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Smartphone based indoor localization and tracking model using bat algorithm and Kalman filter

机译：基于智能手机的室内本地化和跟踪模型使用BAT算法和卡尔曼滤波器

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摘要

In recent days, accurate localization becomes essential for enabling smartphone-based navigation to attain maximum accuracy in the construction of the real world.Fingerprint-based localization is the widespread solution to achieve and assure effective performance. In this study, a new fingerprint-based localization model using a bat algorithm (BA) is presented stimulated by the echolocation nature of microbats. The presented model adapts BA for estimating the location information. Initially, the presented model applies a Bayesian-rule based objective function. Then, the BA is used for improving the accuracy and analyzing the effects of the initial position of the bats on the localization outcome. For mitigating the estimation error, the Kalman filter is employed for updating the initially determined position using the BA for tracking purposes. The experimental analysis indicated an improvement in real-time performance and decrease in computation complexity. The presented model also obtained maximum localization accuracy with minimum localization error over the compared methods.
机译：最近几天，准确的本地化对于实现基于智能手机的导航来实现现实世界的建设中的最大准确性成为必不可少的。基于纲要的本地化是实现和确保有效性能的广泛解决方案。在该研究中，通过微垫的回声分配性质刺激了使用BAT算法（BA）的新的基于指纹的定位模型。呈现的模型适应BA来估计位置信息。最初，所呈现的模型适用于基于贝叶斯规则的目标函数。然后，BA用于提高蝙蝠初始位置对本地化结果的准确性和分析的精度和分析。为了减轻估计误差，使用Kalman滤波器用于使用BA更新最初确定的位置以进行跟踪目的。实验分析表明了实时性能的改善和计算复杂性降低。呈现的模型还通过比较方法获得最大的本地化精度，并通过比较的方法获得最低定位误差。

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