  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.
