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Nonlinear observer with observability-based parameter adaptation for vehicle motion estimation

机译：具有基于可观察性的参数自适应的非线性观测器用于车辆运动估计

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In the framework of self-driving cars and driver-assistance systems the demand for reliable information about the vehicle ego-motion is increasing. This paper describes an estimation scheme, based on a nonlinear observer design, that provides velocity and attitude angle estimates. The approach relies on a state-affine representation of a kinematic model bolstered by a dynamic model-based measurement equation. By means of a thorough observability analysis, global exponential convergence is theoretically guaranteed. Additionally, in order to minimize the errors introduced by the dynamic model limitations, an observer tuning rule is proposed. The adaptation of the tuning parameters is built upon an online observability assessment of the system without the support of the dynamic model. Experimental results show that the presented approach reliably estimates the motion states.
机译：在自动驾驶汽车和驾驶员辅助系统的框架中，对有关车辆自我运动的可靠信息的需求正在增长。本文介绍了一种基于非线性观测器设计的估算方案，该方案可提供速度和姿态角估算。该方法依赖于由基于动态模型的测量方程支持的运动学模型的状态仿射表示。通过彻底的可观察性分析，理论上可以保证全局指数收敛。另外，为了最小化由动态模型限制引入的误差，提出了观察者调整规则。调整参数的调整基于系统的在线可观察性评估而无需动态模型的支持。实验结果表明，该方法能够可靠地估计运动状态。

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