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Dual extended Kalman filter for vehicle state and parameter estimation

机译：用于车辆状态和参数估计的双重扩展卡尔曼滤波器

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The article demonstrates the implementation of a model-based vehicle estimator, which can be used for combined estimation of vehicle states and parameters. The estimator is realised using the dual extended Kalman filter (DEKF) technique, which makes use of two Kalman filters running in parallel, thus 'splitting' the state and parameter estimation problems. Note that the two problems cannot be entirely separated due to their inherent interdependencies. This technique provides several advantages, such as the possibility to switch off the parameter estimator, once a sufficiently good set of estimates has been obtained. The estimator is based on a four-wheel vehicle model with four degrees of freedom, which accommodates the dominant modes only, and is designed to make use of several interchangeable tyre models. The paper demonstrates the appropriateness of the DEKF. Results to date indicate that this is an effective approach, which is considered to be of potential benefit to the automotive industry.
机译：本文演示了基于模型的车辆估计器的实现，该估计器可用于车辆状态和参数的组合估计。估计器是使用双扩展卡尔曼滤波器（DEKF）技术实现的，该技术利用了两个并行运行的卡尔曼滤波器，从而“分解”了状态和参数估计问题。注意，这两个问题由于其固有的相互依赖性而不能完全分开。该技术具有几个优点，例如一旦获得了足够好的估算值，就有可能关闭参数估算器。估算器基于具有四个自由度的四轮车辆模型，该模型仅适用于主要模式，并设计为使用多个可互换的轮胎模型。本文演示了DEKF的适当性。迄今为止的结果表明，这是一种有效的方法，被认为对汽车行业具有潜在的好处。

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