimation accuracy and significantly improving in the orientation.
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