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We introduce the Membership Degree Min-Max (MD-Min-Max) localization algorithm as a precise and simple lateration algorithm for indoor localization. MD-Min-Max is based on the well known Min-Max algorithm that uses a bounding box to compute the position. We present an analysis of the Min-Max algorithm and show strengths and weaknesses in the spatial distribution of the position error. MD-Min-Max uses a Membership Function (MF) based on an estimated error distribution of the distance measurements to gain a higher precision than Min-Max. The algorithm has the same complexity as Min-Max and can be used for indoor localization even on small devices, e.g. in Wireless Sensor Networks (WSNs). To evaluate the performance of the algorithm we compare it with other Min-Max algorithms in simulations and in a large real world deployment of a WSN.
Will et al. (Mon,) studied this question.