成员学位最低地方化等级</s> (The Membership Degree Min-Max Localisation Algorithm)

from arxiv, This paper was invited and accepted for publication in a special issue of the "Journal of Global Positioning Systems" in 2013. That issue has never appeared. After a decade, we decided to publish the final manuscript

We introduce the Membership Degree Min-Max (MD-Min-Max) localisation algorithm as a precise and simple lateration algorithm for indoor localisation. MD-Min-Max is based on the well-known Min-Max algorithm that computes a bounding box to estimate the position. MD-Min-Max uses a Membership Function (MF) based on an estimated error distribution of the distance measurements to improve the precision of Min-Max. The algorithm has similar complexity to Min-Max and can be used for indoor localisation even on small devices, e.g., in Wireless Sensor Networks (WSNs). To evaluate the performance of the algorithm, we compare it with other improvements of the Min-Max algorithm and maximum likelihood estimators, both in simulations and in a large real-world deployment of a WSN. Results show that MD-Min-Max achieves the best performance in terms of average positioning accuracy while keeping computational cost low compared to the other algorithms.

翻译：我们引入成交分级( MD- Min- Max) 本地化算法, 作为室内本地化的精确和简单通缩算法。 MD- Min- Max 是基于众所周知的Min- Max 算法, 该算法可以计算一个捆绑框来估计位置。 MD- Min- Max 使用一个会员函数( MF), 其依据是距离测量的距离分布估计误差, 以提高 Min- Max 的精确度。该算法与 Min- Max 相类似, 并可用于室内本地本地化, 甚至在小型设备上, 例如无线感应器网络。为了评估算法的性能, 我们将其与其他微量算算法的改进和最大概率估计器的其他改进方法进行比较, 包括在模拟和大规模实际部署 WSNN 中。结果显示, MD- Min- Max 在平均定位精度方面达到最佳的性能, 同时又保持计算成本低于其他算法。</s>