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：我们提出隶属度最小-最大（MD-Min-Max）定位算法，作为一种用于室内定位的精确且简单的测距交会算法。MD-Min-Max基于广为人知的Min-Max算法，该算法通过计算边界框来估计位置。MD-Min-Max利用基于距离测量误差分布估计的隶属函数（MF），以提升Min-Max算法的精度。该算法具有与Min-Max相似的复杂度，可适用于小型设备（例如无线传感器网络）中的室内定位。为评估算法性能，我们在仿真实验和实际大规模无线传感器网络部署中，将其与Min-Max算法的其他改进方案及最大似然估计器进行比较。结果表明，MD-Min-Max在保持较低计算成本的同时，平均定位精度优于其他对比算法。