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An inertial sensor calibration platform to estimate and select error models

机译:惯性传感器校准平台,用于估计和选择误差模型

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A new open-source software platform that, among others, allows to select models for inertial sensor stochastic calibration is presented in this paper. This platform consists in a package included in the statistical software R. The identification of stochastic models and estimation of model parameters is based on the method of Generalized Method of Wavelet Moments. This approach provides an extremely general framework for the identification, estimation and testing of models to describe and predict the error signals coming from inertial sensors. With the possibility of estimating complex models made of the sum of different underlying processes, this paper also presents the method with which a model, or a restrict set of models, can be selected that best describes and predicts the error signal.
机译:本文提出了一个新的开源软件平台,该平台可以选择惯性传感器随机校准的模型。该平台包含在统计软件R中的一个包中。随机模型的识别和模型参数的估计是基于小波矩广义方法的。这种方法为模型的识别,估计和测试提供了一个非常通用的框架,以描述和预测来自惯性传感器的误差信号。由于可以估计由不同基础过程之和构成的复杂模型,因此本文还提出了一种方法,可以选择一种模型或一组限制模型来最好地描述和预测误差信号。

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