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Scale-free PSO for in-run and infield inertial sensor calibration

机译:无尺度的PSO用于运行和infield惯性传感器校准

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摘要

Inertial sensor calibration is one of the most important aspects of estimating motion using an inertial navigation system. The traditional calibration technique requires mounting of sensor in different orientations using costly equipment which are not available many a times for calibrating low cost MEMS sensors. The infield calibration scheme helps in obtaining the calibration parameters directly by minimizing a multi-dimensional cost function built around principles that these sensors follow. In this paper, a novel in-run calibration algorithm is proposed that does not require the sensor to be mounted in specific orientations. This scheme updates calibration parameter even without dismounting it from its location. In this work particle swarm optimization technique with scale free network is compared with other PSO variants and recommended to be used for inertial sensor calibration. The proposed in-run calibration scheme is run on simulated and real world dataset to investigate its efficacy on uncalibrated sensor readings. (C) 2019 Elsevier Ltd. All rights reserved.
机译:惯性传感器校准是使用惯性导航系统估算运动的最重要方面之一。传统的校准技术需要使用昂贵的设备在不同方向上安装传感器,这对于校准低成本MEMS传感器的次数不可用。 infield校准方案有助于通过最小化这些传感器遵循的原理构建的多维成本函数直接获得校准参数。在本文中,提出了一种新的运行校准算法,其不要求传感器以特定方向安装。此方案即使在没有从其位置卸下它也会更新校准参数。在此工作粒子中,与其他PSO变型进行比较,使用尺度无级网络进行综合优化技术,并建议用于惯性传感器校准。所提出的运行校准方案在模拟和现实世界数据集上运行,以研究其对未校准传感器读数的功效。 (c)2019年elestvier有限公司保留所有权利。

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