变化环境下的自适应测距模型

Jiyuan Sun, Kai Ruan, Mengjiao Zhang, Yangrui Zhu, Xiaohui Chen
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引用次数: 0

摘要

针对室内环境的复杂性,提出了一种自适应测距T-S模型(ADRTS)。该模型可以通过自学习进行测距,提高测距精度。该方法利用了在多变环境下精确定位的自适应能力。与经典测距模型相比,该模型在室内环境下具有较高的定位精度,具有可行性和简单性。在室内环境下的仿真实验表明,该方法对于提高测距精度是可行和有效的,有助于提高节点定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An adaptive ranging model under changeable environment
Due to the complexity of the indoor environment, this paper proposes an adaptive ranging T-S model (ADRTS). The new model can be through self-learning to range and improve the ranging accuracy. The proposed method exploits the adaptive to range precisely under the changeable environments. Compared with the classical ranging model, the proposed model is with high accuracy under the indoor environment about localization, and it is feasible and simple. In the indoor environment, the simulation experiment indicates that this method is feasible and effective for improving ranging accuracy and will help to improve the accuracy of localization of node.
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